43 lines
1.4 KiB
Bash
43 lines
1.4 KiB
Bash
#!/bin/sh
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#SBATCH -N 1 # nodes requested
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#SBATCH -n 1 # tasks requested
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#SBATCH --partition=Teach-Standard
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#SBATCH --gres=gpu:1
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#SBATCH --mem=12000 # memory in Mb
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#SBATCH --time=0-08:00:00
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export CUDA_HOME=/opt/cuda-9.0.176.1/
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export CUDNN_HOME=/opt/cuDNN-7.0/
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export STUDENT_ID=$(whoami)
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export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
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export LIBRARY_PATH=${CUDNN_HOME}/lib64:$LIBRARY_PATH
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export CPATH=${CUDNN_HOME}/include:$CPATH
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export PATH=${CUDA_HOME}/bin:${PATH}
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export PYTHON_PATH=$PATH
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mkdir -p /disk/scratch/${STUDENT_ID}
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export TMPDIR=/disk/scratch/${STUDENT_ID}/
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export TMP=/disk/scratch/${STUDENT_ID}/
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mkdir -p ${TMP}/datasets/
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export DATASET_DIR=${TMP}/datasets/
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# Activate the relevant virtual environment:
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source /home/${STUDENT_ID}/miniconda3/bin/activate mlp
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cd ..
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python train_evaluate_emnist_classification_system.py --batch_size 100 --continue_from_epoch -1 --seed 0 \
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--image_num_channels 3 --image_height 32 --image_width 32 \
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--dim_reduction_type "strided" --num_layers 4 --num_filters 64 \
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--num_epochs 100 --experiment_name 'cifar100_test_exp' \
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--use_gpu "True" --weight_decay_coefficient 0. \
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--dataset_name "cifar100" |