HTCondor examples for pyhf workflows

Example configurations for using pyhf with HTCondor inspired by the Center for High Throughput Computing examples.

CUDA enabled Docker images

These examples assume that you want to use GPU resources to take advantage of hardware acceleration and so focus on using the pyhf Docker base images built on the NVIDIA CUDA enabled images for runtime use with the the NVIDIA Container Toolkit.

Local installation

• Make sure that you have the nvidia-container-toolkit installed on the host machine
• Check the list of available tags on Docker Hub to find the tag you want
• Use docker pull to pull down the image corresponding to the tag

Example:

docker pull pyhf/cuda:0.7.2-jax-cuda-11.6.0-cudnn8

Local use

To check that NVIDIA GPUS are being properly detected run

docker run --rm --gpus all pyhf/cuda:0.7.2-jax-cuda-11.6.0-cudnn8 'nvidia-smi'

and check if the nvidia-smi output appears correctly.

To run (interactively) using GPUs on the host machine:

docker run --rm -ti --gpus all pyhf/cuda:0.7.2-jax-cuda-11.6.0-cudnn8