tf.contrib.tpu.batch_parallel

tf.contrib.tpu.batch_parallel(
    computation,
    inputs=None,
    num_shards=1,
    infeed_queue=None,
    device_assignment=None,
    name=None
)

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

Shards 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 computation. e.g.,

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.

Args:

  • 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.
  • num_shards: The number of shards.
  • infeed_queue: If not None, the InfeedQueue from which to append a tuple of arguments as inputs to computation.
  • device_assignment: If not None, a DeviceAssignment describing the mapping between logical cores in the computation with physical cores in the TPU topology. Uses a default device assignment if None. The DeviceAssignment may 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.

Returns:

A list of output tensors.

Raises:

  • ValueError: If num_shards <= 0