Install LeRobot and prepare a Python environment

Prepare the software environment

The tested workflow uses LeRobot v0.6.0, Python 3.12.3, and PyTorch 2.11.0 with CUDA 12.8.

Check that git, ffmpeg, and uv are installed:

    

        
        
git --version
ffmpeg -version
uv --version

    

If one of these tools is missing, follow the LeRobot installation documentation .

Create an isolated environment

Clone the official LeRobot repository and check out the tested revision, then create a virtual environment:

    

        
        
git clone https://github.com/huggingface/lerobot.git
cd lerobot
git checkout 30da8e687a6dfc617fcd94afc367ac7071c376ce

uv venv --python 3.12 .venv
source .venv/bin/activate
uv pip install -e ".[core_scripts,training,feetech,smolvla]"

    

The extras install the robot command-line tools, training dependencies, Feetech motor support, and SmolVLA dependencies.

Verify LeRobot and CUDA

Print the core versions and confirm that PyTorch can see CUDA:

    

        
        
python -c "import platform, torch; from importlib.metadata import version; print({'python': platform.python_version(), 'lerobot': version('lerobot'), 'torch': torch.__version__, 'cuda': torch.cuda.is_available()})"

    

The output from the DGX Spark is similar to:

    

        
        {'python': '3.12.3', 'lerobot': '0.6.0', 'torch': '2.11.0+cu128', 'cuda': True}

        
    

Verify the commands that you’ll use later:

    

        
        
for cli in lerobot-calibrate lerobot-find-cameras lerobot-find-port \
           lerobot-record lerobot-rollout lerobot-teleoperate lerobot-train; do
    command -v "$cli" > /dev/null || exit 1
done

    

The loop exits without output when all commands are available.

Authenticate with Hugging Face

Sign in to Hugging Face interactively:

    

        
        
hf auth login
hf auth whoami

    

Follow the browser or terminal prompt to complete authentication.

What you’ve accomplished and what’s next

You now have a Python environment with CUDA, LeRobot’s SO-101 tools, and SmolVLA dependencies. Keep this terminal active.

Next, you’ll connect the two arms and cameras to the DGX Spark and identify their device paths.