mirror of
https://github.com/vale981/ray
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cd5218f831
Replaces #24225 and adds example navigation Signed-off-by: Max Pumperla <max.pumperla@googlemail.com>
296 lines
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8.9 KiB
Text
296 lines
No EOL
8.9 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0332db08",
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"metadata": {},
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"source": [
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"# Running Tune experiments with Dragonfly\n",
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"\n",
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"In this tutorial we introduce Dragonfly, while running a simple Ray Tune experiment.\n",
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"Tune’s Search Algorithms integrate with Dragonfly and, as a result,\n",
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"allow you to seamlessly scale up a Dragonfly optimization process - without\n",
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"sacrificing performance.\n",
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"\n",
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"Dragonfly is an open source python library for scalable Bayesian optimization.\n",
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"Bayesian optimization is used optimizing black-box functions whose evaluations\n",
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"are usually expensive. Beyond vanilla optimization techniques,\n",
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"Dragonfly provides an array of tools to scale up Bayesian optimization to expensive\n",
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"large scale problems. These include features that are especially suited for high\n",
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"dimensional spaces (optimizing with a large number of variables), parallel evaluations\n",
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"in synchronous or asynchronous settings (conducting multiple evaluations in parallel),\n",
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"multi-fidelity optimization (using cheap approximations to speed up the optimization\n",
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"process), and multi-objective optimization (optimizing multiple functions\n",
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"simultaneously).\n",
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"\n",
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"Bayesian optimization does not rely on the gradient of the objective function,\n",
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"but instead, learns from samples of the search space. It is suitable for optimizing\n",
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"functions that are non-differentiable, with many local minima, or even unknown but only\n",
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"testable. Therefore, it belongs to the domain of \"derivative-free optimization\" and\n",
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"\"black-box optimization\". In this example we minimize a simple objective to briefly\n",
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"demonstrate the usage of Dragonfly with Ray Tune via `DragonflySearch`. It's useful\n",
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"to keep in mind that despite the emphasis on machine learning experiments,\n",
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"Ray Tune optimizes any implicit or explicit objective. Here we assume\n",
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"`dragonfly-opt==0.1.6` library is installed. To learn more, please refer to\n",
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"the [Dragonfly website](https://dragonfly-opt.readthedocs.io/)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9878e2bd",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"outputs": [],
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"source": [
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"# !pip install ray[tune]\n",
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"!pip install dragonfly-opt==0.1.6"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3acad068",
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"metadata": {},
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"source": [
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"Click below to see all the imports we need for this example.\n",
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"You can also launch directly into a Binder instance to run this notebook yourself.\n",
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"Just click on the rocket symbol at the top of the navigation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4ea7aefa",
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"metadata": {
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"tags": [
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"hide-input"
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]
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import time\n",
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"\n",
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"import ray\n",
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"from ray import tune\n",
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"from ray.tune.suggest import ConcurrencyLimiter\n",
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"from ray.tune.suggest.dragonfly import DragonflySearch"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dc67fc3f",
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"metadata": {},
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"source": [
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"Let's start by defining a optimization problem.\n",
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"Suppose we want to figure out the proportions of water and several salts to add to an\n",
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"ionic solution with the goal of maximizing it's ability to conduct electricity.\n",
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"The objective here is explicit for demonstration, yet in practice they often come\n",
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"out of a black-box (e.g. a physical device measuring conductivity, or reporting the\n",
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"results of a long-running ML experiment). We artificially sleep for a bit\n",
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"(`0.02` seconds) to simulate a more typical experiment. This setup assumes that we're\n",
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"running multiple `step`s of an experiment and try to tune relative proportions of\n",
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"4 ingredients-- these proportions should be considered as hyperparameters.\n",
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"Our `objective` function will take a Tune `config`, evaluates the `conductivity` of\n",
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"our experiment in a training loop,\n",
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"and uses `tune.report` to report the `conductivity` back to Tune."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f0d72404",
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"metadata": {},
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"outputs": [],
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"source": [
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"def objective(config):\n",
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" \"\"\"\n",
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" Simplistic model of electrical conductivity with added Gaussian\n",
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" noise to simulate experimental noise.\n",
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" \"\"\"\n",
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" for i in range(config[\"iterations\"]):\n",
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" vol1 = config[\"LiNO3_vol\"] # LiNO3\n",
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" vol2 = config[\"Li2SO4_vol\"] # Li2SO4\n",
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" vol3 = config[\"NaClO4_vol\"] # NaClO4\n",
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" vol4 = 10 - (vol1 + vol2 + vol3) # Water\n",
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" conductivity = vol1 + 0.1 * (vol2 + vol3) ** 2 + 2.3 * vol4 * (vol1 ** 1.5)\n",
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" conductivity += np.random.normal() * 0.01\n",
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" tune.report(timesteps_total=i, objective=conductivity)\n",
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" time.sleep(0.02)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1808e8e0",
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"metadata": {},
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"source": [
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"Next we define a search space. The critical assumption is that the optimal\n",
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"hyperparameters live within this space. Yet, if the space is very large, then those\n",
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"hyperparameters may be difficult to find in a short amount of time."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6b867a19",
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"metadata": {},
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"outputs": [],
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"source": [
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"search_space = {\n",
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" \"iterations\": 100,\n",
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" \"LiNO3_vol\": tune.uniform(0, 7),\n",
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" \"Li2SO4_vol\": tune.uniform(0, 7),\n",
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" \"NaClO4_vol\": tune.uniform(0, 7)\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fe16177",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"outputs": [],
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"source": [
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"ray.init(configure_logging=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ed5fc098",
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"metadata": {},
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"source": [
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"Now we define the search algorithm from `DragonflySearch` with `optimizer` and\n",
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"`domain` arguments specified in a common way. We also use `ConcurrencyLimiter`\n",
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"to constrain to 4 concurrent trials."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a8075e34",
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"metadata": {},
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"outputs": [],
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"source": [
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"algo = DragonflySearch(\n",
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" optimizer=\"bandit\",\n",
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" domain=\"euclidean\",\n",
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")\n",
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"algo = ConcurrencyLimiter(algo, max_concurrent=4)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "82240170",
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"metadata": {},
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"source": [
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"The number of samples is the number of hyperparameter combinations that will be\n",
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"tried out. This Tune run is set to `1000` samples.\n",
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"(you can decrease this if it takes too long on your machine)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c437c1e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"num_samples = 100"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "70192183",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"outputs": [],
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"source": [
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"# Reducing samples for smoke tests\n",
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"num_samples = 10"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a823fb1",
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"metadata": {},
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"source": [
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"Finally, we run the experiment to `min`imize the `mean_loss` of the `objective` by\n",
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"searching `search_config` via `algo`, `num_samples` times. This previous sentence is\n",
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"fully characterizes the search problem we aim to solve. With this in mind,\n",
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"notice how efficient it is to execute `tune.run()`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "97c69cbe",
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"metadata": {},
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"outputs": [],
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"source": [
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"analysis = tune.run(\n",
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" objective,\n",
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" metric=\"objective\",\n",
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" mode=\"max\",\n",
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" name=\"dragonfly_search\",\n",
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" search_alg=algo,\n",
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" num_samples=num_samples,\n",
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" config=search_space\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fb1a7563",
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"metadata": {},
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"source": [
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"Below are the recommended relative proportions of water and each salt found to\n",
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"maximize conductivity in the ionic solution (according to the simple model):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ccb3b44b",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Best hyperparameters found: \", analysis.best_config)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "42eca06e",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"outputs": [],
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"source": [
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"ray.shutdown()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"orphan": true
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |