{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "oTuvowvgFQpm" }, "source": [ "# CleanRL's Huggingface Integration Demo\n", "\n", "\n", "\n", "[](https://github.com/vwxyzjn/cleanrl)\n", "[![tests](https://github.com/vwxyzjn/cleanrl/actions/workflows/tests.yaml/badge.svg)](https://github.com/vwxyzjn/cleanrl/actions/workflows/tests.yaml)\n", "[![docs](https://img.shields.io/github/deployments/vwxyzjn/cleanrl/Production?label=docs&logo=vercel)](https://docs.cleanrl.dev/)\n", "[](https://discord.gg/D6RCjA6sVT)\n", "[](https://www.youtube.com/channel/UCDdC6BIFRI0jvcwuhi3aI6w/videos)\n", "[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n", "[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)\n", "[](https://huggingface.co/cleanrl)\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vwxyzjn/cleanrl/blob/master/docs/get-started/CleanRL_Huggingface_Integration_Demo.ipynb)\n", "\n", "\n", "CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. It now has has ๐Ÿงช experimental support for saving and loading models from ๐Ÿค— HuggingFace's [Model Hub](https://huggingface.co/models). This notebook is a preliminary demo.\n", "\n", "\n", "* ๐Ÿ’พ [GitHub Repo](https://github.com/vwxyzjn/cleanrl)\n", "* ๐Ÿ“œ [Documentation](https://docs.cleanrl.dev/)\n", "* ๐Ÿค— [HuggingFace Model Hub](https://huggingface.co/cleanrl)\n", "* ๐Ÿ”— [Open RL Benchmark reports](https://wandb.ai/openrlbenchmark/openrlbenchmark/reportlist)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "J0zhqyfea0If" }, "source": [ "## Get Started\n", "\n", "CleanRL can be installed via `pip`. Let's say we are interested in pulling the model for [`dqn_atari_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari_jax.py), we can install the algorithm-variant-specific dependencies as follows:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lhnJkrYLOvcs", "outputId": "381d9d0d-7e83-4f21-ef89-91d4e3b93c18" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting cleanrl[dqn-atari-jax]\n", " Downloading cleanrl-1.1.2-py3-none-any.whl (16.9 MB)\n", "\u001b[K |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 16.9 MB 241 kB/s \n", "\u001b[?25hCollecting pygame==2.1.0\n", " Downloading pygame-2.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n", "\u001b[K 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sha256=57226a75b752bf852ac2f0f5ad878217a63376d6c44a4b29ccdf40b4921bf4bc\n", " Stored in directory: /root/.cache/pip/wheels/4c/8e/7e/72fbc243e1aeecae64a96875432e70d4e92f3d2d18123be004\n", "Successfully built gym moviepy AutoROM.accept-rom-license pathtools\n", "Installing collected packages: smmap, gitdb, tensorstore, shortuuid, setproctitle, sentry-sdk, proglog, pathtools, libtorrent, imageio-ffmpeg, gymnasium-notices, gym, GitPython, docker-pycreds, commonmark, chex, cached-property, wandb, tensorboard, stable-baselines3, rich, pygame, orbax, optax, moviepy, huggingface-hub, gymnasium, AutoROM.accept-rom-license, AutoROM, flax, cleanrl-test, ale-py\n", " Attempting uninstall: gym\n", " Found existing installation: gym 0.25.2\n", " Uninstalling gym-0.25.2:\n", " Successfully uninstalled gym-0.25.2\n", " Attempting uninstall: tensorboard\n", " Found existing installation: tensorboard 2.9.1\n", " Uninstalling tensorboard-2.9.1:\n", " Successfully uninstalled tensorboard-2.9.1\n", " Attempting uninstall: moviepy\n", " Found existing installation: moviepy 0.2.3.5\n", " Uninstalling moviepy-0.2.3.5:\n", " Successfully uninstalled moviepy-0.2.3.5\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "tensorflow 2.9.2 requires tensorboard<2.10,>=2.9, but you have tensorboard 2.11.0 which is incompatible.\u001b[0m\n", "Successfully installed AutoROM-0.4.2 AutoROM.accept-rom-license-0.5.0 GitPython-3.1.30 ale-py-0.7.4 cached-property-1.5.2 chex-0.1.5 cleanrl-test-1.1.2 commonmark-0.9.1 docker-pycreds-0.4.0 flax-0.6.3 gitdb-4.0.10 gym-0.23.1 gymnasium-0.26.3 gymnasium-notices-0.0.1 huggingface-hub-0.11.1 imageio-ffmpeg-0.4.7 libtorrent-2.0.7 moviepy-1.0.3 optax-0.1.4 orbax-0.0.23 pathtools-0.1.2 proglog-0.1.10 pygame-2.1.0 rich-13.0.0 sentry-sdk-1.9.0 setproctitle-1.3.2 shortuuid-1.0.11 smmap-5.0.0 stable-baselines3-1.2.0 tensorboard-2.11.0 tensorstore-0.1.28 wandb-0.13.7\n" ] } ], "source": [ "!pip install --upgrade \"cleanrl[dqn-atari-jax]\" # CAVEAT: the extra key is `dqn-atari-jax` with dashes instead of `dqn_atari_jax` with underscores" ] }, { "cell_type": "markdown", "metadata": { "id": "xXQXZTh_AHZ0" }, "source": [ "## Enjoy Utility\n", "\n", "We have a simple way to load the model by running our \"enjoy\" utility, which automatically pull the model from ๐Ÿค— HuggingFace and run for a few episodes. It also produces a rendered video through the `--capture_video` flag. See more at our [๐Ÿ“œ Documentation](https://docs.cleanrl.dev/get-started/zoo/)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4H9VZKBC_3_1", "outputId": "fc03fd9b-84f8-43dc-b4e3-041e7a201c12" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/jupyter_client/connect.py:28: DeprecationWarning: Jupyter is migrating its paths to use standard platformdirs\n", "given by the platformdirs library. To remove this warning and\n", "see the appropriate new directories, set the environment variable\n", "`JUPYTER_PLATFORM_DIRS=1` and then run `jupyter --paths`.\n", "The use of platformdirs will be the default in `jupyter_core` v6\n", " from jupyter_core.paths import jupyter_data_dir, jupyter_runtime_dir, secure_write\n", "loading saved models from cleanrl/BreakoutNoFrameskip-v4-dqn_atari_jax-seed1...\n", "Downloading: 100% 6.75M/6.75M [00:00<00:00, 62.6MB/s]\n", "A.L.E: Arcade Learning Environment (version 0.7.4+069f8bd)\n", "[Powered by Stella]\n", "/usr/local/lib/python3.8/dist-packages/gym/utils/seeding.py:138: DeprecationWarning: \u001b[33mWARN: Function `hash_seed(seed, max_bytes)` is marked as deprecated and will be removed in the future. \u001b[0m\n", " deprecation(\n", "/usr/local/lib/python3.8/dist-packages/gym/utils/seeding.py:175: DeprecationWarning: \u001b[33mWARN: Function `_bigint_from_bytes(bytes)` is marked as deprecated and will be removed in the future. \u001b[0m\n", " deprecation(\n", "/usr/local/lib/python3.8/dist-packages/gym/wrappers/monitoring/video_recorder.py:43: DeprecationWarning: \u001b[33mWARN: `env.metadata[\"render.modes\"] is marked as deprecated and will be replaced with `env.metadata[\"render_modes\"]` see https://github.com/openai/gym/pull/2654 for more details\u001b[0m\n", " logger.deprecation(\n", "/usr/local/lib/python3.8/dist-packages/gym/utils/seeding.py:47: DeprecationWarning: \u001b[33mWARN: Function `rng.randint(low, [high, size, dtype])` is marked as deprecated and will be removed in the future. Please use `rng.integers(low, [high, size, dtype])` instead.\u001b[0m\n", " deprecation(\n", "/usr/local/lib/python3.8/dist-packages/gym/wrappers/monitoring/video_recorder.py:43: DeprecationWarning: \u001b[33mWARN: `env.metadata[\"render.modes\"] is marked as deprecated and will be replaced with `env.metadata[\"render_modes\"]` see https://github.com/openai/gym/pull/2654 for more details\u001b[0m\n", " logger.deprecation(\n", "/usr/local/lib/python3.8/dist-packages/gym/utils/seeding.py:47: DeprecationWarning: \u001b[33mWARN: Function `rng.randint(low, [high, size, dtype])` is marked as deprecated and will be removed in the future. Please use `rng.integers(low, [high, size, dtype])` instead.\u001b[0m\n", " deprecation(\n", "eval_episode=0, episodic_return=400.0\n", "eval_episode=1, episodic_return=128.0\n" ] } ], "source": [ "!python -m cleanrl_utils.enjoy --exp-name dqn_atari_jax --env-id BreakoutNoFrameskip-v4 --eval-episodes 2 --capture_video" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 231 }, "id": "KpzdA4dkFbdT", "outputId": "1b53628e-ac19-4f36-89e4-1a831b51f06b" }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Video\n", "Video('videos/eval/rl-video-episode-0.mp4', embed=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "WU29XP1ICwxv" }, "source": [ "## Diving Deeper\n", "\n", "What happened above was achieved by a simple wrapper for [cleanrl_utils/evals/dqn_eval.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl_utils/evals/dqn_eval.py), which is pretty succinct and may give you a more fine-grained control and access to the model. Its content is roughly as follows, where it attempts to download a model from https://huggingface.co/cleanrl/BreakoutNoFrameskip-v4-dqn_atari_jax-seed1 and run an evaluation pass. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eZY6cAxkDJF5", "outputId": "0144efd9-5d8e-4631-8a07-6385d8365558" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/gym/utils/seeding.py:138: DeprecationWarning: \u001b[33mWARN: Function `hash_seed(seed, max_bytes)` is marked as deprecated and will be removed in the future. \u001b[0m\n", " deprecation(\n", "/usr/local/lib/python3.8/dist-packages/gym/utils/seeding.py:175: DeprecationWarning: \u001b[33mWARN: Function `_bigint_from_bytes(bytes)` is marked as deprecated and will be removed in the future. \u001b[0m\n", " deprecation(\n", "/usr/local/lib/python3.8/dist-packages/gym/utils/seeding.py:47: DeprecationWarning: \u001b[33mWARN: Function `rng.randint(low, [high, size, dtype])` is marked as deprecated and will be removed in the future. Please use `rng.integers(low, [high, size, dtype])` instead.\u001b[0m\n", " deprecation(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "eval_episode=0, episodic_return=340.0\n", "eval_episode=1, episodic_return=399.0\n" ] }, { "data": { "text/plain": [ "[340.0, 399.0]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "from typing import Callable\n", "\n", "import flax\n", "import flax.linen as nn\n", "import gym\n", "import jax\n", "import numpy as np\n", "\n", "\n", "def evaluate(\n", " model_path: str,\n", " make_env: Callable,\n", " env_id: str,\n", " eval_episodes: int,\n", " run_name: str,\n", " Model: nn.Module,\n", " epsilon: float = 0.05,\n", " capture_video: bool = True,\n", " seed=1,\n", "):\n", " envs = gym.vector.SyncVectorEnv([make_env(env_id, 0, 0, capture_video, run_name)])\n", " obs = envs.reset()\n", " model = Model(action_dim=envs.single_action_space.n)\n", " q_key = jax.random.PRNGKey(seed)\n", " params = model.init(q_key, obs)\n", " with open(model_path, \"rb\") as f:\n", " params = flax.serialization.from_bytes(params, f.read())\n", " model.apply = jax.jit(model.apply)\n", "\n", " episodic_returns = []\n", " while len(episodic_returns) < eval_episodes:\n", " if random.random() < epsilon:\n", " actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])\n", " else:\n", " q_values = model.apply(params, obs)\n", " actions = q_values.argmax(axis=-1)\n", " actions = jax.device_get(actions)\n", " next_obs, _, _, infos = envs.step(actions)\n", " for info in infos:\n", " if \"episode\" in info.keys():\n", " print(f\"eval_episode={len(episodic_returns)}, episodic_return={info['episode']['r']}\")\n", " episodic_returns += [info[\"episode\"][\"r\"]]\n", " obs = next_obs\n", "\n", " return episodic_returns\n", "\n", "\n", "from huggingface_hub import hf_hub_download\n", "\n", "from cleanrl.dqn_atari_jax import QNetwork, make_env\n", "\n", "model_path = hf_hub_download(repo_id=\"cleanrl/BreakoutNoFrameskip-v4-dqn_atari_jax-seed1\", filename=\"dqn_atari_jax.cleanrl_model\")\n", "evaluate(\n", " model_path,\n", " make_env,\n", " \"BreakoutNoFrameskip-v4\",\n", " eval_episodes=2,\n", " run_name=f\"eval\",\n", " Model=QNetwork,\n", " capture_video=False,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "ZxM0A6LmQtnn" }, "source": [ "## More Examples\n", "\n", "Now let's get going with more examples!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "TrQae62Y70H0" }, "outputs": [], "source": [ "import argparse\n", "from dataclasses import dataclass\n", "\n", "from huggingface_hub import hf_hub_download\n", "\n", "try:\n", " from pip import main as pipmain\n", "except ImportError:\n", " from pip._internal import main as pipmain\n", "\n", "@dataclass\n", "class Args:\n", " exp_name: str = \"dqn_atari_jax\"\n", " seed: int = 1\n", " hf_entity: str = \"cleanrl\"\n", " hf_repository: str = \"\"\n", " env_id: str = \"BreakoutNoFrameskip-v4\"\n", "\n", "\n", "def dqn():\n", " import cleanrl.dqn\n", " import cleanrl_utils.evals.dqn_eval\n", " return cleanrl.dqn.QNetwork, cleanrl.dqn.make_env, cleanrl_utils.evals.dqn_eval.evaluate\n", "\n", "def dqn_atari():\n", " import cleanrl.dqn_atari\n", " import cleanrl_utils.evals.dqn_eval\n", " return cleanrl.dqn_atari.QNetwork, cleanrl.dqn_atari.make_env, cleanrl_utils.evals.dqn_eval.evaluate\n", "\n", "def dqn_jax():\n", " import cleanrl.dqn_jax\n", " import cleanrl_utils.evals.dqn_jax_eval\n", " return cleanrl.dqn_jax.QNetwork, cleanrl.dqn_jax.make_env, cleanrl_utils.evals.dqn_jax_eval.evaluate\n", "\n", "def dqn_atari_jax():\n", " import cleanrl.dqn_atari_jax\n", " import cleanrl_utils.evals.dqn_jax_eval\n", " return cleanrl.dqn_atari_jax.QNetwork, cleanrl.dqn_atari_jax.make_env, cleanrl_utils.evals.dqn_jax_eval.evaluate\n", "\n", "MODELS = {\n", " \"dqn\": dqn,\n", " \"dqn_atari\": dqn_atari,\n", " \"dqn_jax\": dqn_jax,\n", " \"dqn_atari_jax\": dqn_atari_jax,\n", "}\n", "\n", "\n", "\n", "exp_names = [\"dqn\", \"dqn_jax\", \"dqn_atari_jax\", \"dqn_atari\"]\n", "env_idss = [\n", " [\n", " \"CartPole-v1\",\n", " \"Acrobot-v1\",\n", " \"MountainCar-v0\",\n", " ],\n", " [\n", " \"CartPole-v1\",\n", " \"Acrobot-v1\",\n", " \"MountainCar-v0\",\n", " ],\n", " [\n", " \"BreakoutNoFrameskip-v4\",\n", " \"PongNoFrameskip-v4\",\n", " \"BeamRiderNoFrameskip-v4\"\n", " ],\n", " [\n", " \"BreakoutNoFrameskip-v4\",\n", " \"PongNoFrameskip-v4\",\n", " \"BeamRiderNoFrameskip-v4\"\n", " ]\n", " ]\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "IeksFU1me8q8" }, "source": [ "### Install dependencies for each variant" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dnvpgpWWfABl", "outputId": "1e41abbf-d9c4-4adf-fe05-40e8e31962f4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/pip/_vendor/packaging/version.py:127: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\n", " warnings.warn(\n", "/usr/local/lib/python3.8/dist-packages/pip/_vendor/packaging/version.py:127: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\n", " warnings.warn(\n", "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n", "/usr/local/lib/python3.8/dist-packages/pip/_vendor/packaging/version.py:127: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\n", " warnings.warn(\n", "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "==== ['install', '--upgrade', 'cleanrl[dqn]', '--quiet']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/pip/_vendor/packaging/version.py:127: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\n", " warnings.warn(\n", "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "==== ['install', '--upgrade', 'cleanrl[dqn-jax]', '--quiet']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/pip/_vendor/packaging/version.py:127: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\n", " warnings.warn(\n", "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n", "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n", "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "==== ['install', '--upgrade', 'cleanrl[dqn-atari-jax]', '--quiet']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/pip/_vendor/packaging/version.py:127: DeprecationWarning: Creating a LegacyVersion has been deprecated and will be removed in the next major release\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "==== ['install', '--upgrade', 'cleanrl[dqn-atari]', '--quiet']\n" ] } ], "source": [ "for exp_name, env_ids in zip(exp_names, env_idss):\n", " # install dependencies for the algorithm variant\n", " pipmain(['install', '--upgrade', f'cleanrl[{exp_name.replace(\"_\", \"-\")}]', \"--quiet\"])\n", " print(\"====\", ['install', '--upgrade', f'cleanrl[{exp_name.replace(\"_\", \"-\")}]', \"--quiet\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "van2E4jFfC2f" }, "source": [ "# Enjoy!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "1af264779a2442e596aed8e620561248", "21bff070d7b342a3b1ca5c9976746a6f", "82dc6a3d16e24066a8531f1390eb0450", "df36fcd7bf4c47f48914e43866da1edf", "f31718702d1a433eac7388ef1612a149", "e6179240e76b4a2abd79e31b44c8bcf5", "6e8a545a6d8e43a4ac9cc3a5cd8c06b6", "1bbd3551eda24347b078a78c54ef9808", "a3e4a40738274a48ad76090b7e3593ec", "11d8ea24816b4d9487b5cf701c77368f", "7bcc89fefa314206aef5e24d1b105b50", "0c074497102c45aab5db63d863a493a5", "8ffa468c4cee423ea37eb5c6a3008a8d", "71728d572be04c4a816e09c4682dd254", "54f10fce2aa44083bdddd67209f75097", "b49b12ca89c5448a8994373aabb8c2de", "3ac10a61d2394023b867276369d75d94", "8676b5de0bb24b49a2624ce56e57b041", 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"data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1af264779a2442e596aed8e620561248", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/45.8k [00:00