tf.tpu.rewrite
Rewrites computation for execution on a TPU system.
tf.tpu.rewrite(
computation, inputs=None, infeed_queue=None, device_assignment=None, name=None
)
| Args | |
|---|---|
computation | A Python function that builds a computation to apply to the input. If the function takes n inputs, 'inputs' should be a list of n tensors.
All |
inputs | A list of input tensors or None (equivalent to an empty list). Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimention list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with tf.convert_to_tensor. |
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. May be omitted for a single-core computation, in which case the core attached to task 0, TPU device 0 is used. |
name | (Deprecated) Does nothing. |
| Returns | |
|---|---|
| Same data structure as if computation(*inputs) is called directly with some exceptions for correctness. Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/tpu/rewrite