Create a conda environment with CuPy¶

For getting conda itself, see Conda on Phoebe and Koios.

Get an interactive shell on a GPU node¶

From Phoebe login node, request 16CPUs, some RAM and one NVIDIA A100:

srun --part=gpu_int --job-name "Conda build at GPU" --gres=gpu:a100:1 --cpus-per-task=16 --mem=128G --time=24:00:00 --pty bash

example

[user@login1 ~]$ srun --part=gpu_int --job-name "Conda build at GPU" --gres=gpu:a100:1 --cpus-per-task=16 --mem=128G --time=24:00:00 --pty bash
srun: job 1401058 queued and waiting for resources
srun: job 1401058 has been allocated resources
[user@gpu2 ~]$

(note that the last prompt changed, and we're now inside of interactive slurm session at gpu2 compute node)

Create an empty conda environment¶

Note: to have CUDA detection properly working, this must be done on gpu node.

First, create new conda environment with recent python included:

conda create --name cupy-231030 python=3.11

example:

[jose@gpu2 ~]$ conda create --name cupy-231030 python=3.11
Collecting package metadata (current_repodata.json): done
...
Proceed ([y]/n)? y
...
[jose@gpu2 ~]$

Activate the new environment and install CUDA into it¶

we install from NVIDIA conda channel because we need nvcc and other dependencies

conda activate cupy-231030 conda install -c "nvidia/label/cuda-12.3.0" cuda-toolkit

Install CuPy for the given CUDA version¶

above we installed cuda-12-x so:

python3.11 -m pip install cupy-cuda12x