dl_benchmark/task_config_template.cfg at develop · zhreshold/dl_benchmark · GitHub

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[resnet50_cifar10_symbolic]
patterns = ['Speed: (\d+\.\d+|\d+) samples/sec', 'Train-accuracy=(\d+\.\d+|\d+)', 'Time cost=(\d+\.\d+|\d+)']
metrics = ['speed', 'training_acc', 'total_training_time']
compute_method = ['average', 'last', 'total']
command_to_execute = python image_classification/image_classification.py --model resnet50_v1 --dataset cifar10 --mode symbolic --gpus 8 --epochs 20 --log-interval 50
num_gpus = 8

[resnet50_cifar10_hybrid]
patterns = ['Speed: (\d+\.\d+|\d+) samples/sec', 'training: accuracy=(\d+\.\d+|\d+)', 'validation: accuracy=(\d+\.\d+|\d+)', 'time cost: (\d+\.\d+|\d+)']
metrics = ['speed', 'training_acc', 'validation_acc', 'total_training_time']
compute_method = ['average', 'last', 'last', 'total']
command_to_execute = python image_classification/image_classification.py --model resnet50_v1 --dataset cifar10 --mode hybrid --gpus 8 --epochs 20 --log-interval 50
num_gpus = 8

[resnet50_cifar10_imperative]
patterns = ['Speed: (\d+\.\d+|\d+) samples/sec', 'training: accuracy=(\d+\.\d+|\d+)', 'validation: accuracy=(\d+\.\d+|\d+)', 'time cost: (\d+\.\d+|\d+)']
metrics = ['speed', 'training_acc', 'validation_acc', 'total_training_time']
compute_method = ['average', 'last', 'last', 'total']
command_to_execute = python image_classification/image_classification.py --model resnet50_v1 --dataset cifar10 --gpus 8 --epochs 20 --log-interval 50
num_gpus = 8

[lstm_ptb_imperative]
patterns = ['time cost (\d+\.\d+|\d+)', 'valid loss (\d+\.\d+|\d+)', 'valid ppl (\d+\.\d+|\d+)', 'test loss (\d+\.\d+|\d+)', 'test ppl (\d+\.\d+|\d+)']
metrics = ['total_training_time', 'validation_loss', 'validation_perplexity', 'test_loss', 'test_perplexity']
compute_method = ['total', 'last', 'last', 'last', 'last']
command_to_execute = python word_language_model/word_language_model.py --gpus 8 --nhid 650 --emsize 650 --dropout 0.5 --epochs 40 --data word_language_model/data/ptb. --mode imperative
num_gpus = 8

[lstm_ptb_hybrid]
patterns = ['time cost (\d+\.\d+|\d+)', 'valid loss (\d+\.\d+|\d+)', 'valid ppl (\d+\.\d+|\d+)', 'test loss (\d+\.\d+|\d+)', 'test ppl (\d+\.\d+|\d+)']
metrics = ['total_training_time', 'validation_loss', 'validation_perplexity', 'test_loss', 'test_perplexity']
compute_method = ['total', 'last', 'last', 'last', 'last']
command_to_execute = python word_language_model/word_language_model.py --gpus 8 --nhid 650 --emsize 650 --dropout 0.5 --epochs 40 --data word_language_model/data/ptb. --mode hybrid
num_gpus = 8

[lstm_ptb_symbolic]
patterns = ['Time cost=(\d+\.\d+|\d+)', 'Train-perplexity=(\d+\.\d+|\d+)', 'Validation-perplexity=(\d+\.\d+|\d+)', 'Speed: (\d+\.\d+|\d+) samples/sec']
metrics = ['total_training_time', 'train_perplexity', 'validation_perplexity', 'speed']
compute_method = ['total', 'last', 'last', 'average']
command_to_execute = python word_language_model/lstm_bucketing.py --num-hidden 650 --num-embed 650 --gpus 8 --epochs 25
num_gpus = 8