"In this tutorial we introduce Ax, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with Ax and, as a result, allow you to seamlessly scale up a Ax optimization process - without sacrificing performance.\n",
"\n",
"Ax is a platform for optimizing any kind of experiment, including machine learning experiments, A/B tests, and simulations. Ax can optimize discrete configurations (e.g., variants of an A/B test) using multi-armed bandit optimization, and continuous/ordered configurations (e.g. float/int parameters) using Bayesian optimization. Results of A/B tests and simulations with reinforcement learning agents often exhibit high amounts of noise. Ax supports state-of-the-art algorithms which work better than traditional Bayesian optimization in high-noise settings. Ax also supports multi-objective and constrained optimization which are common to real-world problems (e.g. improving load time without increasing data use). Ax belongs to the domain of \"derivative-free\" and \"black-box\" optimization.\n",
"\n",
"In this example we minimize a simple objective to briefly demonstrate the usage of AxSearch with Ray Tune via `AxSearch`. It's useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume `ax-platform==0.2.4` library is installed withe python version >= 3.7. To learn more, please refer to the [Ax website](https://ax.dev/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "297d8b18",
"metadata": {
"tags": [
"remove-cell"
]
},
"outputs": [],
"source": [
"# !pip install ray[tune]\n",
"!pip install ax-platform==0.2.4"
]
},
{
"cell_type": "markdown",
"id": "59b1e0d1",
"metadata": {},
"source": [
"Click below to see all the imports we need for this example.\n",
"You can also launch directly into a Binder instance to run this notebook yourself.\n",
"Just click on the rocket symbol at the top of the navigation."
"Next we define a search space. The critical assumption is that the optimal hyperparamters live within this space. Yet, if the space is very large, then those hyperparamters may be difficult to find in a short amount of time."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "30f75f5a",
"metadata": {},
"outputs": [],
"source": [
"search_space = {\n",
" \"iterations\":100,\n",
" \"x1\": tune.uniform(0.0, 1.0),\n",
" \"x2\": tune.uniform(0.0, 1.0),\n",
" \"x3\": tune.uniform(0.0, 1.0),\n",
" \"x4\": tune.uniform(0.0, 1.0),\n",
" \"x5\": tune.uniform(0.0, 1.0),\n",
" \"x6\": tune.uniform(0.0, 1.0)\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "106d8578",
"metadata": {
"tags": [
"remove-cell"
]
},
"outputs": [],
"source": [
"ray.init(configure_logging=False)"
]
},
{
"cell_type": "markdown",
"id": "932f74e6",
"metadata": {},
"source": [
"Now we define the search algorithm from `AxSearch`. If you want to constrain your parameters or even the space of outcomes, that can be easily done by passing the argumentsas below."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "34dd5c95",
"metadata": {},
"outputs": [],
"source": [
"algo = AxSearch(\n",
" parameter_constraints=[\"x1 + x2 <= 2.0\"],\n",
" outcome_constraints=[\"l2norm <= 1.25\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "f6d18a99",
"metadata": {},
"source": [
"We also use `ConcurrencyLimiter` to constrain to 4 concurrent trials. "
"The number of samples is the number of hyperparameter combinations that will be tried out. This Tune run is set to `1000` samples.\n",
"You can decrease this if it takes too long on your machine, or you can set a time limit easily through `stop` argument in `tune.run()` as we will show here."
"Finally, we run the experiment to find the global minimum of the provided landscape (which contains 5 false minima). The argument to metric, `\"landscape\"`, is provided via the `objective` function's `session.report`. The experiment `\"min\"`imizes the \"mean_loss\" of the `landscape` by searching within `search_space` via `algo`, `num_samples` times or when `\"timesteps_total\": stop_timesteps`. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, notice how efficient it is to execute `tune.run()`."
