train_era5_template.sh

#!/bin/bash -x
#SBATCH --account=deepacf
#SBATCH --nodes=1
#SBATCH --ntasks=1
##SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --output=train_era5-out.%j
#SBATCH --error=train_era5-err.%j
#SBATCH --time=00:20:00
#SBATCH --gres=gpu:2
#SBATCH --partition=develgpus
#SBATCH --mail-type=ALL
#SBATCH --mail-user=b.gong@fz-juelich.de
##jutil env activate -p cjjsc42


# Name of virtual environment 
VIRT_ENV_NAME="vp"

# Loading mouldes
source ../env_setup/modules_train.sh
# Activate virtual environment if needed (and possible)
if [ -z ${VIRTUAL_ENV} ]; then
   if [[ -f ../${VIRT_ENV_NAME}/bin/activate ]]; then
      echo "Activating virtual environment..."
      source ../${VIRT_ENV_NAME}/bin/activate
   else 
      echo "ERROR: Requested virtual environment ${VIRT_ENV_NAME} not found..."
      exit 1
   fi
fi




# declare directory-variables which will be modified appropriately during Preprocessing (invoked by mpi_split_data_multi_years.py)
source_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/preprocessedData/
destination_dir=/p/project/deepacf/deeprain/video_prediction_shared_folder/models/

# for choosing the model for choosing the model, convLSTM,savp, mcnet,vae
model=convLSTM
model_hparams=../hparams/era5/${model}/model_hparams.json

# rund training
srun python ../scripts/train_dummy.py --input_dir  ${source_dir}/tfrecords/ --dataset era5  --model ${model} --model_hparams_dict ${model_hparams} --output_dir ${destination_dir}/${model}/