#!/bin/sh
#SBATCH -N 1	  # nodes requested
#SBATCH -n 1	  # tasks requested
#SBATCH --partition=Teach-Standard
#SBATCH --gres=gpu:1
#SBATCH --mem=12000  # memory in Mb
#SBATCH --time=0-08:00:00

export CUDA_HOME=/opt/cuda-9.0.176.1/

export CUDNN_HOME=/opt/cuDNN-7.0/

export STUDENT_ID=$(whoami)

export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:$LD_LIBRARY_PATH

export LIBRARY_PATH=${CUDNN_HOME}/lib64:$LIBRARY_PATH

export CPATH=${CUDNN_HOME}/include:$CPATH

export PATH=${CUDA_HOME}/bin:${PATH}

export PYTHON_PATH=$PATH

mkdir -p /disk/scratch/${STUDENT_ID}


export TMPDIR=/disk/scratch/${STUDENT_ID}/
export TMP=/disk/scratch/${STUDENT_ID}/

mkdir -p ${TMP}/datasets/
export DATASET_DIR=${TMP}/datasets/
# Activate the relevant virtual environment:


source /home/${STUDENT_ID}/miniconda3/bin/activate mlp
cd ..
python train_evaluate_emnist_classification_system.py --batch_size 100 --continue_from_epoch -1 --seed 0 \
                                                      --image_num_channels 1 --image_height 28 --image_width 28 \
                                                      --dim_reduction_type "strided" --num_layers 4 --num_filters 64 \
                                                      --num_epochs 100 --experiment_name 'emnist_test_exp' \
                                                      --use_gpu "True" --weight_decay_coefficient 0. \
                                                      --dataset_name "emnist"