Installation

Requirements

The code is built and tested with Python 3.10, CUDA 12.8, and PyTorch 2.7.1.

Preparation

1. Clone the repository

git clone https://github.com/InternRobotics/InternVLA-A1.git
cd InternVLA-A1

2. Create Conda Environment

conda create -y -n internvla_a1 python=3.10
conda activate internvla_a1

pip install --upgrade pip

3. Install System Dependencies

We use FFmpeg for video encoding/decoding and SVT-AV1 for efficient storage.

conda install -c conda-forge ffmpeg=7.1.1 svt-av1 -y

4. Install PyTorch (CUDA 12.8)

pip install torch==2.7.1 torchvision==0.22.1 torchaudio==2.7.1 \
  --index-url https://download.pytorch.org/whl/cu128

5. Install Python Dependencies

pip install torchcodec numpy scipy transformers==4.57.1 mediapy loguru pytest omegaconf
pip install -e .

6. Patch HuggingFace Transformers

We replace the default implementations of several model modules (e.g., π0, InternVLA_A1_3B, InternVLA_A1_2B) to support custom architectures for robot learning.

TRANSFORMERS_DIR=${CONDA_PREFIX}/lib/python3.10/site-packages/transformers/

cp -r src/lerobot/policies/pi0/transformers_replace/models        ${TRANSFORMERS_DIR}
cp -r src/lerobot/policies/InternVLA_A1_3B/transformers_replace/models  ${TRANSFORMERS_DIR}
cp -r src/lerobot/policies/InternVLA_A1_2B/transformers_replace/models  ${TRANSFORMERS_DIR}

Make sure the target directory exists—otherwise create it manually.

7. Configure Environment Variables

export HF_TOKEN=your_token  # for downloading hf models, tokenizers, or processors
export HF_HOME=path_to_huggingface   # default: ~/.cache/huggingface

8. Link Local HuggingFace Cache

ln -s ${HF_HOME}/lerobot data

This allows the repo to access datasets via ./data/.