Batch Visualization on Andes

It is best to work on andes.olcf.ornl.gov. You will want to setup a new env for andes. We’ll call it andes_env.

You need to load python with anaconda support there:

module load python/3.7-anaconda3

then setup conda:

conda init bash

this will modify your .bashrc, adding code that is specific to andes.

Then you do:

conda create -n andes_env -y ipykernel nb_conda_kernels
conda install -n andes_env -c conda-forge yt

Note

If you want to install yt from source, then you would first clone the yt repo:

git clone git@github.com:yt-project/yt

Then in the top-level yt/ directory, do:

pip install -e .

If you have an existing yt installation, you can uninstall it first via:

pip uninstall yt

Finally, you can activate the environment as:

source activate andes_env

Then you can run a python script that does visualization as with the following submission script:

#!/bin/bash
#SBATCH -A ast106
#SBATCH -J plots
#SBATCH -N 1
#SBATCH -t 2:00:00

cd $SLURM_SUBMIT_DIR

source activate andes_env

srun python vol-xrb-enuc.py flame_wave_1000Hz_25cm_smallplt203204

Here vol-xrb-enuc.py is the script with the yt code to make the visualization. This is then submitted to SLURM via sbatch.

Note

For very large plotfiles, it might run out of memory when doing the visualization. A solution is to use the gpu nodes on Andes, which have more memory. This is accomplished by adding #SBATCH -p gpu to the script.