interpol_job

#!/bin/sh
#SBATCH --partition=cms-uhh             ## or allgpu / cms / cms-uhh / maxgpu
#SBATCH --time=24:00:00
#SBATCH --nodes=1
#SBATCH --job-name interpol_mpi           # give job unique name
#SBATCH --output ./interpol_log/%j-std.out      # terminal output
#SBATCH --error ./interpol_log/%j-std.err
#S BATCH --mail-type END
#S BATCH --mail-user lennart.kaemmle@desy.de
#SBATCH --constraint=GPU

# #SBATCH --nodelist=max-cmsg004         # you can select specific nodes, if necessary
#SBATCH --constraint="P100|V100"        # ask for specific types of GPUs

# (GPU drivers, anaconda, .bashrc, etc) --> likely not necessary if you're installing your own cudatoolkit & cuDNN versions
#source /etc/profile.d/modules.sh
#module load maxwell
#module load cuda
#module load anaconda/3
#module load mpi/mpich-x86_64
module load mpi/openmpi3-x86_64

. ~/.bashrc
export PATH="/home/kaemmlel/miniconda3/envs/torch/bin:$PATH"

set -x
# export PATH="/home/kaemmlel/miniconda3/envs/torch/bin:$PATH"
export PYTHONPATH=""  # openmpi module adds python 2 packages to pythonpath

# go to your folder with your python scripts
cd ~/Projects/amazing_ai

START=b
END=b

# run
MPI4PY_RC_THREADS=0 time mpirun --use-hwthread-cpus \
    python -m amazing_ai.scripts.interpol_images \
        --file-start @events_${START}_sr.h5 \
        --file-end @events_${END}_sr.h5 \
        -o @interpol_${START}${END}{flags}_{n_starts}_{n_per_start}.h5 \
        --steps 100 \
        -n 6000 \
        --n-per-start 100 \
        --blur \
        --mpi