tf.contrib.tpu.device_assignment

tf.contrib.tpu.device_assignment(
    topology,
    computation_shape=None,
    computation_stride=None,
    num_replicas=1
)

Defined in tensorflow/contrib/tpu/python/tpu/device_assignment.py.

Computes a device_assignment of a computation across a TPU topology.

Returns a DeviceAssignment that describes the cores in the topology assigned to each core of each replica.

computation_shape and computation_stride values should be powers of 2 for optimal packing.

Args:

• topology: A Topology object that describes the TPU cluster topology. To obtain a TPU topology, evaluate the Tensor returned by initialize_system using Session.run. Either a serialized TopologyProto or a Topology object may be passed. Note: you must evaluate the Tensor first; you cannot pass an unevaluated Tensor here.
• computation_shape: A rank 1 int32 numpy array of size 3, describing the shape of the computation's block of cores. If None, the computation_shape is [1, 1, 1].
• computation_stride: A rank 1 int32 numpy array of size 3, describing the inter-core spacing of the computation_shape cores in the TPU topology. If None, the computation_stride is [1, 1, 1].
• num_replicas: The number of computation replicas to run. The replicas will be packed into the free spaces of the topology.

Returns:

A DeviceAssignment object, which describes the mapping between the logical cores in each computation replica and the physical cores in the TPU topology.

Raises:

• ValueError: If topology is not a valid Topology object.
• ValueError: If computation_shape or computation_stride are not 1D int32 numpy arrays with shape [3] where all values are positive.
• ValueError: If computation's replicas cannot fit into the TPU topology.