Goal: Demonstrate best practices using Conda environments with salloc and sbatch.
Conda with salloc and sbatch: Best Practice
As indicated in the Modules and using salloc and sbatch: Best Practice we recommend to perform a module purge when starting an interactive session or submitting a job.
After performing a conda activate on a login node, typically the related set environment variables will be inherited after performing an salloc. But, notice what happens to the command-line prompt:
[@mblog1]$ module purge [@mblog1]$ module load miniconda3/24.3.0 [@mblog1]$ conda activate py_env (py_env) []$ python --version Python 3.12.4 (py_env) [@mblog1]$ salloc -A arcc -t 10:00 salloc: Granted job allocation 1243597 salloc: Nodes mbcpu-025 are ready for job [mbcpu-025]$ python --version Python 3.12.4
Which Conda environment is currently active?
We would suggest, as with performing a module purge, to conda activate environments explicitly after performing an salloc, and within you scripts that you sbatch.
[@mblog1]$ salloc -A arcc -t 10:00 salloc: Granted job allocation 1243600 salloc: Nodes mbcpu-025 are ready for job [@mbcpu-025]$ module purge [@mbcpu-025]$ module load miniconda3/24.3.0 [@mbcpu-025]$ conda activate py_env (py_env) [@mbcpu-025]$ python --version Python 3.12.4 (py_env) [@mbcpu-025]$ conda deactivate [@mbcpu-025]$ exit [@mblog1]$
Again, since this is now detailed within an sbatch-ed script, ARCC can see and replicate exactly what you are doing when there is an issue.
Conda sbatch: Example
Here is a very minimal example of what a submission script would look lie:
Using this script would look something like this:
[]$ sbatch run_conda.sh Submitted batch job 1902001 []$ cat conda_results_1902001.out SLURM_JOB_ID: 1902001 Start: 08/15/24 10:47:23 Python 3.12.2 End: 08/15/24 10:47:24
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