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authorGravatar TensorFlower Gardener <gardener@tensorflow.org>2018-10-03 15:30:54 -0700
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2018-10-03 15:31:05 -0700
commitd0882f34a46f616b3cf5854ebecef1f21e161ef0 (patch)
tree7748b3acf2635c755ebb546166e22ba50688d8ac /tensorflow/contrib
parentefbee1ab2cac59f511cc0850d84414e711bbda3b (diff)
parentd0690d46466bf0393ad65544d1e8c55e948df133 (diff)
Merge pull request #22591 from EFanZh:fix-docs
PiperOrigin-RevId: 215639962
Diffstat (limited to 'tensorflow/contrib')
-rw-r--r--tensorflow/contrib/distribute/python/mirrored_strategy.py5
1 files changed, 3 insertions, 2 deletions
diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py
index 6bd380a22d..a32424b316 100644
--- a/tensorflow/contrib/distribute/python/mirrored_strategy.py
+++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py
@@ -318,12 +318,13 @@ class MirroredStrategy(distribute_lib.DistributionStrategy):
[TensorFlow's documentation](https://www.tensorflow.org/deploy/distributed).
The distribution strategy inherits these concepts as well and in addition to
that we also clarify several more concepts:
- * **In-graph replication**: the `client` creates a single `tf.Graph` that
+
+ * **In-graph replication**: the `client` creates a single `tf.Graph` that
specifies tasks for devices on all workers. The `client` then creates a
client session which will talk to the `master` service of a `worker`. Then
the `master` will partition the graph and distribute the work to all
participating workers.
- * **Worker**: A `worker` is a TensorFlow `task` that usually maps to one
+ * **Worker**: A `worker` is a TensorFlow `task` that usually maps to one
physical machine. We will have multiple `worker`s with different `task`
index. They all do similar things except for one worker checkpointing model
variables, writing summaries, etc. in addition to its ordinary work.