| # Copyright 2018 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 configure tuple for high-level APIs for running distribution strategies.""" |
| |
| import collections |
| |
| |
| class DistributeConfig( |
| collections.namedtuple( |
| 'DistributeConfig', |
| ['train_distribute', 'eval_distribute', 'remote_cluster'])): |
| """A config tuple for distribution strategies. |
| |
| Attributes: |
| train_distribute: a `DistributionStrategy` object for training. |
| eval_distribute: an optional `DistributionStrategy` object for |
| evaluation. |
| remote_cluster: a dict, `ClusterDef` or `ClusterSpec` object specifying |
| the cluster configurations. If this is given, the `train_and_evaluate` |
| method will be running as a standalone client which connects to the |
| cluster for training. |
| """ |
| |
| def __new__(cls, |
| train_distribute=None, |
| eval_distribute=None, |
| remote_cluster=None): |
| return super(DistributeConfig, cls).__new__(cls, train_distribute, |
| eval_distribute, remote_cluster) |