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main.slurm
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main.slurm
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#!/bin/bash -l
#SBATCH --account=yrf@gpu
#SBATCH --nodes=1
#SBATCH --gres=gpu:4 # nombre de GPU à réserver (un unique GPU ici)
#SBATCH --ntasks-per-node=4 # nombre de coeurs à réserver (un quart du noeud)
#SBATCH --cpus-per-task=10
#SBATCH --time=00:10:00
#SBATCH --hint=nomultithread
#SBATCH -C v100-32g
#SBATCH --qos=qos_gpu-t3
#SBATCH --output=log%j.out
#SBATCH --error=log%j.err
# activate conda env
#source activate $1
module purge
# chargement des modules
eval "$(conda shell.bash hook)"
conda activate 4dvarnet
export PYTHONPATH=${WORK}/4dvarnet-core:${PYTHONPATH}
# run script from above
# config files stored in config_q --> examples: --config=q.nad_swot
# train the model
srun python main.py --config=$1 --max_epochs=20 --progress_bar_refresh_rate=5 run
# test the model
#srun python main.py --config=$1 --ckpt_path=checkpoints/GF_OSE_OSSE.ckpt --progress_bar_refresh_rate=5 test