{
"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 04-17 18:35:34] ipy_plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
},
{
"data": {
"text/html": [
" \n",
" "
]
},
"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 04-17 18:35:34] 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 04-17 18:35:34] 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": [
"2020-04-17 18:35:34,931\tINFO resource_spec.py:212 -- Starting Ray with 4.35 GiB memory available for workers and up to 2.18 GiB for objects. You can adjust these settings with ray.init(memory=, object_store_memory=).\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-04-17 18:35:35,279\tINFO services.py:1148 -- View the Ray dashboard at \u001b[1m\u001b[32mlocalhost:8265\u001b[39m\u001b[22m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:35] ax.service.ax_client: Generated new trial 0 with parameters {'lr': 0.06, 'momentum': 0.37}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:35] ax.service.ax_client: Generated new trial 1 with parameters {'lr': 0.0, 'momentum': 0.48}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:35] ax.service.ax_client: Generated new trial 2 with parameters {'lr': 0.0, 'momentum': 0.41}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:35] ax.service.ax_client: Generated new trial 3 with parameters {'lr': 0.0, 'momentum': 0.37}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:35] ax.service.ax_client: Generated new trial 4 with parameters {'lr': 0.0, 'momentum': 0.57}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:36] ax.service.ax_client: Generated new trial 5 with parameters {'lr': 0.0, 'momentum': 0.76}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:36] ax.service.ax_client: Generated new trial 6 with parameters {'lr': 0.0, 'momentum': 0.31}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:36] ax.service.ax_client: Generated new trial 7 with parameters {'lr': 0.0, 'momentum': 0.76}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:36] ax.service.ax_client: Generated new trial 8 with parameters {'lr': 0.06, 'momentum': 0.67}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:35:36] ax.service.ax_client: Generated new trial 9 with parameters {'lr': 0.0, 'momentum': 0.93}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4021)\u001b[0m Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /home/travis/.data/MNIST/raw/train-images-idx3-ubyte.gz\n",
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"\u001b[2m\u001b[36m(pid=4020)\u001b[0m \r",
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"\u001b[2m\u001b[36m(pid=4021)\u001b[0m Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /home/travis/.data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n",
"\u001b[2m\u001b[36m(pid=4020)\u001b[0m Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /home/travis/.data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
]
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"\u001b[2m\u001b[36m(pid=4057)\u001b[0m 2020-04-17 18:35:59,554\tINFO trainable.py:217 -- Getting current IP.\n"
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"\u001b[2m\u001b[36m(pid=4153)\u001b[0m 2020-04-17 18:36:42,596\tINFO trainable.py:217 -- Getting current IP.\n"
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"[INFO 04-17 18:37:00] ax.service.ax_client: Generated new trial 16 with parameters {'lr': 0.0, 'momentum': 1.0}.\n"
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"output_type": "stream",
"text": [
"[INFO 04-17 18:37:00] ax.service.ax_client: Completed trial 7 with data: {'mean_accuracy': (0.96, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:02] ax.service.ax_client: Generated new trial 17 with parameters {'lr': 0.4, 'momentum': 1.0}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4185)\u001b[0m 2020-04-17 18:37:03,916\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4186)\u001b[0m 2020-04-17 18:37:05,368\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:19] ax.service.ax_client: Completed trial 8 with data: {'mean_accuracy': (0.11, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:21] ax.service.ax_client: Generated new trial 18 with parameters {'lr': 0.4, 'momentum': 0.0}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:21] ax.service.ax_client: Completed trial 9 with data: {'mean_accuracy': (0.11, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:25] ax.service.ax_client: Generated new trial 19 with parameters {'lr': 0.0, 'momentum': 0.75}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4220)\u001b[0m 2020-04-17 18:37:25,470\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4221)\u001b[0m 2020-04-17 18:37:27,782\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:41] ax.service.ax_client: Completed trial 10 with data: {'mean_accuracy': (0.24, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:44] ax.service.ax_client: Generated new trial 20 with parameters {'lr': 0.0, 'momentum': 0.72}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:44] ax.service.ax_client: Completed trial 11 with data: {'mean_accuracy': (0.92, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:37:47] ax.service.ax_client: Generated new trial 21 with parameters {'lr': 0.0, 'momentum': 0.35}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4250)\u001b[0m 2020-04-17 18:37:47,911\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4251)\u001b[0m 2020-04-17 18:37:49,550\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:04] ax.service.ax_client: Completed trial 12 with data: {'mean_accuracy': (0.67, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:06] ax.service.ax_client: Generated new trial 22 with parameters {'lr': 0.0, 'momentum': 0.18}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:06] ax.service.ax_client: Completed trial 13 with data: {'mean_accuracy': (0.91, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:08] ax.service.ax_client: Generated new trial 23 with parameters {'lr': 0.0, 'momentum': 0.0}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4281)\u001b[0m 2020-04-17 18:38:10,011\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4282)\u001b[0m 2020-04-17 18:38:10,717\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:26] ax.service.ax_client: Completed trial 14 with data: {'mean_accuracy': (0.9, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:28] ax.service.ax_client: Generated new trial 24 with parameters {'lr': 0.0, 'momentum': 0.14}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:28] ax.service.ax_client: Completed trial 15 with data: {'mean_accuracy': (0.1, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4313)\u001b[0m 2020-04-17 18:38:31,715\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:32] ax.service.ax_client: Generated new trial 25 with parameters {'lr': 0.0, 'momentum': 0.9}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4312)\u001b[0m 2020-04-17 18:38:35,071\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:47] ax.service.ax_client: Completed trial 16 with data: {'mean_accuracy': (0.88, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:51] ax.service.ax_client: Generated new trial 26 with parameters {'lr': 0.0, 'momentum': 1.0}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:51] ax.service.ax_client: Completed trial 17 with data: {'mean_accuracy': (0.1, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:51] ModelBridge: Leaving out out-of-design observations for arms: 17_0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4348)\u001b[0m 2020-04-17 18:38:54,954\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:38:56] ax.service.ax_client: Generated new trial 27 with parameters {'lr': 0.0, 'momentum': 0.85}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4347)\u001b[0m 2020-04-17 18:38:58,710\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:11] ax.service.ax_client: Completed trial 18 with data: {'mean_accuracy': (0.1, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:11] ModelBridge: Leaving out out-of-design observations for arms: 17_0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:14] ax.service.ax_client: Generated new trial 28 with parameters {'lr': 0.0, 'momentum': 0.68}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:15] ax.service.ax_client: Completed trial 19 with data: {'mean_accuracy': (0.97, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:15] ModelBridge: Leaving out out-of-design observations for arms: 17_0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4396)\u001b[0m 2020-04-17 18:39:17,169\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:20] ax.service.ax_client: Generated new trial 29 with parameters {'lr': 0.0, 'momentum': 0.77}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4411)\u001b[0m 2020-04-17 18:39:23,688\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:33] ax.service.ax_client: Completed trial 20 with data: {'mean_accuracy': (0.87, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4426)\u001b[0m 2020-04-17 18:39:36,858\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:39] ax.service.ax_client: Completed trial 21 with data: {'mean_accuracy': (0.94, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4441)\u001b[0m 2020-04-17 18:39:43,288\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:53] ax.service.ax_client: Completed trial 22 with data: {'mean_accuracy': (0.93, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4457)\u001b[0m 2020-04-17 18:39:56,556\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:39:59] ax.service.ax_client: Completed trial 23 with data: {'mean_accuracy': (0.97, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4473)\u001b[0m 2020-04-17 18:40:02,902\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:40:12] ax.service.ax_client: Completed trial 24 with data: {'mean_accuracy': (0.85, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4488)\u001b[0m 2020-04-17 18:40:16,506\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:40:19] ax.service.ax_client: Completed trial 25 with data: {'mean_accuracy': (0.49, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4508)\u001b[0m 2020-04-17 18:40:22,621\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:40:32] ax.service.ax_client: Completed trial 26 with data: {'mean_accuracy': (0.86, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m 2020-04-17 18:40:35,876\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:40:38] ax.service.ax_client: Completed trial 27 with data: {'mean_accuracy': (0.89, 0.0)}.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(pid=4539)\u001b[0m 2020-04-17 18:40:42,068\tINFO trainable.py:217 -- Getting current IP.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:40:52] ax.service.ax_client: Completed trial 28 with data: {'mean_accuracy': (0.93, 0.0)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 04-17 18:40:56] ax.service.ax_client: Completed trial 29 with data: {'mean_accuracy': (0.25, 0.0)}.\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"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': 0.0008918200730215226, 'momentum': 0.745181368720066}"
]
},
"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': 0.9738333633678309}"
]
},
"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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