Update docs

docs/conf.py

@@ -10,9 +10,6 @@ project = u'MiAI Compute Engine'
 author = u'%s Developers' % project
 copyright = u'2018, %s' % author
 
-version = u'0.6'
-release = u'0.6.0 alpha'
-
 source_parsers = {
     '.md': recommonmark.parser.CommonMarkParser,
 }

docs/development/adding_a_new_op.md

@@ -3,7 +3,7 @@ Adding a new Op
 
 You can create a custom op if it is not supported yet.
 
-To add a custom op, you need to finish the following steps.
+To add a custom op, you need to finish the following steps:
 
 Define the Op class
 --------------------

docs/development/contributing.md

@@ -34,7 +34,8 @@ clang-format -style="{BasedOnStyle: google,            \
 C++ logging guideline
 ---------------------
 
-The rule of VLOG level:
+VLOG is used for verbose logging, which is configured by environment variable
+`MACE_CPP_MIN_VLOG_LEVEL`. The guideline of VLOG level is as follows:
 
 ```
 0. Ad hoc debug logging, should only be added in test or temporary ad hoc

docs/faq.md

-Frequently Asked Questions
+Frequently asked questions
 ==========================
 
 Why is the generated static library file size so huge?

docs/getting_started/create_a_model_deployment.rst

-Create a model deployment
-=========================
+Create a model deployment file
+==============================
 
-Each YAML deployment script describes a case of deployments (for example,
-a smart camera application may contains face recognition, object recognition,
-and voice recognition models, which can be defined in one deployment file),
-which will generate one static library (if more than one ABIs specified,
-there will be one static library for each). Each YAML scripts can contains one
-or more models.
+The first step to deploy you models is to create a YAML model deployment
+file.
 
+One deployment file describes a case of model deployment,
+each file will generate one static library (if more than one ABIs specified,
+there will be one static library for each). The deployment file can contains
+one or more models, for example, a smart camera application may contains face
+recognition, object recognition, and voice recognition models, which can be
+defined in one deployment file),
 
-Model deployment file example
--------------------------------
-TODO: change to a link to a standalone file with comments.
 
-.. code:: yaml
+Example
+----------
+Here is an deployment file example used by Android demo application.
 
-    # 配置文件名会被用作生成库的名称：libmace-${filename}.a
-    target_abis: [armeabi-v7a, arm64-v8a]
-    # 具体机型的soc编号，可以使用`adb shell getprop | grep ro.board.platform | cut -d [ -f3 | cut -d ] -f1`获取
-    target_socs: [msm8998]
-    embed_model_data: 1
-    vlog_level: 0
-    models: # 一个配置文件可以包含多个模型的配置信息，最终生成的库中包含多个模型
-      first_net: # 模型的标签，在调度模型的时候，会用这个变量
-        platform: tensorflow
-        model_file_path: path/to/model64.pb # also support http:// and https://
-        model_sha256_checksum: 7f7462333406e7dea87222737590ebb7d94490194d2f21a7d72bafa87e64e9f9
-        input_nodes: input_node
-        output_nodes: output_node
-        input_shapes: 1,64,64,3
-        output_shapes: 1,64,64,2
-        runtime: gpu
-        limit_opencl_kernel_time: 0
-        dsp_mode: 0
-        obfuscate: 1
-        fast_conv: 0
-        input_files:
-          - path/to/input_files # support http://
-      second_net:
-        platform: caffe
-        model_file_path: path/to/model.prototxt
-        weight_file_path: path/to/weight.caffemodel
-        model_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
-        weight_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
-        input_nodes:
-          - input_node0
-          - input_node1
-        output_nodes:
-          - output_node0
-          - output_node1
-        input_shapes:
-          - 1,256,256,3
-          - 1,128,128,3
-        output_shapes:
-          - 1,256,256,2
-          - 1,1,1,2
-        runtime: cpu
-        limit_opencl_kernel_time: 1
-        dsp_mode: 0
-        obfuscate: 1
-        fast_conv: 0
-        input_files:
-          - path/to/input_files # support http://
+TODO: change this example file to the demo deployment file
+(reuse the same file) and rename to a reasonable name.
+
+.. literalinclude :: models/demo_app_models.yaml
+   :language: yaml
 
 Configurations
 --------------------

docs/getting_started/docker.md

+Docker Images
+=============
+
+* Login in [Xiaomi Docker Registry](http://docs.api.xiaomi.net/docker-registry/)
+
+```
+docker login cr.d.xiaomi.net
+```
+
+* Build with `Dockerfile`
+
+```
+docker build -t cr.d.xiaomi.net/mace/mace-dev
+```
+
+* Pull image from docker registry
+
+```
+docker pull cr.d.xiaomi.net/mace/mace-dev
+```
+
+* Create container
+
+```
+# Set 'host' network to use ADB
+docker run -it --rm -v /local/path:/container/path --net=host cr.d.xiaomi.net/mace/mace-dev /bin/bash
+```

docs/getting_started/docker.rst

-Docker Images
-=============
-
--  Login in `Xiaomi Docker
-   Registry <http://docs.api.xiaomi.net/docker-registry/>`__
-
-``docker login cr.d.xiaomi.net``
-
--  Build with ``Dockerfile``
-
-``docker build -t cr.d.xiaomi.net/mace/mace-dev .``
-
--  Pull image from docker registry
-
-``docker pull cr.d.xiaomi.net/mace/mace-dev``
-
--  Create container
-
-``# Set 'host' network to use ADB   docker run -it --rm -v /local/path:/container/path --net=host cr.d.xiaomi.net/mace/mace-dev /bin/bash``

docs/getting_started/models/demo_app_models.yaml

+# 配置文件名会被用作生成库的名称：libmace-${filename}.a
+target_abis: [armeabi-v7a, arm64-v8a]
+# 具体机型的soc编号，可以使用`adb shell getprop | grep ro.board.platform | cut -d [ -f3 | cut -d ] -f1`获取
+target_socs: [msm8998]
+embed_model_data: 1
+models: # 一个配置文件可以包含多个模型的配置信息，最终生成的库中包含多个模型
+  first_net: # 模型的标签，在调度模型的时候，会用这个变量
+    platform: tensorflow
+    model_file_path: path/to/model64.pb # also support http:// and https://
+    model_sha256_checksum: 7f7462333406e7dea87222737590ebb7d94490194d2f21a7d72bafa87e64e9f9
+    input_nodes: input_node
+    output_nodes: output_node
+    input_shapes: 1,64,64,3
+    output_shapes: 1,64,64,2
+    runtime: gpu
+    limit_opencl_kernel_time: 0
+    dsp_mode: 0
+    obfuscate: 1
+    fast_conv: 0
+    input_files:
+      - path/to/input_files # support http://
+  second_net:
+    platform: caffe
+    model_file_path: path/to/model.prototxt
+    weight_file_path: path/to/weight.caffemodel
+    model_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
+    weight_sha256_checksum: 05d92625809dc9edd6484882335c48c043397aed450a168d75eb8b538e86881a
+    input_nodes:
+      - input_node0
+      - input_node1
+    output_nodes:
+      - output_node0
+      - output_node1
+    input_shapes:
+      - 1,256,256,3
+      - 1,128,128,3
+    output_shapes:
+      - 1,256,256,2
+      - 1,1,1,2
+    runtime: cpu
+    limit_opencl_kernel_time: 1
+    dsp_mode: 0
+    obfuscate: 1
+    fast_conv: 0
+    input_files:
+      - path/to/input_files # support http://

docs/index.rst

@@ -11,8 +11,8 @@ The main documentation is organized into the following sections:
 
    getting_started/introduction
    getting_started/create_a_model_deployment
-   getting_started/how_to_build
    getting_started/docker
+   getting_started/how_to_build
    getting_started/op_lists
 
 .. toctree::