{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ax Service API with RayTune on PyTorch CNN\n",
"\n",
"Ax integrates easily with different scheduling frameworks and distributed training frameworks. In this example, Ax-driven optimization is executed in a distributed fashion using [RayTune](https://ray.readthedocs.io/en/latest/tune.html). \n",
"\n",
"RayTune is a scalable framework for hyperparameter tuning that provides many state-of-the-art hyperparameter tuning algorithms and seamlessly scales from laptop to distributed cluster with fault tolerance. RayTune leverages [Ray](https://ray.readthedocs.io/)'s Actor API to provide asynchronous parallel and distributed execution.\n",
"\n",
"Ray 'Actors' are a simple and clean abstraction for replicating your Python classes across multiple workers and nodes. Each hyperparameter evaluation is asynchronously executed on a separate Ray actor and reports intermediate training progress back to RayTune. Upon reporting, RayTune then uses this information to performs actions such as early termination, re-prioritization, or checkpointing."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"from ray import tune\n",
"from ray.tune import track\n",
"from ray.tune.suggest.ax import AxSearch\n",
"logger = logging.getLogger(tune.__name__) \n",
"logger.setLevel(level=logging.CRITICAL) # Reduce the number of Ray warnings that are not relevant here."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 10-01 15:42:23] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
},
{
"data": {
"text/html": [
" \n",
" "
]
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"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import torch\n",
"import numpy as np\n",
"\n",
"from ax.plot.contour import plot_contour\n",
"from ax.plot.trace import optimization_trace_single_method\n",
"from ax.service.ax_client import AxClient\n",
"from ax.utils.notebook.plotting import render, init_notebook_plotting\n",
"from ax.utils.tutorials.cnn_utils import CNN, load_mnist, train, evaluate\n",
"\n",
"\n",
"init_notebook_plotting()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Initialize client\n",
"We specify `enforce_sequential_optimization` as False, because Ray runs many trials in parallel. With the sequential optimization enforcement, `AxClient` would expect the first few trials to be completed with data before generating more trials.\n",
"\n",
"When high parallelism is not required, it is best to enforce sequential optimization, as it allows for achieving optimal results in fewer (but sequential) trials. In cases where parallelism is important, such as with distributed training using Ray, we choose to forego minimizing resource utilization and run more trials in parallel."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 10-01 15:42:23] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 2 decimal points.\n"
]
}
],
"source": [
"ax = AxClient(enforce_sequential_optimization=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Set up experiment\n",
"Here we set up the search space and specify the objective; refer to the Ax API tutorials for more detail."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 10-01 15:42:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter lr. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 10-01 15:42:23] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter momentum. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 10-01 15:42:23] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 5 trials, GPEI for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n"
]
}
],
"source": [
"ax.create_experiment(\n",
" name=\"mnist_experiment\",\n",
" parameters=[\n",
" {\"name\": \"lr\", \"type\": \"range\", \"bounds\": [1e-6, 0.4], \"log_scale\": True},\n",
" {\"name\": \"momentum\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
" ],\n",
" objective_name=\"mean_accuracy\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Define how to evaluate trials\n",
"Since we use the Ax Service API here, we evaluate the parameterizations that Ax suggests, using RayTune. The evaluation function follows its usual pattern, taking in a parameterization and outputting an objective value. For detail on evaluation functions, see [Trial Evaluation](https://ax.dev/docs/runner.html). "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def train_evaluate(parameterization):\n",
" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
" train_loader, valid_loader, test_loader = load_mnist(data_path='~/.data')\n",
" net = train(net=CNN(), train_loader=train_loader, parameters=parameterization, dtype=torch.float, device=device)\n",
" track.log(\n",
" mean_accuracy=evaluate(\n",
" net=net,\n",
" data_loader=valid_loader,\n",
" dtype=torch.float,\n",
" device=device,\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Run optimization\n",
"Execute the Ax optimization and trial evaluation in RayTune using [AxSearch algorithm](https://ray.readthedocs.io/en/latest/tune-searchalg.html#ax-search):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 10-01 15:42:23] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 2 decimal points.\n"
]
},
{
"ename": "TypeError",
"evalue": "'AxClient' object is not iterable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtrain_evaluate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msearch_alg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAxSearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# Note that the argument here is the `AxClient`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# Set this level to 1 to see status updates and to 2 to also see trial results.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# To use GPU, specify: resources_per_trial={\"gpu\": 1}.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/virtualenv/python3.7.1/lib/python3.7/site-packages/ray/tune/suggest/ax.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, space, metric, mode, parameter_constraints, outcome_constraints, ax_client, use_early_stopped_trials, max_concurrent)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ax\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_space\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup_experiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msetup_experiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/virtualenv/python3.7.1/lib/python3.7/site-packages/ray/tune/suggest/ax.py\u001b[0m in \u001b[0;36msetup_experiment\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0mparameter_constraints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameter_constraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0moutcome_constraints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_outcome_constraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m minimize=self._mode != \"max\")\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m if any([\n",
"\u001b[0;32m~/build/facebook/Ax/ax/service/ax_client.py\u001b[0m in \u001b[0;36mcreate_experiment\u001b[0;34m(self, parameters, name, objective_name, minimize, parameter_constraints, outcome_constraints, status_quo, overwrite_existing_experiment, experiment_type, choose_generation_strategy_kwargs)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0moutcome_constraints\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutcome_constraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0mstatus_quo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstatus_quo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m \u001b[0mexperiment_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexperiment_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 264\u001b[0m )\n\u001b[1;32m 265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/build/facebook/Ax/ax/service/utils/instantiation.py\u001b[0m in \u001b[0;36mmake_experiment\u001b[0;34m(parameters, name, objective_name, minimize, parameter_constraints, outcome_constraints, status_quo, experiment_type)\u001b[0m\n\u001b[1;32m 327\u001b[0m without importing or instantiating any Ax classes.\"\"\"\n\u001b[1;32m 328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 329\u001b[0;31m \u001b[0mexp_parameters\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mparameter_from_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 330\u001b[0m \u001b[0mstatus_quo_arm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstatus_quo\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mArm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstatus_quo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0mparameter_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexp_parameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'AxClient' object is not iterable"
]
}
],
"source": [
"tune.run(\n",
" train_evaluate, \n",
" num_samples=30, \n",
" search_alg=AxSearch(ax), # Note that the argument here is the `AxClient`.\n",
" verbose=0, # Set this level to 1 to see status updates and to 2 to also see trial results.\n",
" # To use GPU, specify: resources_per_trial={\"gpu\": 1}.\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Retrieve the optimization results"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'lr': 0.001126015869936531, 'momentum': 0.4754412202164531}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_parameters, values = ax.get_best_parameters()\n",
"best_parameters"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'mean_accuracy': 0.9616665982503324}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"means, covariances = values\n",
"means"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Plot the response surface and optimization trace"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
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