mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
Changes since Teledyne
This commit is contained in:
Antoine Harlé
parent
03ffd7fe05
commit
b89dac9084
185 changed files with 16668 additions and 484 deletions
34
jobs/benchmark.sh
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34
jobs/benchmark.sh
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#!/bin/bash
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#SBATCH --gres=gpu:v100l:1 # https://docs.computecanada.ca/wiki/Using_GPUs_with_Slurm
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#SBATCH --cpus-per-task=6 #6 # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M #32000M # Memory proportional to CPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=20:00:00
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#SBATCH --job-name=Benchmark
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#SBATCH --output=log/%x-%j.out
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#SBATCH --mail-user=harle.collette.antoine@gmail.com
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#SBATCH --mail-type=END
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#SBATCH --mail-type=FAIL
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#SBATCH --array=1-3
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# Setup
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source ~/dataug/bin/activate
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#Execute
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# echo $(pwd) = /home/antoh/projects/def-mpederso/antoh/smart_augmentation/jobs
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cd ../higher/smart_aug/
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python test_dataug.py \
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-a true \
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-m resnet18 \
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-ep 200 \
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-K 1 \
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-N 3 \
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-tfc ../config/invScale_wide_tf_config2.json \
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-ds CIFAR10 \
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-al 10 \
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-wu 2 \
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-rf '../res/benchmark/' \
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-pf __$SLURM_ARRAY_TASK_ID
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24
jobs/classic.sh
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24
jobs/classic.sh
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#!/bin/bash
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#SBATCH --gres=gpu:1 #gpu:v100l:1 # https://docs.computecanada.ca/wiki/Using_GPUs_with_Slurm
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#SBATCH --cpus-per-task=6 #6 (V100:8) # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M #32000M # Memory proportional to CPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=8:00:00
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#SBATCH --job-name=Classic-WRN50_2-TinyImageNet
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#SBATCH --output=log/%x-%j.out
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#SBATCH --mail-user=harle.collette.antoine@gmail.com
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#SBATCH --mail-type=END
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#SBATCH --mail-type=FAIL
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# Setup
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source ~/dataug/bin/activate
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#Execute
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# echo $(pwd) = /home/antoh/projects/def-mpederso/antoh/smart_augmentation/jobs
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cd ../higher/smart_aug/
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python test_dataug.py \
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-m wide_resnet50_2 \
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-ep 40 \
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-pt true \
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-pf __pretrained
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27
jobs/cpu_test.sh
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27
jobs/cpu_test.sh
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#!/bin/bash
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#SBATCH --cpus-per-task=6 #6 # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M #32000M # Memory proportional to CPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=01:00:00
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#SBATCH --job-name=test_dataug
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#SBATCH --output=log/%x-%j.out
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# Setup
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source ~/dataug/bin/activate
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#Execute
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# echo $(pwd) = /home/antoh/projects/def-mpederso/antoh/smart_augmentation/jobs
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cd ../higher/smart_aug/
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python test_dataug.py \
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-dv 'cpu' \
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-a true \
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-m LeNet \
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-ep 2 \
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-K 1 \
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-N 3 \
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-tfc ../config/wide_geom_tf_config.json \
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-ms 1 \
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-ls true \
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-pf __test
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30
jobs/dataug.sh
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30
jobs/dataug.sh
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#!/bin/bash
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#SBATCH --gres=gpu:v100l:1 # https://docs.computecanada.ca/wiki/Using_GPUs_with_Slurm
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#SBATCH --cpus-per-task=6 #6 # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M # Memory proportional to CPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=10:00:00
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#SBATCH --job-name=Dataug-ResNet18
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# --exclusive #In case of Out of Memory ?
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#SBATCH --output=log/%x-%j.out
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#SBATCH --mail-user=harle.collette.antoine@gmail.com
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#SBATCH --mail-type=END
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#SBATCH --mail-type=FAIL
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# Setup
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source ~/dataug/bin/activate
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#Execute
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# echo $(pwd) = /home/antoh/projects/def-mpederso/antoh/smart_augmentation/jobs
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cd ../higher/smart_aug/
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python test_dataug.py \
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-a true \
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-m resnet18 \
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-ep 50 \
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-K 1 \
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-N 3 \
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-tfc ../config/invScale_wide_tf_config.json \
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-ds CIFAR10 \
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-al 1 \
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-pf __test_noCopy
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37
jobs/imageNet_job.sh
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37
jobs/imageNet_job.sh
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#!/bin/bash
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#SBATCH --gres=gpu:v100l:1 # https://docs.computecanada.ca/wiki/Using_GPUs_with_Slurm
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#SBATCH --cpus-per-task=6 # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M # Memory proportional to GPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=300:00:00
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#SBATCH --job-name=imageNet-RandAug-ResNet18
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#SBATCH --output=log/%x-%j.out
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#SBATCH --mail-user=harle.collette.antoine@gmail.com
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#SBATCH --mail-type=END
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#SBATCH --mail-type=FAIL
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#ImageNet Extract
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echo "Extracting ImageNet..."
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mkdir $SLURM_TMPDIR/data
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time tar -xf ~/projects/def-mpederso/dataset/imagenet/imagenet.tar -C $SLURM_TMPDIR/data
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echo "Executing code..."
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# Setup
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source ~/dataug/bin/activate
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#Execute
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# echo $(pwd) = /home/antoh/projects/def-mpederso/antoh/smart_augmentation/jobs
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cd ../higher/smart_aug/
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time python test_dataug.py \
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-a true \
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-m resnet18 \
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-ep 40 \
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-K 0 \
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-N 3 \
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-tfc ../config/invScale_wide_tf_config.json \
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-dr $SLURM_TMPDIR/data \
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-ds ImageNet \
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-al 10 \
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-pt true \
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-pf __pretrained_AL10
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30
jobs/randaug.sh
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30
jobs/randaug.sh
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#!/bin/bash
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#SBATCH --gres=gpu:1 #v100l:1 # https://docs.computecanada.ca/wiki/Using_GPUs_with_Slurm
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#SBATCH --cpus-per-task=6 #6 # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M #32000M # Memory proportional to CPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=10:00:00
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#SBATCH --job-name=RandAug-ResNet18
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#SBATCH --output=log/%x-%j.out
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#SBATCH --mail-user=harle.collette.antoine@gmail.com
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#SBATCH --mail-type=END
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#SBATCH --mail-type=FAIL
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# Setup
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source ~/dataug/bin/activate
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#Execute
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# echo $(pwd) = /home/antoh/projects/def-mpederso/antoh/smart_augmentation/jobs
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cd ../higher/smart_aug/
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python test_dataug.py \
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-a true \
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-m resnet18 \
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-ep 200 \
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-K 0 \
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-N 3 \
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-tfc ../config/invScale_wide_tf_config.json \
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-ds CIFAR10 \
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-al 10 \
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-dt FP16 \
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-pf __AL10_FP16
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30
jobs/setup_env.sh
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30
jobs/setup_env.sh
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#!/bin/bash
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#SBATCH --gres=gpu:1 # Request GPU "generic resources"
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#SBATCH --cpus-per-task=6 # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M # Memory proportional to GPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=00:03:00
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#SBATCH --job-name=setup
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#SBATCH --output=log/%x-%j.out
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# Setup
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#sudo rm -rf ~/dataug/
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module load python/3.7.4
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virtualenv --no-download ~/dataug
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source ~/dataug/bin/activate
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pip install --no-index --upgrade pip
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pip install --no-index torch torchvision #torchviz
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pip install kornia # --no-deps ?
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git clone https://github.com/facebookresearch/higher.git
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cd higher
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pip install .
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cd ..
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pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git
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pip install efficientnet_pytorch
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pip install --no-index matplotlib scipy
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#
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25
jobs/test.sh
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25
jobs/test.sh
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#!/bin/bash
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#SBATCH --gres=gpu:1 #:v100l:1 # https://docs.computecanada.ca/wiki/Using_GPUs_with_Slurm
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#SBATCH --cpus-per-task=6 #6 # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
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#SBATCH --mem=32000M #32000M # Memory proportional to CPUs: 32000 Cedar, 64000 Graham.
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#SBATCH --account=def-mpederso
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#SBATCH --time=01:00:00
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#SBATCH --job-name=test_dataug
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#SBATCH --output=log/%x-%j.out
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# Setup
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source ~/dataug/bin/activate
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#Execute
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# echo $(pwd) = /home/antoh/projects/def-mpederso/antoh/smart_augmentation/jobs
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cd ../higher/smart_aug/
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python test_dataug.py \
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-a true \
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-m LeNet \
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-ep 20 \
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-K 1 \
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-N 3 \
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-tfc ../config/wide_geom_tf_config.json \
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-pf __test_LossFix
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