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Grammar fixes on architecture.md (#19035)

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ctiijima 2018-05-03 08:34:07 -07:00 committed by Shanqing Cai
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tensorflow/docs_src/extend/architecture.md
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@ -4,8 +4,8 @@ We designed TensorFlow for large-scale distributed training and inference, but
it is also flexible enough to support experimentation with new machine
learning models and system-level optimizations.
This document describes the system architecture that makes possible this
combination of scale and flexibility. It assumes that you have basic familiarity
This document describes the system architecture that makes this
combination of scale and flexibility possible. It assumes that you have basic familiarity
with TensorFlow programming concepts such as the computation graph, operations,
and sessions. See @{$programmers_guide/low_level_intro$this document}
for an introduction to these topics. Some familiarity
@ -15,8 +15,8 @@ will also be helpful.
This document is for developers who want to extend TensorFlow in some way not
supported by current APIs, hardware engineers who want to optimize for
TensorFlow, implementers of machine learning systems working on scaling and
distribution, or anyone who wants to look under Tensorflow's hood. After
reading it you should understand TensorFlow architecture well enough to read
distribution, or anyone who wants to look under Tensorflow's hood. By the end of this document
you should understand the TensorFlow architecture well enough to read
and modify the core TensorFlow code.
## Overview
@ -35,7 +35,7 @@ This document focuses on the following layers:
* **Client**:
* Defines the computation as a dataflow graph.
* Initiates graph execution using a [**session**](
https://www.tensorflow.org/code/tensorflow/python/client/session.py)
https://www.tensorflow.org/code/tensorflow/python/client/session.py).
* **Distributed Master**
* Prunes a specific subgraph from the graph, as defined by the arguments
to Session.run().
@ -55,7 +55,7 @@ Figure 2 illustrates the interaction of these components. "/job:worker/task:0" a
server": a task responsible for storing and updating the model's parameters.
Other tasks send updates to these parameters as they work on optimizing the
parameters. This particular division of labor between tasks is not required, but
it is common for distributed training.
is common for distributed training.
![TensorFlow Architecture Diagram](https://www.tensorflow.org/images/diag1.svg){: width="500"}
@ -193,7 +193,7 @@ https://www.tensorflow.org/code/tensorflow/contrib/nccl/python/ops/nccl_ops.py))
## Kernel Implementations
The runtime contains over 200 standard operations, including mathematical, array
The runtime contains over 200 standard operations including mathematical, array
manipulation, control flow, and state management operations. Each of these
operations can have kernel implementations optimized for a variety of devices.
Many of the operation kernels are implemented using Eigen::Tensor, which uses