[singa-doc] 02/02: fix conflict

[wa...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

wangwei pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/singa-doc.git

commit 0a2fc3f353accfed81f616a2b133679bea17f5de
Merge: 855b4b0 108f126
Author: wang wei <wa...@gmail.com>
AuthorDate: Wed Oct 21 10:28:01 2020 +0800

    fix conflict

 docs-site/docs/autograd.md        |  2 +-
 docs-site/docs/benchmark-train.md |  4 ++--
 docs-site/docs/build.md           |  2 +-
 docs-site/docs/graph.md           |  4 ++--
 docs-site/docs/install-win.md     | 28 ++++++++++++++--------------
 docs-site/docs/installation.md    |  2 +-
 docs-site/docs/software-stack.md  | 21 +++++++++++----------
 7 files changed, 32 insertions(+), 31 deletions(-)

diff --cc docs-site/docs/software-stack.md
index 05d8208,97c215b..f83453a
--- a/docs-site/docs/software-stack.md
+++ b/docs-site/docs/software-stack.md
@@@ -7,12 -7,12 +7,12 @@@ title: Software Stac
  
  SINGA's software stack includes two major levels, the low level backend classes
  and the Python interface level. Figure 1 illustrates them together with the
- hardware. The backend components provides basic data structures for deep
 -hardware. The backend components provide basic data structures for deep
--learning models, hardware abstractions for scheduling and executing operations,
--and communication components for distributed training. The Python interface
--wraps some CPP data structures and provides additional high-level classes for
--neural network training, which makes it convenient to implement complex neural
--network models.
++hardware. The backend components provide basic data structures for deep learning
++models, hardware abstractions for scheduling and executing operations, and
++communication components for distributed training. The Python interface wraps
++some CPP data structures and provides additional high-level classes for neural
++network training, which makes it convenient to implement complex neural network
++models.
  
  Next, we introduce the software stack in a bottom-up manner.
  
@@@ -72,7 -72,7 +72,8 @@@ The neural network specific operators a
  Typically, users create a `Device` instance and use it to create multiple
  `Tensor` instances. When users call the Tensor functions or neural network
  operations, the corresponding implementation for the resident device will be
- invoked In other words, the implementation of operators is transparent to users.
 -invoked. In other words, the implementation of operators is transparent to users.
++invoked. In other words, the implementation of operators is transparent to
++users.
  
  The Tensor and Device abstractions are extensible to support a wide range of
  hardware device using different programming languages. A new hardware device