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Fixes
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Antonin Raffin
parent
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1 changed files with 46 additions and 35 deletions
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@ -52,8 +52,8 @@ Most of them are available via the RL Zoo.
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Official pre-trained models are saved in the SB3 organization on the hub: https://huggingface.co/sb3
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We wrote a tutorial on how to use 🤗 Hub and Stable-Baselines3
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`here <https://colab.research.google.com/github/huggingface/huggingface_sb3/blob/main/notebooks/sb3_huggingface.ipynb>`_
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We wrote a tutorial on how to use 🤗 Hub and Stable-Baselines3
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`here <https://colab.research.google.com/github/huggingface/huggingface_sb3/blob/main/notebooks/sb3_huggingface.ipynb>`_.
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Installation
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@ -63,6 +63,19 @@ Installation
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pip install huggingface_sb3
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.. note::
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If you use the `RL Zoo <https://github.com/DLR-RM/rl-baselines3-zoo>`_, pushing/loading models from the hub is integrated in the RL Zoo:
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.. code-block:: bash
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# Download model and save it into the logs/ folder
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python -m rl_zoo3.load_from_hub --algo a2c --env LunarLander-v2 -orga sb3 -f logs/
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# Test the agent
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python -m rl_zoo3.enjoy --algo a2c --env LunarLander-v2 -f logs/
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# push model, config and hyperparameters to the hub
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python -m rl_zoo3.push_to_hub --algo a2c --env LunarLander-v2 -f logs/ -orga sb3 -m "Initial commit"
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Download a model from the Hub
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-----------------------------
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@ -95,8 +108,8 @@ For instance ``sb3/demo-hf-CartPole-v1``:
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You need to define two parameters:
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- `repo-id`: the name of the Hugging Face repo you want to download.
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- `filename`: the file you want to download.
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- ``repo-id``: the name of the Hugging Face repo you want to download.
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- ``filename``: the file you want to download.
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Upload a model to the Hub
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@ -104,9 +117,9 @@ Upload a model to the Hub
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You can easily upload your models using two different functions:
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1. `package_to_hub()`: save the model, evaluate it, generate a model card and record a replay video of your agent before pushing the complete repo to the Hub.
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1. ``package_to_hub()``: save the model, evaluate it, generate a model card and record a replay video of your agent before pushing the complete repo to the Hub.
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2. `push_to_hub()`: simply push a file to the Hub.
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2. ``push_to_hub()``: simply push a file to the Hub.
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First, you need to be logged in to Hugging Face to upload a model:
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@ -128,13 +141,16 @@ First, you need to be logged in to Hugging Face to upload a model:
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Then, in this example, we train a PPO agent to play CartPole-v1 and push it to a new repo ``sb3/demo-hf-CartPole-v1``
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With package_to_hub()
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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With ``package_to_hub()``
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^^^^^^^^^^^^^^^^^^^^^^^^^
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.. code-block:: python
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from stable_baselines3 import PPO
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from stable_baselines3.common.env_util import make_vec_env
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from huggingface_sb3 import package_to_hub
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# Create the environment
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env_id = "CartPole-v1"
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env = make_vec_env(env_id, n_envs=1)
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@ -144,12 +160,12 @@ With package_to_hub()
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# Instantiate the agent
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model = PPO("MlpPolicy", env, verbose=1)
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# Train the agent
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model.learn(total_timesteps=int(5000))
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# This method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
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package_to_hub(model=model,
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package_to_hub(model=model,
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model_name="ppo-CartPole-v1",
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model_architecture="PPO",
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env_id=env_id,
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@ -159,28 +175,32 @@ With package_to_hub()
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You need to define seven parameters:
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- `model`: your trained model.
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- `model_architecture`: name of the architecture of your model (DQN, PPO, A2C, SAC…).
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- `env_id`: name of the environment.
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- `eval_env`: environment used to evaluate the agent.
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- `repo-id`: the name of the Hugging Face repo you want to create or update. It’s <your huggingface username>/<the repo name>.
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- `commit-message`.
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- `filename`: the file you want to push to the Hub.
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- ``model``: your trained model.
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- ``model_architecture``: name of the architecture of your model (DQN, PPO, A2C, SAC…).
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- ``env_id``: name of the environment.
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- ``eval_env``: environment used to evaluate the agent.
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- ``repo-id``: the name of the Hugging Face repo you want to create or update. It’s <your huggingface username>/<the repo name>.
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- ``commit-message``.
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- ``filename``: the file you want to push to the Hub.
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With push_to_hub()
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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With ``push_to_hub()``
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^^^^^^^^^^^^^^^^^^^^^^
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.. code-block:: python
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from huggingface_sb3 import package_to_hub
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from stable_baselines3 import PPO
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from stable_baselines3.common.env_util import make_vec_env
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from huggingface_sb3 import push_to_hub
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# Create the environment
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env_id = "CartPole-v1"
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env = make_vec_env(env_id, n_envs=1)
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# Instantiate the agent
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model = PPO("MlpPolicy", env, verbose=1)
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# Train the agent
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model.learn(total_timesteps=int(5000))
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@ -197,20 +217,11 @@ With push_to_hub()
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commit_message="Added CartPole-v1 model trained with PPO",
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)
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# This method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
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package_to_hub(model=model,
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model_name="ppo-CartPole-v1",
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model_architecture="PPO",
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env_id=env_id,
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eval_env=eval_env,
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repo_id="sb3/demo-hf-CartPole-v1",
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commit_message="Push ppo-CartPole-v1 model to the Hub")
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You need to define three parameters:
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- `repo-id`: the name of the Hugging Face repo you want to create or update. It’s <your huggingface username>/<the repo name>.
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- `filename`: the file you want to push to the Hub.
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- `commit-message`.
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- ``repo-id``: the name of the Hugging Face repo you want to create or update. It’s <your huggingface username>/<the repo name>.
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- ``filename``: the file you want to push to the Hub.
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- ``commit-message``.
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MLFLow
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======
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