ray/doc/source/tune/examples/nevergrad_example.ipynb

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    "# Running Tune experiments with Nevergrad\n",
    "\n",
    "In this tutorial we introduce Nevergrad, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with Nevergrad and, as a result, allow you to seamlessly scale up a Nevergrad optimization process - without sacrificing performance.\n",
    "\n",
    "Nevergrad provides gradient/derivative-free optimization able to handle noise over the objective landscape, including evolutionary, bandit, and Bayesian optimization algorithms. Nevergrad internally supports search spaces which are continuous, discrete or a mixture of thereof. It also provides a library of functions on which to test the optimization algorithms and compare with other benchmarks.\n",
    "\n",
    "In this example we minimize a simple objective to briefly demonstrate the usage of Nevergrad with Ray Tune via `NevergradSearch`. 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 `nevergrad==0.4.3.post7` library is installed. To learn more, please refer to [Nevergrad website](https://github.com/facebookresearch/nevergrad)."
   ]
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   "source": [
    "# !pip install ray[tune]\n",
    "!pip install nevergrad==0.4.3.post7 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66cb8206",
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   "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."
   ]
  },
  {
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   "source": [
    "import time\n",
    "\n",
    "import ray\n",
    "import nevergrad as ng\n",
    "from ray import tune\n",
    "from ray.tune.suggest import ConcurrencyLimiter\n",
    "from ray.tune.suggest.nevergrad import NevergradSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41f2c881",
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   "source": [
    "Let's start by defining a simple evaluation function.\n",
    "We artificially sleep for a bit (`0.1` seconds) to simulate a long-running ML experiment.\n",
    "This setup assumes that we're running multiple `step`s of an experiment and try to tune two hyperparameters,\n",
    "namely `width` and `height`, and `activation`."
   ]
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  {
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   "source": [
    "def evaluate(step, width, height, activation):\n",
    "    time.sleep(0.1)\n",
    "    activation_boost = 10 if activation==\"relu\" else 1\n",
    "    return (0.1 + width * step / 100) ** (-1) + height * 0.1 + activation_boost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f060ea83",
   "metadata": {},
   "source": [
    "Next, our `objective` function takes a Tune `config`, evaluates the `score` of your experiment in a training loop,\n",
    "and uses `tune.report` to report the `score` back to Tune."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "def objective(config):\n",
    "    for step in range(config[\"steps\"]):\n",
    "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
    "        tune.report(iterations=step, mean_loss=score)"
   ]
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   "source": [
    "ray.init(configure_logging=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7a4b94",
   "metadata": {},
   "source": [
    "Now we construct the hyperparameter search space using `ConfigSpace`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2405373",
   "metadata": {},
   "source": [
    "Next we define the search algorithm built from `NevergradSearch`, constrained  to a maximum of `4` concurrent trials with a `ConcurrencyLimiter`. Here we use `ng.optimizers.OnePlusOne`, a simple evolutionary algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f099b674",
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   "source": [
    "algo = NevergradSearch(\n",
    "    optimizer=ng.optimizers.OnePlusOne,\n",
    ")\n",
    "algo = tune.suggest.ConcurrencyLimiter(algo, max_concurrent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bddc4e5",
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   "source": [
    "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)."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "num_samples = 1000"
   ]
  },
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   "source": [
    "# If 1000 samples take too long, you can reduce this number.\n",
    "# We override this number here for our smoke tests.\n",
    "num_samples = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3956381",
   "metadata": {},
   "source": [
    "Finally, all that's left is to define a search space."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "search_config = {\n",
    "    \"steps\": 100,\n",
    "    \"width\": tune.uniform(0, 20),\n",
    "    \"height\": tune.uniform(-100, 100),\n",
    "    \"activation\": tune.choice([\"relu, tanh\"])\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9d051f",
   "metadata": {},
   "source": [
    "Finally, we run the experiment to `\"min\"`imize the \"mean_loss\" of the `objective` by searching `search_space` via `algo`, `num_samples` times. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, observe how efficient it is to execute `tune.run()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "769f4368",
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   "source": [
    "analysis = tune.run(\n",
    "    objective,\n",
    "    search_alg=algo,\n",
    "    metric=\"mean_loss\",\n",
    "    mode=\"min\",\n",
    "    name=\"nevergrad_exp\",\n",
    "    num_samples=num_samples,\n",
    "    config=search_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950003be",
   "metadata": {},
   "source": [
    "Here are the hyperparamters found to minimize the mean loss of the defined objective."
   ]
  },
  {
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   "source": [
    "print(\"Best hyperparameters found were: \", analysis.best_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3824ae",
   "metadata": {},
   "source": [
    "## Optional: passing the (hyper)parameter space into the search algorithm\n",
    "\n",
    "We can also pass the search space into `NevergradSearch` using their designed format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89ae7455",
   "metadata": {},
   "outputs": [],
   "source": [
    "space = ng.p.Dict(\n",
    "    width=ng.p.Scalar(lower=0, upper=20),\n",
    "    height=ng.p.Scalar(lower=-100, upper=100),\n",
    "    activation=ng.p.Choice(choices=[\"relu\", \"tanh\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e52eeab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "algo = NevergradSearch(\n",
    "    optimizer=ng.optimizers.OnePlusOne,\n",
    "    space=space,\n",
    "    metric=\"mean_loss\",\n",
    "    mode=\"min\"\n",
    ")\n",
    "algo = tune.suggest.ConcurrencyLimiter(algo, max_concurrent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8926177",
   "metadata": {},
   "source": [
    "Again we run the experiment, this time with a less passed via the `config` and instead passed through `search_alg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64f39800",
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = tune.run(\n",
    "    objective,\n",
    "    search_alg=algo,\n",
    "#    metric=\"mean_loss\",\n",
    "#    mode=\"min\",\n",
    "    name=\"nevergrad_exp\",\n",
    "    num_samples=num_samples,\n",
    "    config={\"steps\": 100},\n",
    ")"
   ]
  },
  {
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   "id": "0478c1ea",
   "metadata": {
    "tags": [
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   "outputs": [],
   "source": [
    "ray.shutdown()"
   ]
  }
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