Installation

In order to train a ChromBPNet model, you will need to have certain packages installed on your machine. First, it is highly recommended that you use a GPU for model training and have the necessary NVIDIA drivers and CUDA already installed. You can check if your machine is properly set up to use GPUs by running the command nvidia-smi and making sure it returns information about your system GPU(s) instead of an error. Additionally, there are two ways to ensure you have the necessary packages to train ChromBPNet models:

1. Running in docker (Recommended)

Download and install the latest version of Docker for your operating system. You can find the appropriate installer for your platform at Docker Installers. Once Docker is installed, run the below shown docker run command followed by the necessary parameters to open an environment with all the necessary packages installed. Then navigate to the chrombpnet directory by running the command cd chrombpnet to start running the tutorial.

Note: To access your system GPU's from within the docker container, you must have NVIDIA Container Toolkit installed on your host machine.

docker run -it --rm --memory=100g --gpus device=0  kundajelab/chrombpnet:latest

2. Local installation

Create a clean conda environment with python >=3.8

conda create -n chrombpnet python=3.8
conda activate chrombpnet

Install non-Python requirements via conda

conda install -y -c conda-forge -c bioconda samtools bedtools ucsc-bedgraphtobigwig pybigwig meme

Install from pypi

pip install chrombpnet

Install from source

git clone https://github.com/kundajelab/chrombpnet.git
pip install -e chrombpnet