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-rw-r--r--tensorflow/contrib/distribute/python/multi_worker_strategy.py2
1 files changed, 1 insertions, 1 deletions
diff --git a/tensorflow/contrib/distribute/python/multi_worker_strategy.py b/tensorflow/contrib/distribute/python/multi_worker_strategy.py
index 0f21a42732..cbfe5df61d 100644
--- a/tensorflow/contrib/distribute/python/multi_worker_strategy.py
+++ b/tensorflow/contrib/distribute/python/multi_worker_strategy.py
@@ -46,7 +46,7 @@ class MultiWorkerMirroredStrategy(MirroredStrategy):
* **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 parition the graph and distribute the work to all
+ 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
physical machine. We will have multiple `worker`s with different `task`