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 agter performing an salloc. But, notice what happens to the command-line prompt:
[salexan5@mblog1 ~]$ conda activate py_env (py_env) [salexan5@mblog1 ~]$ python --version Python 3.12.4 (py_env) [salexan5@mblog1 ~]$ salloc -A arcc -t 10:00 salloc: Granted job allocation 1243597 salloc: Nodes mbcpu-025 are ready for job [salexan5@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.
[salexan5@mblog1 ~]$ salloc -A arcc -t 10:00 salloc: Granted job allocation 1243600 salloc: Nodes mbcpu-025 are ready for job [salexan5@mbcpu-025 ~]$ module purge [salexan5@mbcpu-025 ~]$ module load miniconda3 [salexan5@mbcpu-025 ~]$ conda activate py_env (py_env) [salexan5@mbcpu-025 ~]$ python --version Python 3.12.4
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.
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