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INSTALL.md

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Environment setup

Clone the cosmos-transfer1 source code

git clone git@github.com:nvidia-cosmos/cosmos-transfer1.git
cd cosmos-transfer1
git submodule update --init --recursive

Cosmos runs only on Linux systems. We have tested the installation with Ubuntu 24.04, 22.04, and 20.04. Cosmos requires the Python version to be 3.12.x.

libnvrtc check

Check libnvrtc.so exists

find /usr -name "libnvrtc.so*" 2>/dev/null | head -n 10

If no output then below are the steps to install libnvrtc

Steps to install libnvrtc

  1. determine cuda version using nvidia-smi command nvidia-smi | grep "CUDA Version"
  2. If the cuda version output is 12.8 set CUDA_VERSION=12-8
  3. CUDA_VERSION=12-8
  4. apt-get update && apt-get install -y wget gnupg
  5. wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb

If you are using ubuntu 20.04 instead of ubuntu 22.04 then run

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb

  1. dpkg -i cuda-keyring_1.1-1_all.deb
  2. apt-get -y update && apt-get install -y cuda-nvrtc-$CUDA_VERSION libcublas-$CUDA_VERSION libcurand-$CUDA_VERSION libcusparse-$CUDA_VERSION

Inference using conda

Please also make sure you have conda installed (instructions).

The below commands create the cosmos-transfer1 conda environment and install the dependencies for inference:

# Create the cosmos-transfer1 conda environment.
conda env create --file cosmos-transfer1.yaml
# Activate the cosmos-transfer1 conda environment.
conda activate cosmos-transfer1
# Install the dependencies.
pip install -r requirements.txt
# Install vllm
pip install https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl
export VLLM_ATTENTION_BACKEND=FLASHINFER
pip install vllm==0.9.2
# Install decord
pip install decord==0.6.0

pip install https://github.com/nvidia-cosmos/cosmos-dependencies/releases/download/v1.1.0/apex-0.1+cu128.torch271-cp312-cp312-linux_x86_64.whl

pip install https://github.com/nvidia-cosmos/cosmos-dependencies/releases/download/v1.1.0/flash_attn-2.6.3+cu128.torch271-cp312-cp312-linux_x86_64.whl

pip install https://github.com/nvidia-cosmos/cosmos-dependencies/releases/download/v1.1.0/natten-0.21.0+cu128.torch271-cp312-cp312-linux_x86_64.whl

pip install https://github.com/nvidia-cosmos/cosmos-dependencies/releases/download/v1.1.0/transformer_engine-1.13.0+cu128.torch271-cp312-cp312-linux_x86_64.whl

pip install https://github.com/nvidia-cosmos/cosmos-dependencies/releases/download/v1.1.0/torch-2.7.1+cu128-cp312-cp312-manylinux_2_28_x86_64.whl

pip install https://github.com/nvidia-cosmos/cosmos-dependencies/releases/download/v1.1.0/torchvision-0.22.1+cu128-cp312-cp312-manylinux_2_28_x86_64.whl

# Patch Transformer engine linking issues in conda environments.
ln -sf $CONDA_PREFIX/lib/python3.12/site-packages/nvidia/*/include/* $CONDA_PREFIX/include/
ln -sf $CONDA_PREFIX/lib/python3.12/site-packages/nvidia/*/include/* $CONDA_PREFIX/include/python3.12

apt-get install -y libmagic1

To test the environment setup for inference run

PYTHONPATH=$(pwd) python scripts/test_environment.py

Inference using docker

If you prefer to use a containerized environment, you can build and run this repo's dockerfile to get an environment with all the packages pre-installed. This environment does not use conda. So, there is no need to specify CUDA_HOME=$CONDA_PREFIX when invoking this repo's scripts.

This requires docker to be already present on your system with the Nvidia Container Toolkit installed.

docker build -f Dockerfile . -t nvcr.io/$USER/cosmos-transfer1:latest

Note: In case you encounter permission issues while mounting local files inside the docker, you can share the folders from your current directory to all users (including docker) using this helpful alias

alias share='sudo chown -R ${USER}:users $PWD && sudo chmod g+w $PWD'

before running the docker.

Training

The below commands creates the cosmos-transfer conda environment and installs the dependencies for training. This is the same as required for inference.

# Create the cosmos-transfer1 conda environment.
conda env create --file cosmos-transfer1.yaml
# Activate the cosmos-transfer1 conda environment.
conda activate cosmos-transfer1
# Install the dependencies.
pip install -r requirements.txt
# Install vllm
pip install https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl
export VLLM_ATTENTION_BACKEND=FLASHINFER
pip install vllm==0.9.2
# Install decord
pip install decord==0.6.0
# Patch Transformer engine linking issues in conda environments.
ln -sf $CONDA_PREFIX/lib/python3.12/site-packages/nvidia/*/include/* $CONDA_PREFIX/include/
ln -sf $CONDA_PREFIX/lib/python3.12/site-packages/nvidia/*/include/* $CONDA_PREFIX/include/python3.12

You can test the environment setup for post-training with

PYTHONPATH=$(pwd) python scripts/test_environment.py --training