# Installation Guide 😄 Don’t worry — both [Quick Installation](#quick-installation) and [Dataset Preparation](#dataset-preparation) are beginner-friendly. ## Prerequisites InternNav works across most hardware setups. Just note the following exceptions: - **Benchmark based on Isaac Sim** such as VN and VLN-PE benchmarks must run on **NVIDIA RTX series GPUs** (e.g., RTX 4090). ### Simulation Requirements - **OS:** Ubuntu 20.04/22.04 - **GPU Compatibility**:

GPU	Model Training & Inference	Simulation
		VLN-CE	VN	VLN-PE
NVIDIA RTX Series (Driver: 535.216.01+ )	✅	✅	✅	✅
NVIDIA V/A/H100	✅	✅	❌	❌

```{note} We provide a flexible installation tool for users who want to use InternNav 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)
	Training	Inference
StreamVLN & InternVLA-N1	A100	RTX 4090 / A100	80GB / 24GB
NavDP (VN Models)	RTX 4090 / A100	RTX 3060 / A100	16GB / 2GB
CMA (VLN-PE Small Models)	RTX 4090 / A100	RTX 3060 / A100	8GB / 1GB

## Quick Installation Our toolchain provides two Python environment solutions to accommodate different usage scenarios with the InternNav-N1 series model: - For quick trials and evaluations of the InternNav-N1 model, we recommend using the [Habitat environment](#habitat-environment). This option offer allowing you to quickly test and eval the InternVLA-N1 models with minimal configuration. - If you require high-fidelity rendering, training capabilities, and physical property evaluations within the environment, we suggest using the [Isaac Sim](#isaac-sim-environment) environment. This solution provides enhanced graphical rendering and more accurate physics simulations for comprehensive testing. Choose the environment that best fits your specific needs to optimize your experience with the InternNav-N1 model. Note that both environments support the training of the system1 model NavDP. ### Isaac Sim Environment #### Prerequisite - Ubuntu 20.04, 22.04 - Conda - Python 3.10.16 (3.10.* should be ok) - NVIDIA Omniverse Isaac Sim 4.5.0 - NVIDIA GPU (RTX 2070 or higher) - NVIDIA GPU Driver (recommended version 535.216.01+) - PyTorch 2.5.1, 2.6.0 (recommended) - CUDA 11.8, 12.4 (recommended) - Docker (Optional) - NVIDIA Container Toolkit (Optional) Before proceeding with the installation, ensure that you have [Isaac Sim 4.5.0](https://docs.isaacsim.omniverse.nvidia.com/4.5.0/installation/install_workstation.html) and [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) installed. To help you get started quickly, we've prepared a Docker image pre-configured with Isaac Sim 4.5 and InternUtopia. You can pull the image and run evaluations in the container using the following command: ```bash docker pull registry.cn-hangzhou.aliyuncs.com/internutopia/internutopia:2.2.0 docker run -it --name internutopia-container registry.cn-hangzhou.aliyuncs.com/internutopia/internutopia:2.2.0 ``` #### Conda installation ```bash $ conda create -n python=3.10 libxcb=1.14 # Install InternUtopia through pip.(2.1.1 and 2.2.0 recommended) $ conda activate $ pip install internutopia # Configure the conda environment. $ python -m internutopia.setup_conda_pypi $ conda deactivate && conda activate ``` For InternUtopia installation, you can find more detailed [docs](https://internrobotics.github.io/user_guide/internutopia/get_started/installation.html) in [InternUtopia](https://github.com/InternRobotics/InternUtopia?tab=readme-ov-file). ```bash # Install PyTorch based on your CUDA version $ pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu118 # Install other deps $ pip install -r isaac_requirements.txt ``` If you need to train or evaluate models on [Habitat](#optional-habitat-environment) without physics simulation, we recommend the following setup and easier environment installation. ### Habitat Environment #### Prerequisite - Python 3.9 - Pytorch 2.1.2 - CUDA 12.4 - GPU: NVIDIA A100 or higher (optional for VLA training) ```bash conda install habitat-sim==0.2.4 withbullet headless -c conda-forge -c aihabitat git clone --branch v0.2.4 https://github.com/facebookresearch/habitat-lab.git cd habitat-lab pip install -e habitat-lab # install habitat_lab pip install -e habitat-baselines # install habitat_baselines pip install -r habitat_requirements.txt ``` ## Verification Please download our latest pretrained [checkpoint](https://huggingface.co/InternRobotics/InternVLA-N1) of InternVLA-N1 and run the following script to inference with visualization results. Move the checkpoint to the `checkpoints` directory. Download the VLN-CE dataset from [huggingface](). The final folder structure should look like this: ```bash InternNav/ |-- data/ | |-- datasets |-- vln |-- vln_datasets |-- scene_datasets |-- hm3d |-- mp3d |-- src/ | |-- ... |-- checkpoints/ | |-- InternVLA-N1/ | | |-- model-00001-of-00004.safetensors | | |-- config.json | | |-- ... | |-- InternVLA-N1-S2 | | |-- model-00001-of-00004.safetensors | | |-- config.json | | |-- ... ``` Replace the 'model_path' variable in 'vln_ray_backend.py' with the path of InternVLA-N1 checkpoint. ```bash srun -p {partition_name} --cpus-per-task 16 --gres gpu:1 python3 scripts/eval/vln_ray_backend.py ``` Find the IP address of the node allocated by Slurm. Then change the BACKEND_URL in the gradio client (navigation_ui.py) to the server's IP address. Start the gradio. ```bash python navigation_ui.py ``` Note that it's better to run the Gradio client on a machine with a graphical user interface (GUI) but ensure there is proper network connectivity between the client and the server. Then open a browser and enter the Gradio address (such as http://0.0.0.0:5700). We can see the interface as shown below.  Click the 'Start Navigation Simulation' button to send a VLN request to the backend. The backend will submit a task to ray server and simulate the VLN task with InternVLA-N1 models. Wait about 3 minutes, the VLN task will be finished and return a result video. We can see the result video in the gradio like this.  🎉 Congratulations! You have successfully installed InternNav. ## Dataset Preparation We also prepare high-quality data for trainning system1/system2. To set up the trainning dataset, please follow the steps below: 1. Download Datasets - Download the [InternData-N1](https://huggingface.co/datasets/InternRobotics/InternData-N1) for: - `vln_pe/` - `vln_ce/` - `vln_n1/` - Download the [SceneData-N1](https://huggingface.co/datasets/InternRobotics/Scene-N1) for the `scene_data/`. 2. Directory Structure After downloading, organize the datasets into the following structure: ```bash data/ ├── scene_data/ │ ├── mp3d_pe/ │ │ ├──17DRP5sb8fy/ │ │ ├── 1LXtFkjw3qL/ │ │ └── ... │ ├── mp3d_ce/ │ └── mp3d_n1/ ├── vln_pe/ │ ├── raw_data/ │ │ ├── train/ │ │ ├── val_seen/ │ │ │ └── val_seen.json.gz │ │ └── val_unseen/ │ │ └── val_unseen.json.gz ├── └── traj_data/ │ └── mp3d/ │ └── trajectory_0/ │ ├── data/ │ ├── meta/ │ └── videos/ ├── vln_ce/ │ ├── raw_data/ │ └── traj_data/ └── vln_n1/ └── traj_data/ ```