This tutorial illustrates how to add a trial-level early stopping strategy to an Ax hyper-parameter optimization (HPO) loop. The goal of trial-level early stopping is to monitor the results of expensive evaluations and terminate those that are unlikely to produce promising results, freeing up resources to explore more configurations.
Most of this tutorial is adapted from the PyTorch Ax Multiobjective NAS Tutorial. The training job is different from the original in that we do not optimize batch_size or epochs. This was done for illustrative purposes, as each validation curve now has the same number of points. The companion training file mnist_train_nas.py has also been altered to log to Tensorboard during training.
NOTE: Although the original NAS tutorial is for a multi-objective problem, this tutorial focuses on a single objective (validation accuracy) problem. Early stopping currently does not support "true" multi-objective stopping, although one can use logical compositions of early stopping strategies to target multiple objectives separately. Early stopping for the multi-objective case is currently a work in progress.
import os
import tempfile
from pathlib import Path
import torchx
from ax.core import Experiment, Objective, ParameterType, RangeParameter, SearchSpace
from ax.core.optimization_config import OptimizationConfig
from ax.early_stopping.strategies import PercentileEarlyStoppingStrategy
from ax.metrics.tensorboard import TensorboardMetric
from ax.modelbridge.dispatch_utils import choose_generation_strategy
from ax.runners.torchx import TorchXRunner
from ax.service.scheduler import Scheduler, SchedulerOptions
from ax.service.utils.report_utils import exp_to_df
from tensorboard.backend.event_processing import plugin_event_multiplexer as event_multiplexer
from torchx import specs
from torchx.components import utils
from matplotlib import pyplot as plt
%matplotlib inline
SMOKE_TEST = os.environ.get("SMOKE_TEST")
Our goal is to optimize the PyTorch Lightning training job defined in mnist_train_nas.py. To do this using TorchX, we write a helper function that takes in the values of the architcture and hyperparameters of the training job and creates a TorchX AppDef with the appropriate settings.
if SMOKE_TEST:
epochs = 3
else:
epochs = 10
def trainer(
log_path: str,
hidden_size_1: int,
hidden_size_2: int,
learning_rate: float,
dropout: float,
trial_idx: int = -1,
) -> specs.AppDef:
# define the log path so we can pass it to the TorchX AppDef
if trial_idx >= 0:
log_path = Path(log_path).joinpath(str(trial_idx)).absolute().as_posix()
batch_size = 32
return utils.python(
# command line args to the training script
"--log_path",
log_path,
"--hidden_size_1",
str(hidden_size_1),
"--hidden_size_2",
str(hidden_size_2),
"--learning_rate",
str(learning_rate),
"--epochs",
str(epochs),
"--dropout",
str(dropout),
"--batch_size",
str(batch_size),
# other config options
name="trainer",
script="tutorials/early_stopping/mnist_train_nas.py",
image=torchx.version.TORCHX_IMAGE,
)
Ax’s Runner
abstraction allows writing interfaces to various backends.
Ax already comes with Runner for TorchX, so we just need to
configure it. For the purpose of this tutorial, we run jobs locally
in a fully asynchronous fashion. In order to launch them on a cluster, you can instead specify a
different TorchX scheduler and adjust the configuration appropriately.
For example, if you have a Kubernetes cluster, you just need to change the
scheduler from local_cwd to kubernetes.
The training job launched by this runner will log partial results to Tensorboard, which will then be monitored by the early stopping strategy. We will show how this is done using an Ax TensorboardMetric below.
# Make a temporary dir to log our results into
log_dir = tempfile.mkdtemp()
ax_runner = TorchXRunner(
tracker_base="/tmp/",
component=trainer,
# NOTE: To launch this job on a cluster instead of locally you can
# specify a different scheduler and adjust args appropriately.
scheduler="local_cwd",
component_const_params={"log_path": log_dir},
cfg={},
)
First, we define our search space. Ax supports both range parameters of type integer and float as well as choice parameters which can have non-numerical types such as strings. We will tune the hidden sizes, learning rate, and dropout parameters.
parameters = [
# NOTE: In a real-world setting, hidden_size_1 and hidden_size_2
# should probably be powers of 2, but in our simple example this
# would mean that num_params can't take on that many values, which
# in turn makes the Pareto frontier look pretty weird.
RangeParameter(
name="hidden_size_1",
lower=16,
upper=128,
parameter_type=ParameterType.INT,
log_scale=True,
),
RangeParameter(
name="hidden_size_2",
lower=16,
upper=128,
parameter_type=ParameterType.INT,
log_scale=True,
),
RangeParameter(
name="learning_rate",
lower=1e-4,
upper=1e-2,
parameter_type=ParameterType.FLOAT,
log_scale=True,
),
RangeParameter(
name="dropout",
lower=0.0,
upper=0.5,
parameter_type=ParameterType.FLOAT,
),
]
search_space = SearchSpace(
parameters=parameters,
# NOTE: In practice, it may make sense to add a constraint
# hidden_size_2 <= hidden_size_1
parameter_constraints=[],
)
Ax has the concept of a Metric that defines properties of outcomes and how observations are obtained for these outcomes. This allows e.g. encodig how data is fetched from some distributed execution backend and post-processed before being passed as input to Ax.
We will optimize the validation accuracy, which is a TensorboardMetric that points to the logging directory assigned above. Note that we have set is_available_while_running, allowing for the metric to be queried as the trial progresses. This is critical for the early stopping strategy to monitor partial results.
class MyTensorboardMetric(TensorboardMetric):
# NOTE: We need to tell the new Tensorboard metric how to get the id /
# file handle for the tensorboard logs from a trial. In this case
# our convention is to just save a separate file per trial in
# the pre-specified log dir.
def _get_event_multiplexer_for_trial(self, trial):
mul = event_multiplexer.EventMultiplexer(max_reload_threads=20)
mul.AddRunsFromDirectory(Path(log_dir).joinpath(str(trial.index)).as_posix(), None)
mul.Reload()
return mul
# This indicates whether the metric is queryable while the trial is
# still running. This is required for early stopping to monitor the
# progress of the running trial.ArithmeticError
@classmethod
def is_available_while_running(cls):
return True
val_acc = MyTensorboardMetric(
name="val_acc",
tag="val_acc",
lower_is_better=False,
)
The OptimizationConfig specifies the objective for Ax to optimize.
opt_config = OptimizationConfig(
objective=Objective(
metric=val_acc,
minimize=False,
)
)
A PercentileEarlyStoppingStrategy is a simple method that stops a trial if its performance falls below a certain percentile of other trials at the same step (e.g., when percentile_threshold is 50, at a given point in time, if a trial ranks in the bottom 50% of trials, it is stopped).
normalize_progressions which normalizes the progression column (e.g. timestamp, epochs, training data used) to be in [0, 1]. This is useful because one doesn't need to know the maximum progression values of the curve (which might be, e.g., the total number of data points in the training dataset).min_progression parameter specifies that trials should only be considered for stopping if the latest progression value is greater than this threshold.min_curves parameter specifies the minimum number of completed curves (i.e., fully completed training jobs) before early stopping will be considered. This should be larger than zero if normalize_progression is used. In general, we want a few completed curves to have a baseline for comparison.Note that PercentileEarlyStoppingStrategy does not make use of learning curve modeling or prediction. More sophisticated model-based methods will be available in future versions of Ax.
percentile_early_stopping_strategy = PercentileEarlyStoppingStrategy(
# stop if in bottom 70% of runs at the same progression
percentile_threshold=70,
# the trial must have passed `min_progression` steps before early stopping is initiated
# note that we are using `normalize_progressions`, so this is on a scale of [0, 1]
min_progression=0.3,
# there must be `min_curves` completed trials and `min_curves` trials reporting data in
# order for early stopping to be applicable
min_curves=5,
# specify, e.g., [0, 1] if the first two trials should never be stopped
trial_indices_to_ignore=None,
# check for new data every 10 seconds
seconds_between_polls=10,
normalize_progressions=True,
)
In Ax, the Experiment object is the object that stores all the information about the problem setup.
experiment = Experiment(
name="torchx_mnist",
search_space=search_space,
optimization_config=opt_config,
runner=ax_runner,
)
A GenerationStrategy is the abstract representation of how we would like to perform the optimization. While this can be customized (if you’d like to do so, see this tutorial), in most cases Ax can automatically determine an appropriate strategy based on the search space, optimization config, and the total number of trials we want to run.
Typically, Ax chooses to evaluate a number of random configurations before starting a model-based Bayesian Optimization strategy.
We remark that in Ax, generation strategies and early stopping strategies are separate, a design decision motivated by ease-of-use. However, we should acknowledge that jointly considering generation and stopping using a single strategy would likely be the "proper" formulation.
if SMOKE_TEST:
total_trials = 6
else:
total_trials = 15 # total evaluation budget
gs = choose_generation_strategy(
search_space=experiment.search_space,
optimization_config=experiment.optimization_config,
num_trials=total_trials,
)
[INFO 12-25 06:44:06] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.
[INFO 12-25 06:44:06] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=4 num_trials=15 use_batch_trials=False
[INFO 12-25 06:44:06] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5
[INFO 12-25 06:44:06] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5
[INFO 12-25 06:44:06] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.
[INFO 12-25 06:44:06] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 5 trials, BoTorch for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.
The Scheduler acts as the loop control for the optimization.
It communicates with the backend to launch trials, check their status, retrieve (partial) results, and importantly for this tutorial, calls the early stopping strategy. If the early stopping strategy suggests a trial to be the stopped, the Scheduler communicates with the backend to terminate the trial.
The Scheduler requires the Experiment and the GenerationStrategy.
A set of options can be passed in via SchedulerOptions. Here, we
configure the number of total evaluations as well as max_pending_trials,
the maximum number of trials that should run concurrently. In our
local setting, this is the number of training jobs running as individual
processes, while in a remote execution setting, this would be the number
of machines you want to use in parallel.
scheduler = Scheduler(
experiment=experiment,
generation_strategy=gs,
options=SchedulerOptions(
total_trials=total_trials,
max_pending_trials=5,
early_stopping_strategy=percentile_early_stopping_strategy,
),
)
[INFO 12-25 06:44:06] Scheduler: `Scheduler` requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to `True` on experiment.
%%time
scheduler.run_all_trials()
[INFO 12-25 06:44:06] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.
warn("Encountered exception in computing model fit quality: " + str(e))
[INFO 12-25 06:44:06] Scheduler: Running trials [0]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.
warn("Encountered exception in computing model fit quality: " + str(e))
[INFO 12-25 06:44:07] Scheduler: Running trials [1]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.
warn("Encountered exception in computing model fit quality: " + str(e))
[INFO 12-25 06:44:08] Scheduler: Running trials [2]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.
warn("Encountered exception in computing model fit quality: " + str(e))
[INFO 12-25 06:44:09] Scheduler: Running trials [3]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.
warn("Encountered exception in computing model fit quality: " + str(e))
[INFO 12-25 06:44:10] Scheduler: Running trials [4]...
[WARNING 12-25 06:44:11] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 06:44:11] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6200>")
[INFO 12-25 06:44:11] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5450>")
[INFO 12-25 06:44:11] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5600>")
[INFO 12-25 06:44:11] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5960>")
[INFO 12-25 06:44:11] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5060>")
[ERROR 12-25 06:44:11] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6200>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:11] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5450>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:11] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5600>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:11] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5960>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:11] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5060>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:44:11] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6200>").
[INFO 12-25 06:44:11] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:11] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5450>").
[INFO 12-25 06:44:11] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:11] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5600>").
[INFO 12-25 06:44:11] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:11] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5960>").
[INFO 12-25 06:44:11] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:11] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5060>").
[INFO 12-25 06:44:11] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:44:11] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:44:11] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:44:21] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7430>")
[INFO 12-25 06:44:21] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6440>")
[INFO 12-25 06:44:21] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6410>")
[INFO 12-25 06:44:21] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5de0>")
[INFO 12-25 06:44:21] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d10>")
[ERROR 12-25 06:44:21] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7430>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:21] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6440>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:21] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6410>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:21] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5de0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:21] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d10>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:44:21] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7430>").
[INFO 12-25 06:44:21] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:21] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6440>").
[INFO 12-25 06:44:21] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:21] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6410>").
[INFO 12-25 06:44:21] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:21] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5de0>").
[INFO 12-25 06:44:21] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:21] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d10>").
[INFO 12-25 06:44:21] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:44:21] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:44:21] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:44:31] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d73a0>")
[INFO 12-25 06:44:31] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5e70>")
[INFO 12-25 06:44:31] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6050>")
[INFO 12-25 06:44:31] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5930>")
[INFO 12-25 06:44:31] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7340>")
[ERROR 12-25 06:44:31] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d73a0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:31] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5e70>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:31] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6050>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:31] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5930>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:31] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7340>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:44:31] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d73a0>").
[INFO 12-25 06:44:31] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:31] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5e70>").
[INFO 12-25 06:44:31] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:31] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6050>").
[INFO 12-25 06:44:31] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:31] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5930>").
[INFO 12-25 06:44:31] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:31] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7340>").
[INFO 12-25 06:44:31] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:44:31] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:44:31] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:44:41] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d62c0>")
[INFO 12-25 06:44:41] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5cc0>")
[INFO 12-25 06:44:41] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7280>")
[INFO 12-25 06:44:41] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5240>")
[INFO 12-25 06:44:41] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6140>")
[ERROR 12-25 06:44:41] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d62c0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:41] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5cc0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:41] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7280>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:41] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5240>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:41] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6140>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:44:41] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d62c0>").
[INFO 12-25 06:44:41] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:41] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5cc0>").
[INFO 12-25 06:44:41] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:41] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7280>").
[INFO 12-25 06:44:41] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:41] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5240>").
[INFO 12-25 06:44:41] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:41] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6140>").
[INFO 12-25 06:44:41] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:44:41] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:44:41] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:44:51] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5bd0>")
[INFO 12-25 06:44:51] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5a50>")
[INFO 12-25 06:44:51] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d78e0>")
[INFO 12-25 06:44:51] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6c50>")
[INFO 12-25 06:44:51] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6ce0>")
[ERROR 12-25 06:44:51] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5bd0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:51] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5a50>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:51] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d78e0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:51] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6c50>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:44:51] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6ce0>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:44:51] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5bd0>").
[INFO 12-25 06:44:51] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:51] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5a50>").
[INFO 12-25 06:44:51] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:51] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d78e0>").
[INFO 12-25 06:44:51] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:51] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6c50>").
[INFO 12-25 06:44:51] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:44:51] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6ce0>").
[INFO 12-25 06:44:51] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:44:51] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:44:51] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:45:01] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6710>")
[INFO 12-25 06:45:01] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5030>")
[INFO 12-25 06:45:01] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7af0>")
[INFO 12-25 06:45:01] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6ad0>")
[INFO 12-25 06:45:01] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7580>")
[ERROR 12-25 06:45:01] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6710>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:01] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5030>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:01] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7af0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:01] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6ad0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:01] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7580>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:45:01] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6710>").
[INFO 12-25 06:45:01] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:01] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5030>").
[INFO 12-25 06:45:01] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:01] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7af0>").
[INFO 12-25 06:45:01] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:01] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6ad0>").
[INFO 12-25 06:45:01] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:01] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7580>").
[INFO 12-25 06:45:01] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:45:01] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:45:01] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:45:11] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5e70>")
[INFO 12-25 06:45:12] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d48e0>")
[INFO 12-25 06:45:12] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d41f0>")
[INFO 12-25 06:45:12] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5060>")
[INFO 12-25 06:45:12] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d4e80>")
[ERROR 12-25 06:45:12] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5e70>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:12] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d48e0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:12] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d41f0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:12] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5060>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:12] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d4e80>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:45:12] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5e70>").
[INFO 12-25 06:45:12] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:12] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d48e0>").
[INFO 12-25 06:45:12] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:12] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d41f0>").
[INFO 12-25 06:45:12] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:12] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5060>").
[INFO 12-25 06:45:12] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:12] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d4e80>").
[INFO 12-25 06:45:12] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:45:12] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:45:12] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:45:22] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7e80>")
[INFO 12-25 06:45:22] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7010>")
[INFO 12-25 06:45:22] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d60b0>")
[INFO 12-25 06:45:22] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7790>")
[INFO 12-25 06:45:22] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6800>")
[ERROR 12-25 06:45:22] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7e80>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:22] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7010>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:22] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d60b0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:22] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7790>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:22] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6800>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:45:22] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7e80>").
[INFO 12-25 06:45:22] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:22] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7010>").
[INFO 12-25 06:45:22] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:22] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d60b0>").
[INFO 12-25 06:45:22] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:22] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7790>").
[INFO 12-25 06:45:22] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:22] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6800>").
[INFO 12-25 06:45:22] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:45:22] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:45:22] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:45:32] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d77f0>")
[INFO 12-25 06:45:32] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d10>")
[INFO 12-25 06:45:32] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5930>")
[INFO 12-25 06:45:32] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6b60>")
[INFO 12-25 06:45:32] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d62f0>")
[ERROR 12-25 06:45:32] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d77f0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:32] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d10>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:32] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5930>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:32] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6b60>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:32] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d62f0>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:45:32] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d77f0>").
[INFO 12-25 06:45:32] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:32] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d10>").
[INFO 12-25 06:45:32] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:32] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5930>").
[INFO 12-25 06:45:32] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:32] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6b60>").
[INFO 12-25 06:45:32] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:32] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d62f0>").
[INFO 12-25 06:45:32] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:45:32] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:45:32] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:45:42] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7ee0>")
[INFO 12-25 06:45:42] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d70>")
[INFO 12-25 06:45:42] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7520>")
[INFO 12-25 06:45:42] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d60b0>")
[INFO 12-25 06:45:42] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5390>")
[ERROR 12-25 06:45:42] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7ee0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:42] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d70>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:42] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7520>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:42] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d60b0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:42] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5390>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:45:42] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7ee0>").
[INFO 12-25 06:45:42] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:42] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6d70>").
[INFO 12-25 06:45:42] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:42] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7520>").
[INFO 12-25 06:45:42] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:42] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d60b0>").
[INFO 12-25 06:45:42] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:42] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5390>").
[INFO 12-25 06:45:42] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:45:42] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:45:42] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:45:52] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6440>")
[INFO 12-25 06:45:52] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d79a0>")
[INFO 12-25 06:45:52] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5ea0>")
[INFO 12-25 06:45:52] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6590>")
[INFO 12-25 06:45:52] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7a30>")
[ERROR 12-25 06:45:52] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6440>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:52] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d79a0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:52] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5ea0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:52] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6590>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:45:52] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7a30>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 06:45:52] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6440>").
[INFO 12-25 06:45:52] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:52] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d79a0>").
[INFO 12-25 06:45:52] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:52] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5ea0>").
[INFO 12-25 06:45:52] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:52] Scheduler: Failed to fetch val_acc for trial 3, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6590>").
[INFO 12-25 06:45:52] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 3 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:45:52] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7a30>").
[INFO 12-25 06:45:52] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
[INFO 12-25 06:45:52] ax.early_stopping.strategies.base: PercentileEarlyStoppingStrategy received empty data. Not stopping any trials.
[INFO 12-25 06:45:52] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:46:02] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5540>")
[INFO 12-25 06:46:02] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d4d00>")
[INFO 12-25 06:46:02] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7520>")
[INFO 12-25 06:46:02] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d51b0>")
[ERROR 12-25 06:46:02] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5540>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:46:02] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d4d00>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:46:02] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7520>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 06:46:02] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d51b0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 06:46:02] Scheduler: Failed to fetch val_acc for trial 0, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d5540>").
[INFO 12-25 06:46:02] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 0 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:46:02] Scheduler: Failed to fetch val_acc for trial 1, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d4d00>").
[INFO 12-25 06:46:02] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 1 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:46:02] Scheduler: Failed to fetch val_acc for trial 2, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d7520>").
[INFO 12-25 06:46:02] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 2 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 06:46:02] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d51b0>").
[INFO 12-25 06:46:02] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:46:02] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:46:02] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 06:46:12] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6590>")
[ERROR 12-25 06:46:12] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6590>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 06:46:12] Scheduler: Failed to fetch val_acc for trial 4, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89252d6590>").
[INFO 12-25 06:46:12] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 4 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:46:12] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:46:12] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:46:23] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:46:23] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:46:33] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:46:33] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:46:43] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:46:43] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:46:53] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:46:53] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:47:04] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:47:04] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:47:14] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:47:14] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:47:24] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:47:24] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:47:34] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:47:34] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:47:45] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:47:45] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:47:55] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:47:55] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:48:05] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:48:05] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:48:15] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:48:15] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:48:26] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:48:26] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:48:36] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:48:36] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:48:46] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:48:46] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:48:56] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:48:56] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:49:07] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:49:07] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:49:17] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:49:17] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:49:27] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:49:27] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:49:37] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:49:37] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:49:47] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:49:47] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:49:58] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:49:58] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:50:08] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:50:08] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:50:18] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:50:18] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:50:28] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:50:28] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:50:39] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:50:39] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:50:49] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:50:49] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:50:59] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:50:59] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:51:09] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:51:09] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:51:20] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:51:20] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:51:30] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:51:30] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:51:40] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:51:40] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:51:50] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:51:50] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:52:01] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:52:01] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:52:11] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:52:11] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:52:21] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:52:21] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:52:31] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:52:31] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:52:42] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:52:42] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:52:52] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:52:52] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:53:02] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:53:02] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:53:12] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:53:12] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:53:22] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:53:23] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:53:33] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:53:33] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:53:43] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:53:43] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:53:53] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:53:53] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:54:03] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:54:03] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:54:14] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:54:14] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:54:24] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:54:24] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:54:34] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:54:34] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:54:44] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:54:44] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:54:55] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:54:55] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:55:05] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:55:05] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:55:15] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:55:15] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:55:25] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:55:25] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:55:36] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:55:36] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:55:46] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:55:46] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:55:56] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:55:56] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:56:06] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:56:06] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:56:17] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:56:17] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:56:27] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:56:27] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:56:37] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:56:37] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:56:48] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:56:48] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:56:58] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:56:58] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:57:08] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:57:08] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:57:18] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:57:18] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:57:29] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:57:29] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:57:39] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:57:39] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:57:49] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:57:49] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:57:59] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:57:59] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:58:10] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:58:10] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:58:20] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:58:20] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:58:30] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:58:30] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:58:40] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:58:40] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:58:51] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:58:51] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:59:01] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:59:01] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:59:11] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:59:11] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:59:21] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:59:21] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:59:32] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:59:32] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:59:42] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[WARNING 12-25 06:59:42] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:59:42] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:59:42] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 06:59:52] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 06:59:52] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:00:03] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:00:03] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:00:13] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:00:13] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:00:23] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:00:23] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:00:33] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:00:33] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:00:44] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:00:44] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:00:54] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:00:54] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:01:04] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:01:04] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:01:14] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:01:14] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:01:25] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:01:25] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:01:35] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:01:35] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:01:45] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:01:45] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:01:55] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:01:55] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:02:06] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:02:06] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:02:16] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:02:16] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:02:26] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:02:26] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:02:36] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:02:36] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:02:47] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:02:47] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:02:57] ax.early_stopping.strategies.base: The number of completed trials (0) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:02:57] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 5).
[INFO 12-25 07:03:07] Scheduler: Retrieved COMPLETED trials: [1].
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:07] ax.early_stopping.strategies.base: The number of completed trials (1) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[WARNING 12-25 07:03:07] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:07] ax.early_stopping.strategies.base: The number of completed trials (1) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:03:07] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 4).
[INFO 12-25 07:03:17] Scheduler: Retrieved COMPLETED trials: [2].
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:18] ax.early_stopping.strategies.base: The number of completed trials (2) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[WARNING 12-25 07:03:18] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:18] ax.early_stopping.strategies.base: The number of completed trials (2) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:03:18] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:03:28] Scheduler: Retrieved COMPLETED trials: [3].
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:28] ax.early_stopping.strategies.base: The number of completed trials (3) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:03:30] Scheduler: Running trials [5]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:03:33] Scheduler: Running trials [6]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:03:37] Scheduler: Running trials [7]...
[WARNING 12-25 07:03:37] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:03:37] Scheduler: Retrieved COMPLETED trials: [0].
[INFO 12-25 07:03:37] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fa30>")
[INFO 12-25 07:03:37] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fa00>")
[INFO 12-25 07:03:37] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f9a0>")
[ERROR 12-25 07:03:37] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fa30>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:37] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fa00>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:37] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f9a0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:03:37] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fa30>").
[INFO 12-25 07:03:37] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:37] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fa00>").
[INFO 12-25 07:03:37] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:37] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f9a0>").
[INFO 12-25 07:03:37] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:37] ax.early_stopping.strategies.base: The number of completed trials (4) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[WARNING 12-25 07:03:38] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:03:38] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>")
[INFO 12-25 07:03:38] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>")
[INFO 12-25 07:03:38] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>")
[ERROR 12-25 07:03:38] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:38] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:38] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:03:38] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>").
[INFO 12-25 07:03:38] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:38] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>").
[INFO 12-25 07:03:38] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:38] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197ca0>").
[INFO 12-25 07:03:38] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:38] ax.early_stopping.strategies.base: The number of completed trials (4) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:03:38] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 4).
[INFO 12-25 07:03:48] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f892417a920>")
[INFO 12-25 07:03:48] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f892417af20>")
[INFO 12-25 07:03:48] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924179000>")
[ERROR 12-25 07:03:48] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f892417a920>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:48] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f892417af20>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:48] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924179000>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:03:48] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f892417a920>").
[INFO 12-25 07:03:48] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:48] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f892417af20>").
[INFO 12-25 07:03:48] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:48] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924179000>").
[INFO 12-25 07:03:48] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:48] ax.early_stopping.strategies.base: The number of completed trials (4) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:03:48] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 4).
[INFO 12-25 07:03:58] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87be0>")
[INFO 12-25 07:03:58] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd872b0>")
[INFO 12-25 07:03:59] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd875b0>")
[ERROR 12-25 07:03:59] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87be0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:59] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd872b0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:03:59] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd875b0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:03:59] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87be0>").
[INFO 12-25 07:03:59] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:59] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd872b0>").
[INFO 12-25 07:03:59] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:03:59] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd875b0>").
[INFO 12-25 07:03:59] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:03:59] ax.early_stopping.strategies.base: The number of completed trials (4) is less than the minimum number of curves needed for early stopping (5). Not early stopping.
[INFO 12-25 07:03:59] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 4).
[INFO 12-25 07:04:09] Scheduler: Retrieved COMPLETED trials: [4].
[INFO 12-25 07:04:09] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>")
[INFO 12-25 07:04:09] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>")
[INFO 12-25 07:04:09] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>")
[ERROR 12-25 07:04:09] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:09] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:09] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:04:09] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>").
[INFO 12-25 07:04:09] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:09] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>").
[INFO 12-25 07:04:09] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:09] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195f90>").
[INFO 12-25 07:04:09] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:04:09] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.
[INFO 12-25 07:04:09] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[WARNING 12-25 07:04:10] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:04:10] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a950>")
[INFO 12-25 07:04:10] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4b970>")
[INFO 12-25 07:04:10] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4ab60>")
[ERROR 12-25 07:04:10] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a950>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:10] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4b970>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:10] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4ab60>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 07:04:10] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a950>").
[INFO 12-25 07:04:10] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:10] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4b970>").
[INFO 12-25 07:04:10] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:10] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4ab60>").
[INFO 12-25 07:04:10] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:04:10] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.
[INFO 12-25 07:04:10] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:04:10] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:04:20] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197160>")
[INFO 12-25 07:04:20] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924196c20>")
[INFO 12-25 07:04:20] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195900>")
[ERROR 12-25 07:04:20] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197160>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:20] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924196c20>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:20] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195900>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 07:04:20] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924197160>").
[INFO 12-25 07:04:20] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:20] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924196c20>").
[INFO 12-25 07:04:20] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:20] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924195900>").
[INFO 12-25 07:04:20] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:04:20] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.
[INFO 12-25 07:04:20] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:04:20] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:04:30] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241951e0>")
[INFO 12-25 07:04:30] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924196fe0>")
[INFO 12-25 07:04:30] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241965c0>")
[ERROR 12-25 07:04:30] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241951e0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:30] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924196fe0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:30] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241965c0>"). Ignoring for now -- will retry query on next call to fetch.
[WARNING 12-25 07:04:30] Scheduler: Failed to fetch val_acc for trial 5, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241951e0>").
[INFO 12-25 07:04:30] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 5 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:30] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924196fe0>").
[INFO 12-25 07:04:30] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:30] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241965c0>").
[INFO 12-25 07:04:30] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:04:30] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.
[INFO 12-25 07:04:30] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:04:30] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:04:40] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a680>")
[INFO 12-25 07:04:40] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49ba0>")
[ERROR 12-25 07:04:40] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a680>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:04:40] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49ba0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:04:40] Scheduler: Failed to fetch val_acc for trial 6, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a680>").
[INFO 12-25 07:04:40] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 6 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:04:40] Scheduler: Failed to fetch val_acc for trial 7, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49ba0>").
[INFO 12-25 07:04:40] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 7 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:04:40] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.09994666666666667.
[INFO 12-25 07:04:40] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:04:40] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:04:50] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.09994666666666667.
[INFO 12-25 07:04:50] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:04:50] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:05:00] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.09994666666666667.
[INFO 12-25 07:05:00] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:05:00] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:05:10] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.09994666666666667.
[INFO 12-25 07:05:10] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:05:10] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:05:20] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:05:20] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:05:20] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:05:30] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:05:30] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:05:30] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:05:41] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:05:41] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:05:41] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:05:51] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:05:51] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:05:51] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:06:01] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:06:01] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:06:01] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:06:11] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:06:11] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:06:11] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:06:21] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:06:21] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:06:21] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:06:31] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:06:31] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:06:31] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:06:41] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:06:41] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:06:41] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:06:51] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:06:51] ax.early_stopping.strategies.percentile: Considering trial 5 for early stopping.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.base: Last progression of Trial 5 is 0.3999466666666667.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 7 0.906273 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.percentile: Early stopping decision for 5: True. Reason: Trial objective value 0.9491976561217472 is worse than 70.0-th percentile (0.9492092034499633) across comparable trials.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.29994666666666664.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.base: Trial 6's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.percentile: Considering trial 7 for early stopping.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.base: Last progression of Trial 7 is 0.3999466666666667.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 7 0.906273 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:06:52] ax.early_stopping.strategies.percentile: Early stopping decision for 7: True. Reason: Trial objective value 0.9062728153660967 is worse than 70.0-th percentile (0.9492092034499633) across comparable trials.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:06:54] Scheduler: Running trials [8]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:07:03] Scheduler: Running trials [9]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:07:03] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[WARNING 12-25 07:07:03] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:07:03] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241292d0>")
[INFO 12-25 07:07:03] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fc10>")
[ERROR 12-25 07:07:03] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241292d0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:07:03] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fc10>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:07:03] Scheduler: Failed to fetch val_acc for trial 8, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f89241292d0>").
[INFO 12-25 07:07:03] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 8 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:07:03] Scheduler: Failed to fetch val_acc for trial 9, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9fc10>").
[INFO 12-25 07:07:03] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 9 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:07:03] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.base: There is not yet any data associated with trial 8 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.base: There is not yet any data associated with trial 9 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.3999466666666667.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:07:03] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.950990422605881 is better than 70.0-th percentile (0.9492496190987196) across comparable trials.
[INFO 12-25 07:07:03] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:07:13] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f8b0>")
[INFO 12-25 07:07:13] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f8b0>")
[ERROR 12-25 07:07:13] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f8b0>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:07:13] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f8b0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:07:13] Scheduler: Failed to fetch val_acc for trial 8, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f8b0>").
[INFO 12-25 07:07:13] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 8 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:07:13] Scheduler: Failed to fetch val_acc for trial 9, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f8b0>").
[INFO 12-25 07:07:13] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 9 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:07:13] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.base: There is not yet any data associated with trial 8 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.base: There is not yet any data associated with trial 9 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.3999466666666667.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:07:13] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.950990422605881 is better than 70.0-th percentile (0.9492496190987196) across comparable trials.
[INFO 12-25 07:07:13] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:07:23] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87610>")
[INFO 12-25 07:07:23] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87610>")
[ERROR 12-25 07:07:23] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87610>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:07:23] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87610>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:07:23] Scheduler: Failed to fetch val_acc for trial 8, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87610>").
[INFO 12-25 07:07:23] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 8 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:07:23] Scheduler: Failed to fetch val_acc for trial 9, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87610>").
[INFO 12-25 07:07:23] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 9 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:07:23] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.base: There is not yet any data associated with trial 8 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.base: There is not yet any data associated with trial 9 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.3999466666666667.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:07:23] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.950990422605881 is better than 70.0-th percentile (0.9492496190987196) across comparable trials.
[INFO 12-25 07:07:23] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:07:33] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd86710>")
[INFO 12-25 07:07:33] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd86710>")
[ERROR 12-25 07:07:33] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd86710>"). Ignoring for now -- will retry query on next call to fetch.
[ERROR 12-25 07:07:33] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd86710>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:07:33] Scheduler: Failed to fetch val_acc for trial 8, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd86710>").
[INFO 12-25 07:07:33] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 8 is still RUNNING continuing the experiment and retrying on next poll...
[WARNING 12-25 07:07:33] Scheduler: Failed to fetch val_acc for trial 9, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd86710>").
[INFO 12-25 07:07:33] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 9 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:07:33] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.base: There is not yet any data associated with trial 8 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.base: There is not yet any data associated with trial 9 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.3999466666666667.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:07:33] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.950990422605881 is better than 70.0-th percentile (0.9492496190987196) across comparable trials.
[INFO 12-25 07:07:33] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:07:43] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9ee00>")
[ERROR 12-25 07:07:43] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9ee00>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:07:43] Scheduler: Failed to fetch val_acc for trial 9, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9ee00>").
[INFO 12-25 07:07:43] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 9 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:07:43] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:07:43] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.09994666666666667.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.base: Trial 8's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.base: There is not yet any data associated with trial 9 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.49994666666666665.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:07:43] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.9556556085610599 is better than 70.0-th percentile (0.9540129545010143) across comparable trials.
[INFO 12-25 07:07:43] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:07:54] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:07:54] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:07:54] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.09994666666666667.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.base: Trial 8's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.base: Last progression of Trial 9 is 0.09994666666666667.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.base: Trial 9's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.49994666666666665.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:07:54] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.9556556085610599 is better than 70.0-th percentile (0.9540129545010143) across comparable trials.
[INFO 12-25 07:07:54] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:08:04] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:08:04] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:08:04] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.09994666666666667.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.base: Trial 8's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.base: Last progression of Trial 9 is 0.09994666666666667.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.base: Trial 9's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.49994666666666665.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:08:04] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.9556556085610599 is better than 70.0-th percentile (0.9540129545010143) across comparable trials.
[INFO 12-25 07:08:04] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:08:14] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:08:14] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:08:14] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.09994666666666667.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.base: Trial 8's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.base: Last progression of Trial 9 is 0.09994666666666667.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.base: Trial 9's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.49994666666666665.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:08:14] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.9556556085610599 is better than 70.0-th percentile (0.9540129545010143) across comparable trials.
[INFO 12-25 07:08:14] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:08:24] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:08:24] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:08:24] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.09994666666666667.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.base: Trial 8's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.base: Last progression of Trial 9 is 0.09994666666666667.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.base: Trial 9's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.49994666666666665.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:08:24] ax.early_stopping.strategies.percentile: Early stopping decision for 6: False. Reason: Trial objective value 0.9556556085610599 is better than 70.0-th percentile (0.9540129545010143) across comparable trials.
[INFO 12-25 07:08:24] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:08:34] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.5999466666666666.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.19994666666666666.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.base: Trial 8's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.base: Last progression of Trial 9 is 0.19994666666666666.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.base: Trial 9's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.percentile: Considering trial 6 for early stopping.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.base: Last progression of Trial 6 is 0.5999466666666666.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:08:34] ax.early_stopping.strategies.percentile: Early stopping decision for 6: True. Reason: Trial objective value 0.9569029895032513 is worse than 70.0-th percentile (0.9569124109096465) across comparable trials.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:08:41] Scheduler: Running trials [10]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:08:43] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[WARNING 12-25 07:08:43] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:08:43] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f850>")
[ERROR 12-25 07:08:43] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f850>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:08:43] Scheduler: Failed to fetch val_acc for trial 10, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9f850>").
[INFO 12-25 07:08:43] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 10 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:08:43] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:08:43] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:08:43] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:08:53] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd48ee0>")
[ERROR 12-25 07:08:53] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd48ee0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:08:53] Scheduler: Failed to fetch val_acc for trial 10, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd48ee0>").
[INFO 12-25 07:08:53] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 10 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:08:53] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:08:53] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:08:53] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:09:03] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd48ee0>")
[ERROR 12-25 07:09:03] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd48ee0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:09:03] Scheduler: Failed to fetch val_acc for trial 10, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd48ee0>").
[INFO 12-25 07:09:03] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 10 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:09:03] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:09:03] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:09:03] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:09:13] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49a80>")
[ERROR 12-25 07:09:13] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49a80>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:09:13] Scheduler: Failed to fetch val_acc for trial 10, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49a80>").
[INFO 12-25 07:09:13] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 10 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:09:13] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:09:13] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:09:13] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:09:23] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49900>")
[ERROR 12-25 07:09:23] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49900>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:09:23] Scheduler: Failed to fetch val_acc for trial 10, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49900>").
[INFO 12-25 07:09:23] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 10 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:09:23] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:09:23] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:09:23] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:09:33] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:09:33] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:09:33] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:09:43] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:09:43] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:09:43] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:09:54] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:09:54] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.3999466666666667.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9605070563959958 is better than 70.0-th percentile (0.9502964106686597) across comparable trials.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.base: Last progression of Trial 9 is 0.29994666666666664.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.base: Trial 9's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.09994666666666667.
[INFO 12-25 07:09:54] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:09:54] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:10:04] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:10:04] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.3999466666666667.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9605070563959958 is better than 70.0-th percentile (0.9497759017157437) across comparable trials.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.percentile: Considering trial 9 for early stopping.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.base: Last progression of Trial 9 is 0.3999466666666667.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.percentile: Early stopping decision for 9: True. Reason: Trial objective value 0.920409746651579 is worse than 70.0-th percentile (0.9497759017157437) across comparable trials.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.09994666666666667.
[INFO 12-25 07:10:04] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:10:08] Scheduler: Running trials [11]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:10:09] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[WARNING 12-25 07:10:09] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:10:09] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd495a0>")
[ERROR 12-25 07:10:09] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd495a0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:10:09] Scheduler: Failed to fetch val_acc for trial 11, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd495a0>").
[INFO 12-25 07:10:09] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 11 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:10:09] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:10:09] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.3999466666666667.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9605070563959958 is better than 70.0-th percentile (0.9497759017157437) across comparable trials.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.09994666666666667.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:10:09] ax.early_stopping.strategies.base: There is not yet any data associated with trial 11 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:10:09] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:10:19] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9c100>")
[ERROR 12-25 07:10:19] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9c100>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:10:19] Scheduler: Failed to fetch val_acc for trial 11, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9c100>").
[INFO 12-25 07:10:19] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 11 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:10:19] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:10:19] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:10:19] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.3999466666666667.
[INFO 12-25 07:10:19] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:10:19] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9605070563959958 is better than 70.0-th percentile (0.9497759017157437) across comparable trials.
[INFO 12-25 07:10:19] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:10:20] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.19994666666666666.
[INFO 12-25 07:10:20] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:10:20] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:10:20] ax.early_stopping.strategies.base: There is not yet any data associated with trial 11 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:10:20] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:10:30] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87ac0>")
[ERROR 12-25 07:10:30] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87ac0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:10:30] Scheduler: Failed to fetch val_acc for trial 11, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd87ac0>").
[INFO 12-25 07:10:30] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 11 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:10:30] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.3999466666666667.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9605070563959958 is better than 70.0-th percentile (0.9497759017157437) across comparable trials.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.19994666666666666.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:10:30] ax.early_stopping.strategies.base: There is not yet any data associated with trial 11 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:10:30] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:10:40] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d330>")
[ERROR 12-25 07:10:40] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d330>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:10:40] Scheduler: Failed to fetch val_acc for trial 11, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d330>").
[INFO 12-25 07:10:40] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 11 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:10:40] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.49994666666666665.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9637372424368947 is better than 70.0-th percentile (0.9560361377714216) across comparable trials.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.19994666666666666.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:10:40] ax.early_stopping.strategies.base: There is not yet any data associated with trial 11 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:10:40] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:10:50] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a260>")
[ERROR 12-25 07:10:50] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a260>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:10:50] Scheduler: Failed to fetch val_acc for trial 11, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a260>").
[INFO 12-25 07:10:50] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 11 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:10:50] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.49994666666666665.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9637372424368947 is better than 70.0-th percentile (0.9560361377714216) across comparable trials.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.19994666666666666.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:10:50] ax.early_stopping.strategies.base: There is not yet any data associated with trial 11 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:10:50] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:11:00] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:11:00] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.49994666666666665.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9637372424368947 is better than 70.0-th percentile (0.9560361377714216) across comparable trials.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.19994666666666666.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.09994666666666667.
[INFO 12-25 07:11:00] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:00] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:11:10] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.49994666666666665.
[INFO 12-25 07:11:10] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.49994666666666665.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9637372424368947 is better than 70.0-th percentile (0.9560361377714216) across comparable trials.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.29994666666666664.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.09994666666666667.
[INFO 12-25 07:11:10] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:10] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:11:21] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.5999466666666666.
[INFO 12-25 07:11:21] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.5999466666666666.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9669882033931652 is better than 70.0-th percentile (0.9572829642414427) across comparable trials.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.29994666666666664.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.09994666666666667.
[INFO 12-25 07:11:21] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:21] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:11:31] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.5999466666666666.
[INFO 12-25 07:11:31] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.5999466666666666.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9669882033931652 is better than 70.0-th percentile (0.9572829642414427) across comparable trials.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.29994666666666664.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.09994666666666667.
[INFO 12-25 07:11:31] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:31] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:11:41] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.5999466666666666.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.5999466666666666.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9669882033931652 is better than 70.0-th percentile (0.9572829642414427) across comparable trials.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.29994666666666664.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.19994666666666666.
[INFO 12-25 07:11:41] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:41] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:11:51] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.5999466666666666.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.5999466666666666.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9669882033931652 is better than 70.0-th percentile (0.9572829642414427) across comparable trials.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.29994666666666664.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.base: Trial 10's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.19994666666666666.
[INFO 12-25 07:11:51] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:11:51] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:12:01] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.5999466666666666.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.5999466666666666.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9669882033931652 is better than 70.0-th percentile (0.9572829642414427) across comparable trials.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.3999466666666667.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9565122045319655 is better than 70.0-th percentile (0.950990422605881) across comparable trials.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.19994666666666666.
[INFO 12-25 07:12:01] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:12:01] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:12:12] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.6999466666666667.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.6999466666666667.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694397909544336 is better than 70.0-th percentile (0.95919573822752) across comparable trials.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.3999466666666667.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9565122045319655 is better than 70.0-th percentile (0.950990422605881) across comparable trials.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.19994666666666666.
[INFO 12-25 07:12:12] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:12:12] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:12:22] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.6999466666666667.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.6999466666666667.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694397909544336 is better than 70.0-th percentile (0.95919573822752) across comparable trials.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.3999466666666667.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9565122045319655 is better than 70.0-th percentile (0.950990422605881) across comparable trials.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.19994666666666666.
[INFO 12-25 07:12:22] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:12:22] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:12:32] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.6999466666666667.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.6999466666666667.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694397909544336 is better than 70.0-th percentile (0.95919573822752) across comparable trials.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.3999466666666667.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9565122045319655 is better than 70.0-th percentile (0.950990422605881) across comparable trials.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.29994666666666664.
[INFO 12-25 07:12:32] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:12:32] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:12:42] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.6999466666666667.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.6999466666666667.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694397909544336 is better than 70.0-th percentile (0.95919573822752) across comparable trials.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.49994666666666665.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 10 0.960190 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9601898406687348 is better than 70.0-th percentile (0.9573679900076875) across comparable trials.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.29994666666666664.
[INFO 12-25 07:12:42] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:12:42] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:12:52] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.7999466666666667.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9691688257122401 is better than 70.0-th percentile (0.9611862373577602) across comparable trials.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.49994666666666665.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 10 0.960190 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9601898406687348 is better than 70.0-th percentile (0.9573679900076875) across comparable trials.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.29994666666666664.
[INFO 12-25 07:12:52] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:12:52] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:03] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.7999466666666667.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9691688257122401 is better than 70.0-th percentile (0.9611862373577602) across comparable trials.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.49994666666666665.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 10 0.960190 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9601898406687348 is better than 70.0-th percentile (0.9573679900076875) across comparable trials.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.29994666666666664.
[INFO 12-25 07:13:03] ax.early_stopping.strategies.base: Trial 11's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:13:03] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:13] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.7999466666666667.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9691688257122401 is better than 70.0-th percentile (0.9611862373577602) across comparable trials.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.49994666666666665.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 10 0.960190 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9601898406687348 is better than 70.0-th percentile (0.9573679900076875) across comparable trials.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Considering trial 11 for early stopping.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.base: Last progression of Trial 11 is 0.3999466666666667.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 11 0.931136 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:13:13] ax.early_stopping.strategies.percentile: Early stopping decision for 11: True. Reason: Trial objective value 0.9311363981862374 is worse than 70.0-th percentile (0.950469913652965) across comparable trials.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:13:17] Scheduler: Running trials [12]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:18] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[WARNING 12-25 07:13:18] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:13:18] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcddb0>")
[ERROR 12-25 07:13:18] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcddb0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:13:18] Scheduler: Failed to fetch val_acc for trial 12, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcddb0>").
[INFO 12-25 07:13:18] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 12 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:18] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.7999466666666667.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9691688257122401 is better than 70.0-th percentile (0.9611862373577602) across comparable trials.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.49994666666666665.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.952370 1 0.828175 2 0.943479 3 0.957558 4 0.941579 6 0.955656 8 0.963737 10 0.960190 Name: 0.49994666666666665, dtype: float64.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9601898406687348 is better than 70.0-th percentile (0.9573679900076875) across comparable trials.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:13:18] ax.early_stopping.strategies.base: There is not yet any data associated with trial 12 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:13:18] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:13:28] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcee60>")
[ERROR 12-25 07:13:28] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcee60>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:13:28] Scheduler: Failed to fetch val_acc for trial 12, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcee60>").
[INFO 12-25 07:13:28] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 12 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:29] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.7999466666666667.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9691688257122401 is better than 70.0-th percentile (0.9611862373577602) across comparable trials.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.5999466666666666.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 10 0.962985 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.962984980566092 is better than 70.0-th percentile (0.9585469259803459) across comparable trials.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:13:29] ax.early_stopping.strategies.base: There is not yet any data associated with trial 12 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:13:29] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:13:39] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a980>")
[ERROR 12-25 07:13:39] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a980>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:13:39] Scheduler: Failed to fetch val_acc for trial 12, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a980>").
[INFO 12-25 07:13:39] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 12 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:39] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.8999466666666667.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694212080100373 is better than 70.0-th percentile (0.9614762379877342) across comparable trials.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.5999466666666666.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 10 0.962985 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.962984980566092 is better than 70.0-th percentile (0.9585469259803459) across comparable trials.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:13:39] ax.early_stopping.strategies.base: There is not yet any data associated with trial 12 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:13:39] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:13:49] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9ce80>")
[ERROR 12-25 07:13:49] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9ce80>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:13:49] Scheduler: Failed to fetch val_acc for trial 12, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9ce80>").
[INFO 12-25 07:13:49] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 12 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:49] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.8999466666666667.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694212080100373 is better than 70.0-th percentile (0.9614762379877342) across comparable trials.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.5999466666666666.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 10 0.962985 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.962984980566092 is better than 70.0-th percentile (0.9585469259803459) across comparable trials.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:13:49] ax.early_stopping.strategies.base: There is not yet any data associated with trial 12 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:13:49] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:13:59] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a260>")
[ERROR 12-25 07:13:59] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a260>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:13:59] Scheduler: Failed to fetch val_acc for trial 12, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd4a260>").
[INFO 12-25 07:13:59] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 12 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:13:59] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.8999466666666667.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694212080100373 is better than 70.0-th percentile (0.9614762379877342) across comparable trials.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.5999466666666666.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.956922 1 0.821675 2 0.948403 3 0.958727 4 0.943494 6 0.956903 8 0.966988 10 0.962985 Name: 0.5999466666666666, dtype: float64.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.962984980566092 is better than 70.0-th percentile (0.9585469259803459) across comparable trials.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:13:59] ax.early_stopping.strategies.base: There is not yet any data associated with trial 12 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:13:59] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:14:09] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:14:09] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.8999466666666667.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694212080100373 is better than 70.0-th percentile (0.9614762379877342) across comparable trials.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.6999466666666667.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 10 0.965193 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9651925856872056 is better than 70.0-th percentile (0.960797114076328) across comparable trials.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.09994666666666667.
[INFO 12-25 07:14:09] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:14:09] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:14:19] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:14:20] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.percentile: Considering trial 8 for early stopping.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.base: Last progression of Trial 8 is 0.8999466666666667.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.percentile: Early stopping decision for 8: False. Reason: Trial objective value 0.9694212080100373 is better than 70.0-th percentile (0.9614762379877342) across comparable trials.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.6999466666666667.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 10 0.965193 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9651925856872056 is better than 70.0-th percentile (0.960797114076328) across comparable trials.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.09994666666666667.
[INFO 12-25 07:14:20] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:14:20] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:14:30] Scheduler: Retrieved COMPLETED trials: [8].
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:14:30] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.6999466666666667.
[INFO 12-25 07:14:30] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:14:30] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:14:30] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.6999466666666667.
[INFO 12-25 07:14:30] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 10 0.965193 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:14:30] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9651925856872056 is better than 70.0-th percentile (0.960797114076328) across comparable trials.
[INFO 12-25 07:14:30] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:14:30] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.09994666666666667.
[INFO 12-25 07:14:30] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:14:35] Scheduler: Running trials [13]...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:14:37] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[WARNING 12-25 07:14:37] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:14:37] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcff70>")
[ERROR 12-25 07:14:37] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcff70>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:14:37] Scheduler: Failed to fetch val_acc for trial 13, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcff70>").
[INFO 12-25 07:14:37] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 13 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:14:37] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.6999466666666667.
[INFO 12-25 07:14:37] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.6999466666666667.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.958693 1 0.820532 2 0.952819 3 0.959698 4 0.941893 8 0.969440 10 0.965193 Name: 0.6999466666666667, dtype: float64.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9651925856872056 is better than 70.0-th percentile (0.960797114076328) across comparable trials.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.09994666666666667.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:14:37] ax.early_stopping.strategies.base: There is not yet any data associated with trial 13 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:14:37] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:14:47] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd495a0>")
[ERROR 12-25 07:14:47] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd495a0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:14:47] Scheduler: Failed to fetch val_acc for trial 13, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd495a0>").
[INFO 12-25 07:14:47] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 13 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:14:47] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:14:47] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.7999466666666667.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 10 0.966713 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9667129107010013 is better than 70.0-th percentile (0.9634594702082468) across comparable trials.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.09994666666666667.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:14:47] ax.early_stopping.strategies.base: There is not yet any data associated with trial 13 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:14:47] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:14:57] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d930>")
[ERROR 12-25 07:14:57] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d930>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:14:57] Scheduler: Failed to fetch val_acc for trial 13, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d930>").
[INFO 12-25 07:14:57] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 13 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:14:57] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.7999466666666667.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 10 0.966713 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9667129107010013 is better than 70.0-th percentile (0.9634594702082468) across comparable trials.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.19994666666666666.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:14:57] ax.early_stopping.strategies.base: There is not yet any data associated with trial 13 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:14:57] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:15:07] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924129960>")
[ERROR 12-25 07:15:07] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924129960>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:15:07] Scheduler: Failed to fetch val_acc for trial 13, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f8924129960>").
[INFO 12-25 07:15:07] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 13 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:15:07] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.7999466666666667.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 10 0.966713 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9667129107010013 is better than 70.0-th percentile (0.9634594702082468) across comparable trials.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.19994666666666666.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:15:07] ax.early_stopping.strategies.base: There is not yet any data associated with trial 13 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:15:07] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:15:17] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49960>")
[ERROR 12-25 07:15:17] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49960>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:15:17] Scheduler: Failed to fetch val_acc for trial 13, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd49960>").
[INFO 12-25 07:15:17] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 13 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:15:17] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.7999466666666667.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.7999466666666667.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959726 1 0.801577 2 0.955505 3 0.962646 4 0.940550 8 0.969169 10 0.966713 Name: 0.7999466666666667, dtype: float64.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9667129107010013 is better than 70.0-th percentile (0.9634594702082468) across comparable trials.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.19994666666666666.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:15:17] ax.early_stopping.strategies.base: There is not yet any data associated with trial 13 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:15:17] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:15:27] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d450>")
[ERROR 12-25 07:15:27] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d450>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:15:27] Scheduler: Failed to fetch val_acc for trial 13, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cd9d450>").
[INFO 12-25 07:15:27] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 13 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:15:27] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.8999466666666667.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 10 0.967100 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9671003120503973 is better than 70.0-th percentile (0.9637951476512393) across comparable trials.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.19994666666666666.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:15:28] ax.early_stopping.strategies.base: There is not yet any data associated with trial 13 and metric val_acc. Not early stopping this trial.
[INFO 12-25 07:15:28] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:15:38] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:15:38] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.8999466666666667.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 10 0.967100 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9671003120503973 is better than 70.0-th percentile (0.9637951476512393) across comparable trials.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.29994666666666664.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.base: Last progression of Trial 13 is 0.09994666666666667.
[INFO 12-25 07:15:38] ax.early_stopping.strategies.base: Trial 13's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:38] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:15:48] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:15:48] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.8999466666666667.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 10 0.967100 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9671003120503973 is better than 70.0-th percentile (0.9637951476512393) across comparable trials.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.29994666666666664.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.base: Last progression of Trial 13 is 0.09994666666666667.
[INFO 12-25 07:15:48] ax.early_stopping.strategies.base: Trial 13's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:48] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:15:58] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:15:58] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.8999466666666667.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 10 0.967100 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9671003120503973 is better than 70.0-th percentile (0.9637951476512393) across comparable trials.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.29994666666666664.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.base: Last progression of Trial 13 is 0.09994666666666667.
[INFO 12-25 07:15:58] ax.early_stopping.strategies.base: Trial 13's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:15:58] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:16:08] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.8999466666666667.
[INFO 12-25 07:16:08] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.percentile: Considering trial 10 for early stopping.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.base: Last progression of Trial 10 is 0.8999466666666667.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.959984 1 0.782439 2 0.956533 3 0.962969 4 0.938594 8 0.969421 10 0.967100 Name: 0.8999466666666667, dtype: float64.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.percentile: Early stopping decision for 10: False. Reason: Trial objective value 0.9671003120503973 is better than 70.0-th percentile (0.9637951476512393) across comparable trials.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.29994666666666664.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.base: Trial 12's most recent progression (0.29994666666666664) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.base: Last progression of Trial 13 is 0.09994666666666667.
[INFO 12-25 07:16:08] ax.early_stopping.strategies.base: Trial 13's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:16:08] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 3).
[INFO 12-25 07:16:18] Scheduler: Retrieved COMPLETED trials: [10].
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:16:18] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:16:18] ax.early_stopping.strategies.percentile: Considering trial 12 for early stopping.
[INFO 12-25 07:16:18] ax.early_stopping.strategies.base: Last progression of Trial 12 is 0.3999466666666667.
[INFO 12-25 07:16:19] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 11 0.931136 12 0.932686 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:16:19] ax.early_stopping.strategies.percentile: Early stopping decision for 12: True. Reason: Trial objective value 0.9326860890012657 is worse than 70.0-th percentile (0.949949404700049) across comparable trials.
[INFO 12-25 07:16:19] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:16:19] ax.early_stopping.strategies.base: Last progression of Trial 13 is 0.19994666666666666.
[INFO 12-25 07:16:19] ax.early_stopping.strategies.base: Trial 13's most recent progression (0.19994666666666666) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
[INFO 12-25 07:16:21] Scheduler: Running trials [14]...
[WARNING 12-25 07:16:21] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
[INFO 12-25 07:16:21] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cc5afb0>")
[ERROR 12-25 07:16:21] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cc5afb0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:16:21] Scheduler: Failed to fetch val_acc for trial 14, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cc5afb0>").
[INFO 12-25 07:16:21] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 14 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:16:21] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:16:21] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:16:21] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 2).
[INFO 12-25 07:16:31] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcc850>")
[ERROR 12-25 07:16:31] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcc850>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:16:31] Scheduler: Failed to fetch val_acc for trial 14, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cdcc850>").
[INFO 12-25 07:16:31] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 14 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:16:31] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:16:31] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:16:31] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 2).
[INFO 12-25 07:16:41] ax.core.metric: MetricFetchE INFO: Initialized MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cc5b1f0>")
[ERROR 12-25 07:16:41] ax.core.experiment: Discovered Metric fetching Err while attaching data MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cc5b1f0>"). Ignoring for now -- will retry query on next call to fetch.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [WARNING 12-25 07:16:41] Scheduler: Failed to fetch val_acc for trial 14, found MetricFetchE(message="No 'scalar' data found for trial in multiplexer mul=<tensorboard.backend.event_processing.plugin_event_multiplexer.EventMultiplexer object at 0x7f891cc5b1f0>").
[INFO 12-25 07:16:41] Scheduler: MetricFetchE INFO: Because val_acc is available_while_running and trial 14 is still RUNNING continuing the experiment and retrying on next poll...
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:16:41] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:16:41] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:16:41] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 2).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:16:51] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:16:51] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:16:51] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 2).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:17:01] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:17:01] ax.early_stopping.utils: Got exception `x and y arrays must have at least 2 entries` during interpolation. Using uninterpolated values instead.
[INFO 12-25 07:17:01] ax.early_stopping.strategies.percentile: Considering trial 13 for early stopping.
[INFO 12-25 07:17:01] ax.early_stopping.strategies.base: Last progression of Trial 13 is 0.3999466666666667.
[INFO 12-25 07:17:01] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 11 0.931136 12 0.932686 13 0.920202 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:17:01] ax.early_stopping.strategies.percentile: Early stopping decision for 13: True. Reason: Trial objective value 0.9202015579040416 is worse than 70.0-th percentile (0.949428895747133) across comparable trials.
[INFO 12-25 07:17:01] ax.early_stopping.strategies.percentile: Considering trial 14 for early stopping.
[INFO 12-25 07:17:01] ax.early_stopping.strategies.base: Last progression of Trial 14 is 0.09994666666666667.
[INFO 12-25 07:17:01] ax.early_stopping.strategies.base: Trial 14's most recent progression (0.09994666666666667) that is available for metric val_acc falls out of the min/max_progression range (0.3, None). Not early stopping this trial.
[INFO 12-25 07:17:02] Scheduler: Done submitting trials, waiting for remaining 1 running trials...
[WARNING 12-25 07:17:02] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:17:02] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.09994666666666667.
[INFO 12-25 07:17:02] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:17:02] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 1).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:17:12] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.19994666666666666.
[INFO 12-25 07:17:12] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:17:12] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 1).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:17:22] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:17:22] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:17:22] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 1).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:17:32] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.29994666666666664.
[INFO 12-25 07:17:32] ax.early_stopping.strategies.base: No trials have reached 0.3. Not stopping any trials.
[INFO 12-25 07:17:32] Scheduler: Waiting for completed trials (for 10 sec, currently running trials: 1).
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( [INFO 12-25 07:17:42] ax.early_stopping.strategies.base: Last progression of any candidate for trial stopping is 0.3999466666666667.
[INFO 12-25 07:17:42] ax.early_stopping.strategies.percentile: Considering trial 14 for early stopping.
[INFO 12-25 07:17:42] ax.early_stopping.strategies.base: Last progression of Trial 14 is 0.3999466666666667.
[INFO 12-25 07:17:42] ax.early_stopping.strategies.percentile: Early stopping objective at last progression is: 0 0.949255 1 0.834013 2 0.933829 3 0.953359 4 0.936954 5 0.949198 6 0.950990 7 0.906273 8 0.960507 9 0.920410 10 0.956512 11 0.931136 12 0.932686 13 0.920202 14 0.941292 Name: 0.3999466666666667, dtype: float64.
[INFO 12-25 07:17:42] ax.early_stopping.strategies.percentile: Early stopping decision for 14: True. Reason: Trial objective value 0.9412920607134626 is worse than 70.0-th percentile (0.9492438454346116) across comparable trials.
[WARNING 12-25 07:17:43] Scheduler: Both `init_seconds_between_polls` and `early_stopping_strategy supplied. `init_seconds_between_polls=1` will be overrridden by `early_stopping_strategy.seconds_between_polls=10` and polling will take place at a constant rate.
CPU times: user 1min 4s, sys: 1.3 s, total: 1min 6s Wall time: 33min 37s
OptimizationResult()
First, we examine the data stored on the experiment. This shows that each trial is associated with an entire learning curve, represented by the column "steps".
experiment.lookup_data().map_df.head(n=10)
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
| arm_name | metric_name | mean | sem | trial_index | step | |
|---|---|---|---|---|---|---|
| 0 | 0_0 | val_acc | 0.927906 | NaN | 0 | 1874.0 |
| 1 | 0_0 | val_acc | 0.938974 | NaN | 0 | 3749.0 |
| 2 | 0_0 | val_acc | 0.944361 | NaN | 0 | 5624.0 |
| 3 | 0_0 | val_acc | 0.949255 | NaN | 0 | 7499.0 |
| 4 | 0_0 | val_acc | 0.952370 | NaN | 0 | 9374.0 |
| 5 | 0_0 | val_acc | 0.956922 | NaN | 0 | 11249.0 |
| 6 | 0_0 | val_acc | 0.958693 | NaN | 0 | 13124.0 |
| 7 | 0_0 | val_acc | 0.959726 | NaN | 0 | 14999.0 |
| 8 | 0_0 | val_acc | 0.959984 | NaN | 0 | 16874.0 |
| 9 | 0_0 | val_acc | 0.961347 | NaN | 0 | 18749.0 |
Below is a summary of the experiment, showing that a portion of trials have been early stopped.
exp_to_df(experiment)
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
| trial_index | arm_name | trial_status | generation_method | val_acc | hidden_size_1 | hidden_size_2 | learning_rate | dropout | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0_0 | COMPLETED | Sobol | 0.961893 | 57 | 17 | 0.000922 | 0.217434 |
| 1 | 1 | 1_0 | COMPLETED | Sobol | 0.824668 | 16 | 77 | 0.003930 | 0.437174 |
| 2 | 2 | 2_0 | COMPLETED | Sobol | 0.959208 | 28 | 28 | 0.000253 | 0.008146 |
| 3 | 3 | 3_0 | COMPLETED | Sobol | 0.964895 | 91 | 45 | 0.001089 | 0.352883 |
| 4 | 4 | 4_0 | COMPLETED | Sobol | 0.940328 | 118 | 39 | 0.005882 | 0.287827 |
| 5 | 5 | 5_0 | EARLY_STOPPED | BoTorch | 0.949198 | 68 | 60 | 0.000214 | 0.207081 |
| 6 | 6 | 6_0 | EARLY_STOPPED | BoTorch | 0.956903 | 128 | 59 | 0.000434 | 0.500000 |
| 7 | 7 | 7_0 | EARLY_STOPPED | BoTorch | 0.906273 | 16 | 16 | 0.000100 | 0.000000 |
| 8 | 8 | 8_0 | COMPLETED | BoTorch | 0.971802 | 99 | 58 | 0.000524 | 0.009356 |
| 9 | 9 | 9_0 | EARLY_STOPPED | BoTorch | 0.920410 | 62 | 16 | 0.000309 | 0.500000 |
| 10 | 10 | 10_0 | COMPLETED | BoTorch | 0.967131 | 128 | 16 | 0.000504 | 0.307582 |
| 11 | 11 | 11_0 | EARLY_STOPPED | BoTorch | 0.931136 | 60 | 53 | 0.000312 | 0.500000 |
| 12 | 12 | 12_0 | EARLY_STOPPED | BoTorch | 0.932686 | 60 | 54 | 0.000306 | 0.500000 |
| 13 | 13 | 13_0 | EARLY_STOPPED | BoTorch | 0.920202 | 105 | 16 | 0.000133 | 0.500000 |
| 14 | 14 | 14_0 | EARLY_STOPPED | BoTorch | 0.941292 | 71 | 128 | 0.000249 | 0.455020 |
We can give a very rough estimate of the amount of computational savings due to early stopping, by looking at the total number of steps used when early stopping is used versus the number of steps used if we ran all trials to completion. Note to do a true comparison, one should run full HPO loops with and without early stopping (as early stopping will influence the model and future points selected by the generation strategy).
map_df = experiment.lookup_data().map_df
trial_to_max_steps = map_df.groupby("trial_index")["step"].max()
completed_trial_steps = trial_to_max_steps.iloc[0]
savings = 1.0 - trial_to_max_steps.sum() / (
completed_trial_steps * len(trial_to_max_steps)
)
# TODO format nicer
print(f"A rough estimate of the computational savings is {100 * savings}%.")
A rough estimate of the computational savings is 30.669511111111113%.
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
Finally, we show a visualization of learning curves versus actual elapsed wall time. This helps to illustrate that stopped trials make room for additional trials to be run.
# helper function for getting trial start times
def time_started(row):
trial_index = row["trial_index"]
return experiment.trials[trial_index].time_run_started
# helper function for getting trial completion times
def time_completed(row):
trial_index = row["trial_index"]
return experiment.trials[trial_index].time_completed
# helper function for getting relevant data from experiment
# with early stopping into useful dfs
def early_stopping_exp_to_df(experiment):
trials_df = exp_to_df(experiment)
curve_df = experiment.lookup_data().map_df
training_row_df = (
curve_df.groupby("trial_index").max().reset_index()[["trial_index", "steps"]]
)
trials_df = trials_df.merge(training_row_df, on="trial_index")
trials_df["time_started"] = trials_df.apply(func=time_started, axis=1)
trials_df["time_completed"] = trials_df.apply(func=time_completed, axis=1)
start_time = trials_df["time_started"].min()
trials_df["time_started_rel"] = (
trials_df["time_started"] - start_time
).dt.total_seconds()
trials_df["time_completed_rel"] = (
trials_df["time_completed"] - start_time
).dt.total_seconds()
return trials_df, curve_df
def plot_curves_by_wall_time(trials_df, curve_df):
trials = set(curve_df["trial_index"])
fig, ax = plt.subplots(1, 1, figsize=(10, 6))
ax.set(xlabel="seconds since start", ylabel="validation accuracy")
for trial_index in trials:
this_trial_df = curve_df[curve_df["trial_index"] == trial_index]
start_time_rel = trials_df["time_started_rel"].iloc[trial_index]
completed_time_rel = trials_df["time_completed_rel"].iloc[trial_index]
total_steps = trials_df.loc[trial_index, "steps"]
smoothed_curve = this_trial_df["mean"].rolling(window=3).mean()
x = (
start_time_rel
+ (completed_time_rel - start_time_rel)
/ total_steps
* this_trial_df["steps"]
)
ax.plot(
x,
smoothed_curve,
label=f"trial #{trial_index}" if trial_index % 2 == 1 else None,
)
ax.legend()
# wrap in try/except in case of flaky I/O issues
try:
trials_df, curve_df = early_stopping_exp_to_df(experiment)
plot_curves_by_wall_time(trials_df, curve_df)
except Exception as e:
print(f"Encountered exception while plotting results: {e}")
Encountered exception while plotting results: "['steps'] not in index"
/tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat( /tmp/tmp.1H3H6nlBS7/Ax-main/ax/core/map_data.py:200: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. df = pd.concat(
Total runtime of script: 33 minutes, 45.71 seconds.