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5776efdb
编写于
5月 14, 2018
作者:
L
Liangliang He
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Add introduction into docs
上级
1f9b2ee2
变更
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并排
Showing
9 changed file
with
89 addition
and
37 deletion
+89
-37
.gitlab-ci.yml
.gitlab-ci.yml
+2
-1
docs/faq.md
docs/faq.md
+11
-0
docs/getting_started/docker.md
docs/getting_started/docker.md
+0
-27
docs/getting_started/how_to_build.rst
docs/getting_started/how_to_build.rst
+30
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docs/getting_started/introduction.md
docs/getting_started/introduction.md
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docs/getting_started/introduction.rst
docs/getting_started/introduction.rst
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docs/getting_started/mace-arch.png
docs/getting_started/mace-arch.png
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docs/getting_started/workflow.jpg
docs/getting_started/workflow.jpg
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docs/index.rst
docs/index.rst
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未找到文件。
.gitlab-ci.yml
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5776efdb
...
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@@ -25,7 +25,8 @@ docs:
-
cd docs
-
make html
-
CI_LATEST_OUTPUT_PATH=/mace-build-output/$CI_PROJECT_NAME/latest
-
CI_JOB_OUTPUT_PATH=/mace-build-output/$CI_PROJECT_NAME/$CI_BUILD_ID
-
CI_JOB_OUTPUT_PATH=/mace-build-output/$CI_PROJECT_NAME/$CI_PIPELINE_ID
-
rm -rf $CI_JOB_OUTPUT_PATH
-
mkdir -p $CI_JOB_OUTPUT_PATH
-
cp -r _build/html $CI_JOB_OUTPUT_PATH/docs
-
rm -rf $CI_LATEST_OUTPUT_PATH
...
...
docs/faq.md
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Frequently asked questions
==========================
Does the tensor data consume extra memory when compiled into C++ code?
----------------------------------------------------------------------
When compiled into C++ code, the data will be mmaped by the system loader.
For CPU runtime, the tensor data are used without memory copy.
For GPU and DSP runtime, the tensor data is used once during model
initialization. The operating system is free to swap the pages out, however,
it still consumes virtual memory space. So generally speaking, it takes
no extra physical memory. If you are short of virtual memory space (this
should be very rare), you can choose load the tensor data from a file, which
can be unmapped after initialization.
Why is the generated static library file size so huge?
-------------------------------------------------------
The static library is simply an archive of a set of object files which are
...
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docs/getting_started/docker.md
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100644 → 0
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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/how_to_build.rst
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...
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@@ -48,6 +48,36 @@ How to build
| docker(for caffe) | >= 17.09.0-ce | `install doc <https://docs.docker.com/install/linux/docker-ce/ubuntu/#set-up-the-repository>`__ |
+---------------------+-----------------+---------------------------------------------------------------------------------------------------+
Docker Images
----------------
* Login in `Xiaomi Docker Registry <http://docs.api.xiaomi.net/docker-registry/>`__
.. code:: sh
docker login cr.d.xiaomi.net
* Build with Dockerfile
.. code:: sh
docker build -t cr.d.xiaomi.net/mace/mace-dev
* Pull image from docker registry
.. code:: sh
docker pull cr.d.xiaomi.net/mace/mace-dev
* Create container
.. code:: sh
# 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
使用简介
--------
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docs/getting_started/introduction.md
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Introduction
============
TODO: describe the conceptions and workflow with diagram.
![
alt text
](
workflow.jpg
"MiAI workflow"
)
TODO: describe the runtime.
docs/getting_started/introduction.rst
0 → 100644
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Introduction
============
MiAI
Compute
Engine
is
a
deep
learning
inference
framework
optimized
for
mobile
heterogeneous
computing
platforms
.
The
following
figure
shows
the
overall
architecture
.
..
image
::
mace
-
arch
.
png
:
scale
:
40
%
:
align
:
center
Model
format
------------
MiAI
Compute
Engine
defines
a
customized
model
format
which
is
similar
to
Caffe2
.
The
MiAI
model
can
be
converted
from
exported
models
by
TensorFlow
and
Caffe
.
We
define
a
YAML
schema
to
describe
the
model
deployment
.
In
the
next
chapter
,
there
is
a
detailed
guide
showing
how
to
create
this
YAML
file
.
Model
conversion
----------------
Currently
,
we
provide
model
converters
for
TensorFlow
and
Caffe
.
And
more
frameworks
will
be
supported
in
the
future
.
Model
loading
-------------
The
MiAI
model
format
contains
two
parts
:
the
model
graph
definition
and
the
model
parameter
tensors
.
The
graph
part
utilizes
Protocol
Buffers
for
serialization
.
All
the
model
parameter
tensors
are
concatenated
together
into
a
continuous
array
,
and
we
call
this
array
tensor
data
in
the
following
paragraphs
.
In
the
model
graph
,
the
tensor
data
offsets
and
lengths
are
recorded
.
The
models
can
be
loaded
in
3
ways
:
1.
Both
model
graph
and
tensor
data
are
dynamically
loaded
externally
(
by
default
,
from
file
system
,
but
the
users
are
free
to
choose
their
own
implementations
,
for
example
,
with
compression
or
encryption
).
This
approach
provides
the
most
flexibility
but
the
weakest
model
protection
.
2.
Both
model
graph
and
tensor
data
are
converted
into
C
++
code
and
loaded
by
executing
the
compiled
code
.
This
approach
provides
the
strongest
model
protection
and
simplest
deployment
.
3.
The
model
graph
is
converted
into
C
++
code
and
constructed
as
the
second
approach
,
and
the
tensor
data
is
loaded
externally
as
the
first
approach
.
docs/getting_started/mace-arch.png
0 → 100644
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18.2 KB
docs/getting_started/workflow.jpg
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docs/index.rst
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...
...
@@ -11,7 +11,6 @@ The main documentation is organized into the following sections:
getting_started/introduction
getting_started/create_a_model_deployment
getting_started/docker
getting_started/how_to_build
getting_started/op_lists
...
...
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