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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===================================================================
"""A RunConfig subclass with TPU support."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from tensorflow.contrib.tpu.python.tpu import util as util_lib
from tensorflow.python.estimator import run_config as run_config_lib
class TPUConfig(
collections.namedtuple('TPUConfig', [
'iterations_per_loop',
'num_shards',
'per_host_input_for_training',
'tpu_job_name',
])):
"""TPU related configuration required by `TPUEstimator`.
Args:
iterations_per_loop: This is the number of train steps runnining in TPU
system before returning to CPU host for each `Session.run`. This means
global step is increased `iterations_per_loop` times in one `Session.run`.
It is recommended to be set as number of global steps for next checkpoint.
num_shards: The number of TPU shards in the system.
per_host_input_for_training: If `True`, `input_fn` is invoked Per-Host
rather than Per-Core. With Per-Host input pipeline deployment, `input_fn`
is invoked once on each host. To be precise, with a global batch size
`train_batch_size` in `TPUEstimator` constructor, the batch size for each
shard is `train_batch_size` // #hosts. With Per-Core input pipeline
deployment, the shard batch size is `train_batch_size` // #cores.
tpu_job_name: The name of the TPU job. Typically, this name is auto-inferred
within TPUEstimator, however when using ClusterSpec propagation in more
esoteric cluster configurations, you may need to specify the job name as a
string.
"""
def __new__(cls,
iterations_per_loop=2,
num_shards=2,
per_host_input_for_training=True,
tpu_job_name=None):
# Check iterations_per_loop.
util_lib.check_positive_integer(iterations_per_loop,
'TPUConfig iterations_per_loop')
# Check num_shards.
util_lib.check_positive_integer(num_shards, 'TPUConfig num_shards')
return super(TPUConfig, cls).__new__(
cls,
iterations_per_loop=iterations_per_loop,
num_shards=num_shards,
per_host_input_for_training=per_host_input_for_training,
tpu_job_name=tpu_job_name)
class RunConfig(run_config_lib.RunConfig):
"""RunConfig with TPU support."""
def __init__(self, tpu_config=None, evaluation_master=None, master='',
**kwargs):
"""Constructs a RunConfig.
Args:
tpu_config: the TPUConfig that specifies TPU-specific configuration.
evaluation_master: a string. The address of the master to use for eval.
Defaults to master if not set.
master: a string. The address of the master to use for training.
tf_random_seed: an int. Sets the TensorFlow random seed. Defaults to None,
which initializes it randomly based on the environment.
"""
super(RunConfig, self).__init__(**kwargs)
self._tpu_config = tpu_config or TPUConfig()
if evaluation_master is None:
self._evaluation_master = master
else:
self._evaluation_master = evaluation_master
self._master = master
@property
def evaluation_master(self):
return self._evaluation_master
@property
def master(self):
return self._master
@property
def tpu_config(self):
return self._tpu_config
def replace(self, **kwargs):
if 'tpu_config' not in kwargs:
return super(RunConfig, self).replace(**kwargs)
tpu_config = kwargs.pop('tpu_config')
new_instance = super(RunConfig, self).replace(**kwargs)
new_instance._tpu_config = tpu_config # pylint: disable=protected-access
return new_instance
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