"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/)."
]
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"# !pip install ray[tune]\n",
"!pip install ax-platform==0.2.4"
]
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"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."
"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."
"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()`."
