> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/NVIDIA/Isaac-GR00T/llms.txt
> Use this file to discover all available pages before exploring further.

# Installation

> Install Isaac GR00T with uv or Docker

GR00T uses [uv](https://github.com/astral-sh/uv) for fast, reproducible dependency management. You can also use Docker for a containerized setup.

## Prerequisites

* **uv v0.8.4+** is required to parse `[tool.uv.extra-build-dependencies]` in `pyproject.toml` (needed for building `flash-attn`)
* NVIDIA GPU with compatible drivers
* CUDA 12.4 is recommended and officially tested (CUDA 11.8 also verified to work)
* For RTX-5090: Tested with CUDA 12.8, `flash-attn==2.8.0.post2`, `pytorch-cu128`

<Note>
  If using CUDA 11.8, you must install a compatible version of `flash-attn` manually (e.g., `flash-attn==2.8.2` confirmed working with CUDA 11.8).
</Note>

## Installation with uv

<Steps>
  <Step title="Clone the repository">
    GR00T relies on submodules for certain dependencies. Include them when cloning:

    ```bash theme={null}
    git clone --recurse-submodules https://github.com/NVIDIA/Isaac-GR00T
    cd Isaac-GR00T
    ```

    If you've already cloned without submodules, initialize them separately:

    ```bash theme={null}
    git submodule update --init --recursive
    ```
  </Step>

  <Step title="Install uv">
    If you don't have uv installed, follow the [uv installation guide](https://github.com/astral-sh/uv#installation).
  </Step>

  <Step title="Create environment and install dependencies">
    After installing uv, create the environment and install GR00T:

    ```bash theme={null}
    uv sync --python 3.10
    uv pip install -e .
    ```

    This installs the core dependencies including:

    * PyTorch 2.7.1
    * Transformers 4.51.3
    * Diffusers 0.35.1
    * Flash Attention 2.7.4
    * And other required packages
  </Step>

  <Step title="Optional: Install TensorRT for faster inference">
    For 2x faster inference with TensorRT acceleration:

    ```bash theme={null}
    uv sync --extra tensorrt
    ```

    This includes ONNX and TensorRT dependencies for optimized deployment.
  </Step>
</Steps>

## Installation with Docker

For a containerized setup that avoids system-level dependency conflicts, use the Docker setup.

### Prerequisites

* Docker (version 20.10+) with [post-installation setup](https://docs.docker.com/engine/install/linux-postinstall/) to run without sudo
* NVIDIA Container Toolkit ([installation guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
* NVIDIA GPU with compatible drivers
* Bash shell
* Sufficient disk space (several GB)

### Build and run

<Steps>
  <Step title="Navigate to the docker directory">
    ```bash theme={null}
    cd docker
    ```
  </Step>

  <Step title="Build the Docker image">
    The build process uses `nvcr.io/nvidia/pytorch:25.04-py3` as the base image:

    ```bash theme={null}
    bash build.sh
    ```

    This installs all dependencies and sets up the GR00T codebase at `/workspace/gr00t/`.
  </Step>

  <Step title="Run the container">
    <CodeGroup>
      ```bash Interactive shell (baked code) theme={null}
      docker run -it --rm --gpus all gr00t-dev /bin/bash
      ```

      ```bash Development mode (mounted code) theme={null}
      docker run -it --rm --gpus all \
          -v $(pwd)/..:/workspace/gr00t \
          gr00t-dev /bin/bash
      ```
    </CodeGroup>

    <Note>
      Development mode mounts your local codebase for live editing. Run this from the `docker/` directory. Changes to your local GR00T code will be immediately reflected inside the container.
    </Note>
  </Step>
</Steps>

### Docker troubleshooting

<AccordionGroup>
  <Accordion title="GPU not detected">
    * Verify NVIDIA Container Toolkit: `nvidia-container-toolkit --version`
    * Restart Docker: `sudo systemctl restart docker`
    * Test GPU access: `docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi`
  </Accordion>

  <Accordion title="Permission errors">
    Use `sudo` with Docker commands, or add your user to the `docker` group:

    ```bash theme={null}
    sudo usermod -aG docker $USER
    ```

    Log out and back in for the changes to take effect.
  </Accordion>

  <Accordion title="Build failures">
    * Check disk space: `df -h`
    * Clean Docker: `docker system prune -a`
    * Rebuild without cache: `sudo bash build.sh --no-cache`
  </Accordion>
</AccordionGroup>

## Verify installation

After installation, verify that GR00T is installed correctly:

```bash theme={null}
uv run python -c "import gr00t; print('GR00T successfully installed!')"
```

## Next steps

<CardGroup cols={2}>
  <Card title="Quick start" icon="rocket" href="/getting-started/quickstart">
    Get started with a quick inference example
  </Card>

  <Card title="Hardware requirements" icon="server" href="/getting-started/hardware-requirements">
    Check hardware recommendations for training and deployment
  </Card>
</CardGroup>