tf.contrib.tpu.batch_parallel( computation, inputs=None, num_shards=1, infeed_queue=None, device_assignment=None, name=None )
computation along the batch dimension for parallel execution.
Convenience wrapper around shard().
inputs must be a list of Tensors or None (equivalent to an empty
list). Each input is split into
num_shards pieces along the 0-th
dimension, and computation is applied to each shard in parallel.
Tensors are broadcast to all shards if they are lexically captured by
x = tf.constant(7) def computation(): return x + 3 ... = shard(computation, ...)
The outputs from all shards are concatenated back together along their 0-th dimension.
Inputs and outputs of the computation must be at least rank-1 Tensors.
computation: A Python function that builds a computation to apply to each shard of the input.
inputs: A list of input tensors or None (equivalent to an empty list). The 0-th dimension of each Tensor must have size divisible by
num_shards: The number of shards.
infeed_queue: If not
InfeedQueuefrom which to append a tuple of arguments as inputs to
device_assignment: If not
DeviceAssignmentdescribing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if
DeviceAssignmentmay be omitted if each shard of the computation uses only one core, and there is either only one shard, or the number of shards is equal to the number of cores in the TPU system.
name: (Deprecated) Does nothing.
A list of output tensors.
num_shards <= 0