A library to load and upload Stable-baselines3 models from the Hub.
pip install huggingface-sb3
We wrote a tutorial on how to use 🤗 Hub and Stable-Baselines3 here
import gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## 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.zip",
)
model = PPO.load(checkpoint)
# Evaluate the agent and watch it
eval_env = gym.make("CartPole-v1")
mean_reward, std_reward = evaluate_policy(
model, eval_env, render=False, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
First you need to be logged in to Hugging Face:
- If you're using Colab/Jupyter Notebooks:
from huggingface_hub import notebook_login
notebook_login()
- Else:
huggingface-cli login
Then:
from huggingface_sb3 import push_to_hub
from stable_baselines3 import PPO
# Define a PPO model with MLP policy network
model = PPO("MlpPolicy", "CartPole-v1", verbose=1)
# Train it for 10000 timesteps
model.learn(total_timesteps=10_000)
# Save the model
model.save("ppo-CartPole-v1")
# Push this saved model 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.zip",
commit_message="Added Cartpole-v1 model trained with PPO",
)