# LeRobot

## Installation

Create a virtual environment with Python 3.10, e.g. using `conda`:
```
conda create -y -n lerobot python=3.10
conda activate lerobot
```

[Install `poetry`](https://python-poetry.org/docs/#installation) (if you don't have it already)
```
curl -sSL https://install.python-poetry.org | python -
```

Install dependencies
```
poetry install
```

If you encounter a disk space error, try to change your tmp dir to a location where you have enough disk space, e.g.
```
mkdir ~/tmp
export TMPDIR='~/tmp'
```

To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiments tracking, log in with
```
wandb login
```

## Usage


### Train

```
python lerobot/scripts/train.py \
hydra.job.name=pusht \
env=pusht
```

### Visualize offline buffer

```
python lerobot/scripts/visualize_dataset.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht
```

### Visualize online buffer / Eval

```
python lerobot/scripts/eval.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht
```


## TODO

If you are not sure how to contribute or want to know the next features we working on, look on this project page: [LeRobot TODO](https://github.com/users/Cadene/projects/1)

Ask [Remi Cadene](re.cadene@gmail.com) for access if needed.


## Profile

**Example**
```python
from torch.profiler import profile, record_function, ProfilerActivity

def trace_handler(prof):
    prof.export_chrome_trace(f"tmp/trace_schedule_{prof.step_num}.json")

with profile(
    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
    schedule=torch.profiler.schedule(
        wait=2,
        warmup=2,
        active=3,
    ),
    on_trace_ready=trace_handler
) as prof:
    with record_function("eval_policy"):
        for i in range(num_episodes):
            prof.step()
```

```bash
python lerobot/scripts/eval.py \
pretrained_model_path=/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt \
eval_episodes=7
```

## Contribute

**Style**
```
# install if needed
pre-commit install
# apply style and linter checks before git commit
pre-commit run -a
```

**Adding dependencies (temporary)**

Right now, for the CI to work, whenever a new dependency is added it needs to be also added to the cpu env, eg:

```
# Run in this directory, adds the package to the main env with cuda
poetry add some-package

# Adds the same package to the cpu env
cd .github/poetry/cpu && poetry add some-package
```

**Tests**

Install [git lfs](https://git-lfs.com/) to retrieve test artifacts (if you don't have it already).

On Mac:
```
brew install git-lfs
git lfs install
```

On Ubuntu:
```
sudo apt-get install git-lfs
git lfs install
```

Pull artifacts if they're not in [tests/data](tests/data)
```
git lfs pull
```

When adding a new dataset, mock it with
```
python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
```

Run tests
```
DATA_DIR="tests/data" pytest -sx tests
```

**Datasets**

To add a pytorch rl dataset to the hub, first login and use a token generated from [huggingface settings](https://huggingface.co/settings/tokens) with write access:
```
huggingface-cli login --token $HUGGINGFACE_TOKEN --add-to-git-credential
```

Then you can upload it to the hub with:
```
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset  \
--revision v1.0
```

For instance, for [cadene/pusht](https://huggingface.co/datasets/cadene/pusht), we used:
```
HF_USER=cadene
DATASET=pusht
```

If you want to improve an existing dataset, you can download it locally with:
```
mkdir -p data/$DATASET
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download $HF_USER/$DATASET \
--repo-type dataset \
--local-dir data/$DATASET \
--local-dir-use-symlinks=False \
--revision v1.0
```

Iterate on your code and dataset with:
```
DATA_DIR=data python train.py
```

Then upload a new version:
```
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset \
--revision v1.1 \
--delete "*"
```

And you might want to mock the dataset if you need to update the unit tests as well:
```
python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET
```


## Acknowledgment
- Our Diffusion policy and Pusht environment are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/)
- Our TDMPC policy and Simxarm environment are adapted from [FOWM](https://www.yunhaifeng.com/FOWM/)
- Our ACT policy and ALOHA environment are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha/)