Installation

This section will discuss the packages needed to train a ChromBPNet model. Firstly, it is recommended that you use a GPU for model training and have the necessary NVIDIA drivers and CUDA already installed. You can verify that your machine is set up to use GPU's properly by executing the nvidia-smi command and ensuring that the command returns information about your system GPU(s) (rather than an error). Secondly there are two ways to ensure you have the necessary packages to train ChromBPNet models which we detail below,

1. Running in docker

Download and install the latest version of Docker for your platform. Here is the link for the installers -Docker Installers. Run the docker run command below to open a environment with all the packages installed and do 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