Update Hugging Face Integration Documentation

docs/guide/integrations.rst

@@ -47,21 +47,20 @@ Hugging Face 🤗
 ===============
 The Hugging Face Hub 🤗 is a central place where anyone can share and explore models. It allows you to host your saved models 💾.
 
-You can see the list of stable-baselines3 saved models here: https://huggingface.co/models?other=stable-baselines3
+You can see the list of stable-baselines3 saved models here: https://huggingface.co/models?library=stable-baselines3
 Most of them are available via the RL Zoo.
 
 Official pre-trained models are saved in the SB3 organization on the hub: https://huggingface.co/sb3
 
-We wrote a tutorial on how to use 🤗 Hub and Stable-Baselines3 here: https://colab.research.google.com/drive/1GI0WpThwRHbl-Fu2RHfczq6dci5GBDVE#scrollTo=q4cz-w9MdO7T
+We wrote a tutorial on how to use 🤗 Hub and Stable-Baselines3 
+here: https://colab.research.google.com/github/huggingface/huggingface_sb3/blob/main/notebooks/sb3_huggingface.ipynb 
 
-For up to date instructions (for instance for using ``package_to_hub()``), please take a look at the Huggingface SB3 package README: https://github.com/huggingface/huggingface_sb3
 
 Installation
 -------------
 
 .. code-block:: bash
 
- pip install huggingface_hub
  pip install huggingface_sb3
 
 
@@ -83,7 +82,7 @@ For instance ``sb3/demo-hf-CartPole-v1``:
   ## filename = name of the model zip file from the repository
   checkpoint = load_from_hub(
       repo_id="sb3/demo-hf-CartPole-v1",
-      filename="ppo-CartPole-v1",
+      filename="ppo-CartPole-v1.zip",
   )
   model = PPO.load(checkpoint)
 
@@ -94,11 +93,22 @@ For instance ``sb3/demo-hf-CartPole-v1``:
   )
   print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
 
+You need to define two parameters:
+
+- `repo-id`: the name of the Hugging Face repo you want to download.
+- `filename`: the file you want to download.
 
 
 Upload a model to the Hub
 -------------------------
 
+You can easily upload your models using two different functions:
+
+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.
+
+2. `push_to_hub()`: simply push a file to the Hub.
+
+
 First, you need to be logged in to Hugging Face to upload a model:
 
 - If you're using Colab/Jupyter Notebooks:
@@ -109,38 +119,99 @@ First, you need to be logged in to Hugging Face to upload a model:
  notebook_login()
 
 
-- Otheriwse:
+- Otherwise:
 
 .. code-block:: bash
 
  huggingface-cli login
 
+
 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``
 
+With package_to_hub()
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 .. code-block:: python
 
-  from huggingface_sb3 import push_to_hub
-  from stable_baselines3 import PPO
+  from huggingface_sb3 import package_to_hub
+  
+  # Create the environment
+  env_id = "CartPole-v1"
+  env = make_vec_env(env_id, n_envs=1)
 
-  # Define a PPO model with MLP policy network
-  model = PPO("MlpPolicy", "CartPole-v1", verbose=1)
+  # Create the evaluation environment
+  eval_env = make_vec_env(env_id, n_envs=1)
 
-  # Train it for 10000 timesteps
-  model.learn(total_timesteps=10_000)
+  # Instantiate the agent
+  model = PPO("MlpPolicy", env, verbose=1)
+  
+  # Train the agent
+  model.learn(total_timesteps=int(5000))
+
+  # This method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
+  package_to_hub(model=model, 
+               model_name="ppo-CartPole-v1",
+               model_architecture="PPO",
+               env_id=env_id,
+               eval_env=eval_env,
+               repo_id="sb3/demo-hf-CartPole-v1",
+               commit_message="Test commit")
+
+You need to define seven parameters:
+
+- `model`: your trained model.
+- `model_architecture`: name of the architecture of your model (DQN, PPO, A2C, SAC…).
+- `env_id`: name of the environment.
+- `eval_env`: environment used to evaluate the agent.
+- `repo-id`: the name of the Hugging Face repo you want to create or update. It’s <your huggingface username>/<the repo name>.
+- `commit-message`.
+- `filename`: the file you want to push to the Hub.
+
+With push_to_hub()
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+  from huggingface_sb3 import package_to_hub
+  
+  # Create the environment
+  env_id = "CartPole-v1"
+  env = make_vec_env(env_id, n_envs=1)
+
+  # Instantiate the agent
+  model = PPO("MlpPolicy", env, verbose=1)
+  
+  # Train the agent
+  model.learn(total_timesteps=int(5000))
 
   # Save the model
   model.save("ppo-CartPole-v1")
 
-  # Push this saved model to the hf repo
+  # Push this saved model .zip file to the hf repo
   # If this repo does not exists it will be created
   ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
   ## filename: the name of the file == "name" inside model.save("ppo-CartPole-v1")
   push_to_hub(
-      repo_id="sb3/demo-hf-CartPole-v1",
-      filename="ppo-CartPole-v1",
-      commit_message="Added Cartpole-v1 model trained with PPO",
+    repo_id="sb3/demo-hf-CartPole-v1",
+    filename="ppo-CartPole-v1.zip",
+    commit_message="Added CartPole-v1 model trained with PPO",
   )
 
+  # This method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
+  package_to_hub(model=model, 
+               model_name="ppo-CartPole-v1",
+               model_architecture="PPO",
+               env_id=env_id,
+               eval_env=eval_env,
+               repo_id="sb3/demo-hf-CartPole-v1",
+               commit_message="Push ppo-CartPole-v1 model to the Hub")
+
+You need to define three parameters:
+
+- `repo-id`: the name of the Hugging Face repo you want to create or update. It’s <your huggingface username>/<the repo name>.
+- `filename`: the file you want to push to the Hub.
+- `commit-message`.
+
 MLFLow
 ======