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new dd1004b fix pi instructions (#14746)
dd1004b is described below
commit dd1004bb02deada90060b6c23a606b6378a54c50
Author: Aaron Markham <[email protected]>
AuthorDate: Sat Apr 20 18:33:15 2019 -0700
fix pi instructions (#14746)
---
docs/install/index.md | 35 ++++++++++++++++++++++-------------
1 file changed, 22 insertions(+), 13 deletions(-)
diff --git a/docs/install/index.md b/docs/install/index.md
index 5708a5b..10db8d9 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -1233,9 +1233,7 @@ You can do a dockerized cross compilation build on your
local machine or a nativ
The complete MXNet library and its requirements can take almost 200MB of RAM,
and loading large models with the library can take over 1GB of RAM. Because of
this, we recommend running MXNet on the Raspberry Pi 3 or an equivalent device
that has more than 1 GB of RAM and a Secure Digital (SD) card that has at least
4 GB of free memory.
## Quick installation
-You can use this [pre-built Python wheel](wget
https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl)
on a Raspberry Pi 3B with Stretch. You will likely need to install several
dependencies to get MXNet to work. Refer to the following **Build** section for
details.
-
-**Cross compilation build (Experimental)**
+You can use this [pre-built Python
wheel](https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl)
on a Raspberry Pi 3B with Stretch. You will likely need to install several
dependencies to get MXNet to work. Refer to the following **Build** section for
details.
## Docker installation
**Step 1** Install Docker on your machine by following the [docker
installation
instructions](https://docs.docker.com/engine/installation/linux/ubuntu/#install-using-the-repository).
@@ -1248,18 +1246,22 @@ Follow the four steps in this [docker
documentation](https://docs.docker.com/eng
## Build
-**Please use a Native build with gcc 4 as explained below, higher compiler
versions currently cause test
-failures on ARM**
+**This cross compilation build is experimental.**
+
+**Please use a Native build with gcc 4 as explained below, higher compiler
versions currently cause test failures on ARM.**
-The following command will build a container with dependencies and tools and
then compile MXNet for
-ARMv7. The resulting artifact will be located in
`build/mxnet-x.x.x-py2.py3-none-any.whl`, copy this
-file to your Raspberry Pi.
+The following command will build a container with dependencies and tools,
+and then compile MXNet for ARMv7.
+You will want to run this on a fast cloud instance or locally on a fast PC to
save time.
+The resulting artifact will be located in
`build/mxnet-x.x.x-py2.py3-none-any.whl`.
+Copy this file to your Raspberry Pi.
+The previously mentioned pre-built wheel was created using this method.
```
ci/build.py -p armv7
```
-## Install
+## Install using a pip wheel
Your Pi will need several dependencies.
@@ -1282,6 +1284,7 @@ sudo apt-get install -y \
libzmq3-dev \
ninja-build \
python-dev \
+ python-pip \
software-properties-common \
sudo \
unzip \
@@ -1298,18 +1301,24 @@ virtualenv -p `which python` mxnet_py27
```
You may use Python 3, however the [wine bottle detection
example](https://mxnet.incubator.apache.org/versions/master/tutorials/embedded/wine_detector.html)
for the Pi with camera requires Python 2.7.
-Create a virtualenv and install the wheel we created previously, or the wheel
that you downloaded.
+Activate the environment, then install the wheel we created previously, or
install this [prebuilt
wheel](https://mxnet-public.s3.amazonaws.com/install/raspbian/mxnet-1.5.0-py2.py3-none-any.whl).
```
-virtualenv -p `which python3` mxnet_py27
source mxnet_py27/bin/activate
pip install mxnet-x.x.x-py2.py3-none-any.whl
```
+Test MXNet with the Python interpreter:
+```
+$ python
+
+>>> import mxnet
+```
+If there are no errors then you're ready to start using MXNet on your Pi!
-**Native Build**
+## Native Build
-Installing MXNet is a two-step process:
+Installing MXNet from source is a two-step process:
1. Build the shared library from the MXNet C++ source code.
2. Install the supported language-specific packages for MXNet.
