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Cluster and distribution strategy configuration

By default, run API takes care of wrapping your model code in a TensorFlow distribution strategy based on the cluster configuration you have provided.

No distribution

CPU chief config and no additional workers

tfc.run(entry_point='mnist_example.py',
        chief_config=tfc.COMMON_MACHINE_CONFIGS['CPU'])

OneDeviceStrategy

1 GPU on chief (defaults to AcceleratorType.NVIDIA_TESLA_T4) and no additional workers.

tfc.run(entry_point='mnist_example.py')

MirroredStrategy

Chief config with multiple GPUs (AcceleratorType.NVIDIA_TESLA_V100).

tfc.run(entry_point='mnist_example.py',
        chief_config=tfc.COMMON_MACHINE_CONFIGS['V100_4X'])

MultiWorkerMirroredStrategy

Chief config with 1 GPU and 2 workers each with 8 GPUs (AcceleratorType.NVIDIA_TESLA_V100).

tfc.run(entry_point='mnist_example.py',
        chief_config=tfc.COMMON_MACHINE_CONFIGS['V100_1X'],
        worker_count=2,
        worker_config=tfc.COMMON_MACHINE_CONFIGS['V100_8X'])

TPUStrategy

Chief config with 1 CPU and 1 worker with TPU.

tfc.run(entry_point="mnist_example.py",
        chief_config=tfc.COMMON_MACHINE_CONFIGS["CPU"],
        worker_count=1,
        worker_config=tfc.COMMON_MACHINE_CONFIGS["TPU"])

Please note that TPUStrategy with TensorFlow Cloud works only with TF version 2.1 as this is the latest version supported by AI Platform cloud TPU

Custom distribution strategy

If you would like to take care of specifying a distribution strategy in your model code and do not want run API to create a strategy, then set distribution_stategy as None. This will be required, for example, when you are using strategy.experimental_distribute_dataset.

tfc.run(entry_point='mnist_example.py',
        distribution_strategy=None,
        worker_count=2)