# 🛠️ Installation Guide ```{important} We are actively fixing mistakes in the document. If you find any errors in the documentation, please feel free to [open an issue](https://github.com/InternRobotics/internrobotics.github.io/issues). Your help in improving the document is greatly appreciated 🥰. ``` 😄 Don’t worry — both [Quick Installation](#quick-installation) and [Dataset Preparation](#dataset-preparation) are beginner-friendly. ## Prerequisites InternManip works across most hardware setups. Just note the following exceptions: - **GenManip Benchmark** must run on **NVIDIA RTX series GPUs** (e.g., RTX 4090). - GR00T requires **CUDA 12.4 installed system-wide (not via Conda)**. ### Overall Requirements - **OS:** Ubuntu 20.04/22.04 - **GPU Compatibility**:

GPU	Model Training & Inference	Simulation
		CALVIN	Simpler-Env	Genmanip
NVIDIA RTX Series (Driver: 535.216.01+ )	✅	✅	✅	✅
NVIDIA V/A/H100	✅	✅	✅	❌

```{note} We provide a flexible installation tool for users who want to use InternManip for different purposes. Users can choose to install the training and inference environment, and the individual simulation environment independently. ``` ### Model-Specific Requirements

Models	Minimum GPU Requirement			System RAM (Train/Inference)	CUDA
	Training (Full)	Training (LoRA)	Inference
GR00T-N1/1.5	RTX 4090 / A100	-	RTX 3090 / A100	24GB / 8GB	⚠️ 12.4
Pi-0	RTX 4090 (48G) / A100	RTX 4090	RTX 3090 / A100	70GB / 8GB	-
Diffusion Policy (DP)	RTX 2080	-	RTX 2070	16GB / 8GB	-

## Quick Installation We provide a unified installation script that handles environment setup and dependency installation automatically. ### Step 1: Clone the Repository and Install uv ```bash # Clone the main repository git clone https://github.com/internrobotics/internmanip.git cd internmanip # Initialize and update submodules git submodule update --init --recursive # Skip the following commands if you have installed uv # Install uv package manager curl -LsSf https://astral.sh/uv/install.sh | sh # Restart your shell or source the profile source ~/.bashrc ``` ### Step 2: Run the Installation Script ```bash # Make the script executable chmod +x install.sh # View available options ./install.sh --help ``` #### 🙋 Lightweight Installation （Recommended for beginners）: ```bash ./install.sh --beginner ``` #### 🧠 Full Installation (Advanced users): ```bash ./install.sh --all ``` #### Available Installation Options: ```bash Usage: --calvin [NAME] Create Calvin benchmark virtual environment and install dependencies --simpler-env [NAME] Create SimplerEnv benchmark virtual environment and install dependencies --genmanip [NAME] Create GenManip benchmark virtual environment and install dependencies --model [NAME] Create model virtual environment and install dependencies --all Create all virtual environments and install dependencies (recommended for advanced users) --beginner Create beginner virtual environments and install dependencies (without genmanip, recommended for beginners) Customization Options: --venv-dir DIR Set custom virtual environment root directory (default: .venv) --python-version V Set default Python version (recommended default: 3.10) Examples: ./install.sh --venv-dir ./my_envs --model ./install.sh --calvin calvin-test --model model-test ./install.sh --python-version 3.10 --calvin calvin-dev --simpler-env simpler-dev ./install.sh --all ./install.sh --beginner --help Show help information ``` #### Selective Installation: ```bash # Install only specific components ./install.sh --gr00t --genmanip ``` #### Activate Virtual Environment After installation, virtual environments are created in the `.venv` directory by default. ```bash # List available environments ls .venv/ # Activate a specific environment source .venv/{environment_name}/bin/activate # Example: Activate GR00T environment source .venv/gr00t/bin/activate # Deactivate environment deactivate ``` Optionally, users can customize the virtual environments directory path by passing the `--venv-dir {path}` option when executing `install.sh`. ```bash ./install.sh --venv-dir ./my_envs --model ``` ## Verification (WIP) To check your installation, you can evaluate the pretrained Pi-0 on the `Simpler-Env` benchmark using the following command: ```bash python scripts/eval/start_evaluator.py --config run_configs/examples/internmanip_demo.py ``` If it installed properly, you can find the evaluation results and the visualization in the `eval_results/bridgedata_v2/pi0` directory:

🎉 Congratulations! You have successfully installed InternManip. ## Dataset Preparation (WIP) ### Automatic Download Datasets, model weights, and benchmark assets are automatically downloaded when running the code for the first time. The default download location is `${repo_root}/data`. The system will prompt you to download required datasets. ```{warning} Please ensure you have enough disk space available before starting the download. For cluster users, we recommend creating a symbolic link to a data storage for `${repo_root}/data` to avoid disk space issues. ``` ### Manual Download If you prefer manual dataset preparation: 1. **Visit our platform:** [Dataset Platform](https://huggingface.co/InternRobotics) 2. **Download datasets** based on your needs: - [GenManip-v1](https://huggingface.co/datasets/InternRobotics/InternData-GenmanipTest) - [CALVIN](https://huggingface.co/datasets/InternRobotics/InternData-Calvin_ABC) - [Google-Robot](https://huggingface.co/datasets/InternRobotics/InternData-fractal20220817_data) - [BridgeData-v2](https://huggingface.co/datasets/InternRobotics/InternData-BridgeV2)