62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Basic loop for training."""
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from tensorflow.python.framework import errors
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from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=["train.basic_train_loop"])
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def basic_train_loop(supervisor,
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train_step_fn,
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args=None,
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kwargs=None,
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master=""):
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"""Basic loop to train a model.
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Calls `train_step_fn` in a loop to train a model. The function is called as:
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```python
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train_step_fn(session, *args, **kwargs)
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```
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It is passed a `tf.compat.v1.Session` in addition to `args` and `kwargs`. The
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function
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typically runs one training step in the session.
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Args:
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supervisor: `tf.compat.v1.train.Supervisor` to run the training services.
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train_step_fn: Callable to execute one training step. Called repeatedly as
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`train_step_fn(session, *args **kwargs)`.
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args: Optional positional arguments passed to `train_step_fn`.
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kwargs: Optional keyword arguments passed to `train_step_fn`.
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master: Master to use to create the training session. Defaults to `""`
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which causes the session to be created in the local process.
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"""
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if args is None:
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args = []
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if kwargs is None:
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kwargs = {}
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should_retry = True
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while should_retry:
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try:
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should_retry = False
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with supervisor.managed_session(master) as sess:
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while not supervisor.should_stop():
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train_step_fn(sess, *args, **kwargs)
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except errors.AbortedError:
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# Always re-run on AbortedError as it indicates a restart of one of the
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# distributed tensorflow servers.
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should_retry = True
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