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How to run this benchmark
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Requirements: Python (tested with python3) with tensorflow-gpu (or tensoflow at least) and keras modules
Step 2: modify config.sh in order to have appropriate environment variables that fit your infrastructure
Step 3: run the benchmark with this command
./run.sh <path_to_config_file>
As CIFAR10 is a very common dataset, Keras has a built-in, ready to use function to download it (in ~/.keras/datasets) and create train and test sets:
from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
outputs:
- model (located in CIFAR10_PATH_SAVED_MODELS)
- logs (located in CIFAR10_PATH_TENSORBOARD_LOGS)
To view logs, run this command:
tensorboard --logdir <CIFAR10_PATH_TENSORBOARD_LOGS>/gpus<CIFAR10_NUM_GPUS>_cores<CIFAR10_NUM_CORES>_batchsize<CIFAR10_BATCHSIZE>_<year>_<month>_<day>/
This command will start a local web server and provide beautiful graphs and diagrams through html pages