#
Using sample-factory at Hugging Face

`sample-factory` is a codebase for high throughput asynchronous reinforcement learning. It has integrations with the Hugging Face Hub to share models with evaluation results and training metrics.

##
Exploring sample-factory in the Hub

You can find `sample-factory` models by filtering at the left of the models page.

All models on the Hub come up with useful features:

1. An automatically generated model card with a description, a training configuration, and more.
2. Metadata tags that help for discoverability.
3. Evaluation results to compare with other models.
4. A video widget where you can watch your agent performing.

##
Install the library

To install the `sample-factory` library, you need to install the package:

`pip install sample-factory`

SF is known to work on Linux and MacOS. There is no Windows support at this time.

##
Loading models from the Hub

###
Using load_from_hub

To download a model from the Hugging Face Hub to use with Sample-Factory, use the `load_from_hub` script:

    python -m sample_factory.huggingface.load_from_hub -r <HuggingFace_repo_id> -d <train_dir_path>

The command line arguments are:

- `-r`: The repo ID for the HF repository to download from. The repo ID should be in the format `<username>/<repo_name>`
- `-d`: An optional argument to specify the directory to save the experiment to. Defaults to `./train_dir` which will save the repo to `./train_dir/<repo_name>`

###
Download Model Repository Directly

Hugging Face repositories can be downloaded directly using `git clone`:

    git clone git@hf.co:<Name of HuggingFace Repo> # example: git clone git@hf.co:bigscience/bloom

##
Using Downloaded Models with Sample-Factory

After downloading the model, you can run the models in the repo with the enjoy script corresponding to your environment. For example, if you are downloading a `mujoco-ant` model, it can be run with:

    python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_ant --experiment=<repo_name> --train_dir=./train_dir

Note, you may have to specify the `--train_dir` if your local train_dir has a different path than the one in the `cfg.json`

##
Sharing your models

###
Using push_to_hub

If you want to upload without generating evaluation metrics or a replay video, you can use the `push_to_hub` script:

    python -m sample_factory.huggingface.push_to_hub -r <hf_username>/<hf_repo_name> -d <experiment_dir_path>

The command line arguments are:

- `-r`: The repo_id to save on HF Hub. This is the same as `hf_repository` in the enjoy script and must be in the form `<hf_username>/<hf_repo_name>`
- `-d`: The full path to your experiment directory to upload

###
Using enjoy.py

You can upload your models to the Hub using your environment’s `enjoy` script with the `--push_to_hub` flag. Uploading using `enjoy` can also generate evaluation metrics and a replay video.

The evaluation metrics are generated by running your model on the specified environment for a number of episodes and reporting the mean and std reward of those runs.

Other relevant command line arguments are:

- `--hf_repository`: The repository to push to. Must be of the form `<username>/<repo_name>`. The model will be saved to `https://huggingface.co/<username>/<repo_name>`
- `--max_num_episodes`: Number of episodes to evaluate on before uploading. Used to generate evaluation metrics. It is recommended to use multiple episodes to generate an accurate mean and std.
- `--max_num_frames`: Number of frames to evaluate on before uploading. An alternative to `max_num_episodes`
- `--no_render`: A flag that disables rendering and showing the environment steps. It is recommended to set this flag to speed up the evaluation process.

You can also save a video of the model during evaluation to upload to the hub with the `--save_video` flag

- `--video_frames`: The number of frames to be rendered in the video. Defaults to -1 which renders an entire episode
- `--video_name`: The name of the video to save as. If `None`, will save to `replay.mp4` in your experiment directory

For example:

    python -m sf_examples.mujoco_examples.enjoy_mujoco --algo=APPO --env=mujoco_ant --experiment=<repo_name> --train_dir=./train_dir --max_num_episodes=10 --push_to_hub --hf_username=<username> --hf_repository=<hf_repo_name> --save_video --no_render