From 894d392dd0a3c42f1f2758c9d5f4923af5a9ebc4 Mon Sep 17 00:00:00 2001 From: Marton Havasi Date: Wed, 24 Jan 2024 13:57:04 -0800 Subject: [PATCH] Submitit ask-tell tutorial (#2127) Summary: Adds a tutorial that shows how to use the AxClient to schedule jobs and tune parameters on a Slurm cluster via SubmitIt. Code changes: 1. The tutorial notebook 2. Linking the tutorial notebook for the website. (index.js, tutorials.json) Testing: Q: How can I make sure that this tutorial shows up correctly on the Ax doc site? Pull Request resolved: https://github.com/facebook/Ax/pull/2127 Reviewed By: mpolson64 Differential Revision: D52700312 Pulled By: mhavasi fbshipit-source-id: d4cd57427e99ac3b0893cd2bfa91b64510b85876 --- setup.py | 1 + tutorials/submitit.ipynb | 7621 ++++++++++++++++++++++++++++++ website/pages/tutorials/index.js | 9 + website/tutorials.json | 4 + 4 files changed, 7635 insertions(+) create mode 100644 tutorials/submitit.ipynb diff --git a/setup.py b/setup.py index 6d3e9f1dd64..4d7fc8e6a31 100644 --- a/setup.py +++ b/setup.py @@ -73,6 +73,7 @@ "pyro-ppl", # Required for to call run_inference. "pytorch-lightning", # For the early stopping tutorial. "papermill", # For executing the tutorials. + "submitit", # Required for building the SubmitIt notebook. ] diff --git a/tutorials/submitit.ipynb b/tutorials/submitit.ipynb new file mode 100644 index 00000000000..2f49c44d235 --- /dev/null +++ b/tutorials/submitit.ipynb @@ -0,0 +1,7621 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameter Optimization on Slurm via SubmitIt\n", + "\n", + "This notebook serves as a quickstart guide for using the Ax library with the SubmitIt library in an ask-tell loop. [SubmitIt](https://github.com/facebookincubator/submitit/) is a Python toolbox for submitting jobs to [Slurm](https://slurm.schedmd.com/quickstart.html). \n", + "\n", + "The notebook demonstrates how to use the Ax client in an ask-tell loop where each trial is scheduled to run on a Slurm cluster asynchronously.\n", + "\n", + "To use this script, run it on a slurm node either as an interactive notebook or export it as a Python script and run it as a Slurm job.\n", + "\n", + "## Importing Necessary Libraries\n", + "Let's start by importing the necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from ax.service.ax_client import AxClient, ObjectiveProperties\n", + "from ax.utils.notebook.plotting import render\n", + "from ax.service.utils.report_utils import exp_to_df\n", + "from submitit import AutoExecutor, LocalJob, DebugJob" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Defining the Function to Optimize\n", + "We'll define a simple function to optimize. This function takes two parameters, and returns a single metric." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate(parameters):\n", + " x = parameters[\"x\"]\n", + " y = parameters[\"y\"]\n", + " return {\"result\": (x - 3)**2 + (y - 4)**2}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: SubmitIt's [CommandFunction](https://github.com/facebookincubator/submitit/blob/main/docs/examples.md#working-with-commands) allows you to define commands to run on the node and then redirects the standard output.\n", + "\n", + "## Setting up Ax\n", + "We'll use Ax's Service API for this example. We start by initializing an AxClient and creating an experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 01-11 17:57:00] 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 6 decimal points.\n", + "[INFO 01-11 17:57:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 01-11 17:57:00] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter y. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n", + "[INFO 01-11 17:57:00] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x', parameter_type=FLOAT, range=[-10.0, 10.0]), RangeParameter(name='y', parameter_type=FLOAT, range=[-10.0, 10.0])], parameter_constraints=[ParameterConstraint(1.0*x + 1.0*y <= 2.0)]).\n", + "[INFO 01-11 17:57:00] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there are more ordered parameters than there are categories for the unordered categorical parameters.\n", + "[INFO 01-11 17:57:00] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False\n", + "[INFO 01-11 17:57:00] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5\n", + "[INFO 01-11 17:57:00] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5\n", + "[INFO 01-11 17:57:00] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n", + "[INFO 01-11 17:57:00] 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.\n" + ] + } + ], + "source": [ + "ax_client = AxClient()\n", + "ax_client.create_experiment(\n", + " name=\"my_experiment\",\n", + " parameters=[\n", + " {\"name\": \"x\", \"type\": \"range\", \"bounds\": [-10.0, 10.0]},\n", + " {\"name\": \"y\", \"type\": \"range\", \"bounds\": [-10.0, 10.0]},\n", + " ],\n", + " objectives={\"result\": ObjectiveProperties(minimize=True)},\n", + " parameter_constraints=[\"x + y <= 2.0\"], # Optional.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other commonly used [parameters types](https://ax.dev/docs/glossary.html#parameter) include `choice` parameters and `fixed` parameters. \n", + "\n", + "Tip 1: you can specify additional information for parameters such as `log_scale`, if a parameter operates at a log-scale and `is_ordered` for choice parameters that have a meaningful ordering.\n", + "\n", + "Tip 2: Ax is an excellent choice for multi-objective optimization problems when there are multiple competing objectives and the goal is to find all Pareto-optimal solutions.\n", + "\n", + "Tip 3: One can define constraints on both the parameters and the outcome.\n", + "\n", + "## Setting up SubmitIt\n", + "We'll use SubmitIt's `AutoExecutor` for this example. We start by initializing an `AutoExecutor`, and setting a few commonly used parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Log folder and cluster. Specify cluster='local' or cluster='debug' to run the jobs locally during development.\n", + "# When we're are ready for deployment, switch to cluster='slurm' \n", + "executor = AutoExecutor(folder=\"/tmp/submitit_runs\", cluster='debug') \n", + "executor.update_parameters(timeout_min=60) # Timeout of the slurm job. Not including slurm scheduling delay.\n", + "executor.update_parameters(cpus_per_task=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other commonly used Slurm parameters include `partition`, `ntasks_per_node`, `cpus_per_task`, `cpus_per_gpu`, `gpus_per_node`, `gpus_per_task`, `qos`, `mem`, `mem_per_gpu`, `mem_per_cpu`, `account`.\n", + "\n", + "## Running the Optimization Loop\n", + "Now, we're ready to run the optimization loop. We'll use an ask-tell loop, where we ask Ax for a suggestion, evaluate it using our function, and then tell Ax the result.\n", + "\n", + "The example loop schedules new jobs whenever there is availability. For tasks that take a similar amount of time regardless of the parameters, it may make more sense to wait for the whole batch to finish before scheduling the next (so ax can make better informed parameter choices).\n", + "\n", + "Note that `get_next_trials` may not use all available `num_parallel_jobs` if it doesn't have good parameter candidates to run." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 01-11 17:57:00] ax.service.ax_client: Generated new trial 0 with parameters {'x': -1.756784, 'y': -4.021679}.\n", + "[INFO 01-11 17:57:00] ax.service.ax_client: Generated new trial 1 with parameters {'x': -9.300127, 'y': -4.654682}.\n", + "[INFO 01-11 17:57:00] ax.service.ax_client: Generated new trial 2 with parameters {'x': 4.881288, 'y': -7.929573}.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING 01-11 17:57:03] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n", + "[INFO 01-11 17:57:03] ax.service.utils.report_utils: No results present for the specified metrics `[Metric('result')]`. Returning arm parameters and metadata only.\n" + ] + }, + { + "data": { + "text/html": [ + "
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trial_indexarm_nametrial_statusgeneration_methodxy
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" + ], + "text/plain": [ + " trial_index arm_name trial_status generation_method x y\n", + "0 0 0_0 RUNNING Sobol -1.756784 -4.021679\n", + "1 1 1_0 RUNNING Sobol -9.300127 -4.654682\n", + "2 2 2_0 RUNNING Sobol 4.881288 -7.929573" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 01-11 17:57:33] ax.service.ax_client: Completed trial 0 with data: {'result': (86.974325, None)}.\n", + "[INFO 01-11 17:57:33] ax.service.ax_client: Completed trial 1 with data: {'result': (226.196643, None)}.\n", + "[INFO 01-11 17:57:33] ax.service.ax_client: Completed trial 2 with data: {'result': (145.853961, None)}.\n", + "[INFO 01-11 17:57:33] ax.service.ax_client: Generated new trial 3 with parameters {'x': 2.752141, 'y': -8.223596}.\n", + "/private/home/marton/miniconda3/envs/axenv/lib/python3.10/site-packages/ax/core/data.py:284: FutureWarning:\n", + "\n", + "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "\n", + "[INFO 01-11 17:57:33] ax.service.ax_client: Generated new trial 4 with parameters {'x': 9.275037, 'y': -7.347285}.\n", + "/private/home/marton/miniconda3/envs/axenv/lib/python3.10/site-packages/ax/core/data.py:284: FutureWarning:\n", + "\n", + "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + "\n", + "[WARNING 01-11 17:57:35] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" + ] + }, + { + "data": { + "text/html": [ + "
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trial_indexarm_nametrial_statusgeneration_methodresultxy
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trial_indexarm_nametrial_statusgeneration_methodresultxy
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trial_indexarm_nametrial_statusgeneration_methodresultxy
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555_0COMPLETEDBoTorch34.9522990.590279-1.398661
666_0COMPLETEDBoTorch32.899584-2.2484771.686329
777_0COMPLETEDBoTorch60.5253761.439472-3.621688
888_0RUNNINGBoTorchNaN0.3087291.691271
999_0RUNNINGBoTorchNaN0.3043001.695700
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" + ], + "text/plain": [ + " trial_index arm_name trial_status generation_method result x \\\n", + "0 0 0_0 COMPLETED Sobol 86.974325 -1.756784 \n", + "1 1 1_0 COMPLETED Sobol 226.196643 -9.300127 \n", + "2 2 2_0 COMPLETED Sobol 145.853961 4.881288 \n", + "3 3 3_0 COMPLETED Sobol 149.477736 2.752141 \n", + "4 4 4_0 COMPLETED Sobol 168.136982 9.275037 \n", + "5 5 5_0 COMPLETED BoTorch 34.952299 0.590279 \n", + "6 6 6_0 COMPLETED BoTorch 32.899584 -2.248477 \n", + "7 7 7_0 COMPLETED BoTorch 60.525376 1.439472 \n", + "8 8 8_0 RUNNING BoTorch NaN 0.308729 \n", + "9 9 9_0 RUNNING BoTorch NaN 0.304300 \n", + "\n", + " y \n", + "0 -4.021679 \n", + "1 -4.654682 \n", + "2 -7.929573 \n", + "3 -8.223596 \n", + "4 -7.347285 \n", + "5 -1.398661 \n", + "6 1.686329 \n", + "7 -3.621688 \n", + "8 1.691271 \n", + "9 1.695700 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 01-11 17:59:41] ax.service.ax_client: Completed trial 8 with data: {'result': (12.573169, None)}.\n", + "[INFO 01-11 17:59:41] ax.service.ax_client: Completed trial 9 with data: {'result': (12.576597, None)}.\n", + "[WARNING 01-11 17:59:41] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n" + ] + }, + { + "data": { + "text/html": [ + "
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trial_indexarm_nametrial_statusgeneration_methodresultxy
000_0COMPLETEDSobol86.974325-1.756784-4.021679
111_0COMPLETEDSobol226.196643-9.300127-4.654682
222_0COMPLETEDSobol145.8539614.881288-7.929573
333_0COMPLETEDSobol149.4777362.752141-8.223596
444_0COMPLETEDSobol168.1369829.275037-7.347285
555_0COMPLETEDBoTorch34.9522990.590279-1.398661
666_0COMPLETEDBoTorch32.899584-2.2484771.686329
777_0COMPLETEDBoTorch60.5253761.439472-3.621688
888_0COMPLETEDBoTorch12.5731690.3087291.691271
999_0COMPLETEDBoTorch12.5765970.3043001.695700
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" + ], + "text/plain": [ + " trial_index arm_name trial_status generation_method result x \\\n", + "0 0 0_0 COMPLETED Sobol 86.974325 -1.756784 \n", + "1 1 1_0 COMPLETED Sobol 226.196643 -9.300127 \n", + "2 2 2_0 COMPLETED Sobol 145.853961 4.881288 \n", + "3 3 3_0 COMPLETED Sobol 149.477736 2.752141 \n", + "4 4 4_0 COMPLETED Sobol 168.136982 9.275037 \n", + "5 5 5_0 COMPLETED BoTorch 34.952299 0.590279 \n", + "6 6 6_0 COMPLETED BoTorch 32.899584 -2.248477 \n", + "7 7 7_0 COMPLETED BoTorch 60.525376 1.439472 \n", + "8 8 8_0 COMPLETED BoTorch 12.573169 0.308729 \n", + "9 9 9_0 COMPLETED BoTorch 12.576597 0.304300 \n", + "\n", + " y \n", + "0 -4.021679 \n", + "1 -4.654682 \n", + "2 -7.929573 \n", + "3 -8.223596 \n", + "4 -7.347285 \n", + "5 -1.398661 \n", + "6 1.686329 \n", + "7 -3.621688 \n", + "8 1.691271 \n", + "9 1.695700 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "total_budget = 10\n", + "num_parallel_jobs = 3\n", + "\n", + "jobs = []\n", + "submitted_jobs = 0\n", + "# Run until all the jobs have finished and our budget is used up.\n", + "while submitted_jobs < total_budget or jobs:\n", + " for job, trial_index in jobs[:]:\n", + " # Poll if any jobs completed\n", + " # Local and debug jobs don't run until .result() is called.\n", + " if job.done() or type(job) in [LocalJob, DebugJob]:\n", + " result = job.result()\n", + " ax_client.complete_trial(trial_index=trial_index, raw_data=result)\n", + " jobs.remove((job, trial_index))\n", + " \n", + " # Schedule new jobs if there is availablity\n", + " trial_index_to_param, _ = ax_client.get_next_trials(\n", + " max_trials=min(num_parallel_jobs - len(jobs), total_budget - submitted_jobs))\n", + " for trial_index, parameters in trial_index_to_param.items():\n", + " job = executor.submit(evaluate, parameters)\n", + " submitted_jobs += 1\n", + " jobs.append((job, trial_index))\n", + " time.sleep(1)\n", + " \n", + " # Display the current trials.\n", + " display(exp_to_df(ax_client.experiment))\n", + "\n", + " # Sleep for a bit before checking the jobs again to avoid overloading the cluster. \n", + " # If you have a large number of jobs, consider adding a sleep statement in the job polling loop aswell.\n", + " time.sleep(30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Finally\n", + "\n", + "We can retrieve the best parameters and render the response surface." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 01-11 18:00:11] ax.service.ax_client: Retrieving contour plot with parameter 'x' on X-axis and 'y' on Y-axis, for metric 'result'. Remaining parameters are affixed to the middle of their range.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best set of parameters: {'x': -2.2484768683250875, 'y': 1.6863286966529074}\n", + "Mean objective value: {'result': 32.90128530853501}\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": false + }, + "data": [ + { + "autocolorscale": false, + "autocontour": true, + "colorbar": { + "tickfont": { + "size": 8 + }, + "ticksuffix": "", + "x": 0.45, + "y": 0.5 + }, + "colorscale": [ + [ + 0, + "rgb(247,252,253)" + ], + [ + 0.125, + "rgb(229,245,249)" + ], + [ + 0.25, + "rgb(204,236,230)" + ], + [ + 0.375, + "rgb(153,216,201)" + ], + [ + 0.5, + "rgb(102,194,164)" + ], + [ + 0.625, + "rgb(65,174,118)" + ], + [ + 0.75, + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "best_parameters, (means, covariances) = ax_client.get_best_parameters()\n", + "print(f'Best set of parameters: {best_parameters}')\n", + "print(f'Mean objective value: {means}')\n", + "# The covariance is only meaningful when multiple objectives are present.\n", + "\n", + "render(ax_client.get_contour_plot())\n" + ] + } + ], + "metadata": { + "custom": { + "cells": [], + "metadata": { + "fileHeader": "", + "fileUid": "c231ca1b-265b-4672-8a47-7d7ec1b0357b", + "isAdHoc": false, + "kernelspec": { + "display_name": "axenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 + }, + "indentAmount": 2 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/website/pages/tutorials/index.js b/website/pages/tutorials/index.js index 52a1b8ad737..618740522f0 100644 --- a/website/pages/tutorials/index.js +++ b/website/pages/tutorials/index.js @@ -100,6 +100,15 @@ class TutorialHome extends React.Component { Ax and integration with an external ML library. +