yasserzamani closed pull request #11258: [MXNET-540] update python gpu build
from source instructions on windows
URL: https://github.com/apache/incubator-mxnet/pull/11258
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diff --git a/ci/build.py b/ci/build.py
index 4473f54210a..35f8b478abf 100755
--- a/ci/build.py
+++ b/ci/build.py
@@ -34,6 +34,7 @@
import subprocess
import sys
import tempfile
+import platform
from copy import deepcopy
from itertools import chain
from subprocess import call, check_call
@@ -121,11 +122,16 @@ def buildir() -> str:
def default_ccache_dir() -> str:
+ # Share ccache across containers
if 'CCACHE_DIR' in os.environ:
ccache_dir = os.path.realpath(os.environ['CCACHE_DIR'])
os.makedirs(ccache_dir, exist_ok=True)
- return ccache_dirpython
- # Share ccache across containers
+ return ccache_dir
+ # In osx tmpdir is not mountable by default
+ if platform.system() == 'Darwin':
+ ccache_dir = "/tmp/_mxnet_ccache"
+ os.makedirs(ccache_dir, exist_ok=True)
+ return ccache_dir
return os.path.join(tempfile.gettempdir(), "ci_ccache")
diff --git a/docs/install/index.md b/docs/install/index.md
index 4b966b62067..0a102394ec7 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -1703,7 +1703,7 @@ msbuild mxnet.sln /p:Configuration=Release;Platform=x64
/maxcpucount
To build and install MXNet yourself using [Microsoft Visual Studio
2015](https://www.visualstudio.com/vs/older-downloads/), you need the following
dependencies. Install the required dependencies:
-1. If [Microsoft Visual Studio
2015](https://www.visualstudio.com/vs/older-downloads/) is not already
installed, download and install it. You can download and install the free
community edition.
+1. If [Microsoft Visual Studio
2015](https://www.visualstudio.com/vs/older-downloads/) is not already
installed, download and install it. You can download and install the free
community edition. At least Update 3 of Microsoft Visual Studio 2015 is
required to build MXNet from source. Upgrade via it's ```Tools -> Extensions
and Updates... | Product Updates``` menu.
2. Download and install [CMake](https://cmake.org/) if it is not already
installed.
3. Download and install
[OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
4. Unzip the OpenCV package.
@@ -1711,10 +1711,12 @@ To build and install MXNet yourself using [Microsoft
Visual Studio 2015](https:/
6. If you don't have the Intel Math Kernel Library (MKL) installed, download
and install [OpenBlas](http://sourceforge.net/projects/openblas/files/v0.2.14/).
7. Set the environment variable ```OpenBLAS_HOME``` to point to the
```OpenBLAS``` directory that contains the ```include``` and ```lib```
directories. Typically, you can find the directory in ```C:\Program files
(x86)\OpenBLAS\```.
8. Download and install
[CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64)
and [cuDNN](https://developer.nvidia.com/cudnn). To get access to the download
link, register as an NVIDIA community user.
+9. Set the environment variable ```CUDACXX``` to point to the ```CUDA
Compiler```(```C:\Program Files\NVIDIA GPU Computing
Toolkit\CUDA\v9.1\bin\nvcc.exe``` for example).
+10. Set the environment variable ```CUDNN_ROOT``` to point to the ```cuDNN```
directory that contains the ```include```, ```lib``` and ```bin``` directories
(```C:\Downloads\cudnn-9.1-windows7-x64-v7\cuda``` for example).
After you have installed all of the required dependencies, build the MXNet
source code:
-1. Download the MXNet source code from
[GitHub](https://github.com/apache/incubator-mxnet).
+1. Download the MXNet source code from
[GitHub](https://github.com/apache/incubator-mxnet) (make sure you also
download third parties submodules e.g. ```git clone --recurse-submodules```).
2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in
```./build```.
3. In Visual Studio, open the solution file,```.sln```, and compile it.
These commands produce a library called ```mxnet.dll``` in the
```./build/Release/``` or ```./build/Debug``` folder.
diff --git a/example/README.md b/example/README.md
index 542162c0bf6..0dc6138c2ef 100644
--- a/example/README.md
+++ b/example/README.md
@@ -1,6 +1,6 @@
# MXNet Examples
-This page contains a curated list of awesome MXNet examples, tutorials and
blogs. It is inspired by [awesome-php](https://github.com/ziadoz/awesome-php)
and
[awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning).
+This page contains a curated list of awesome MXNet examples, tutorials and
blogs. It is inspired by [awesome-php](https://github.com/ziadoz/awesome-php)
and
[awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning).
See also [Awesome-MXNet](https://github.com/chinakook/Awesome-MXNet) for a
similar list.
- [Contributing](#contributing)
- [List of examples](#list-of-examples)
@@ -28,7 +28,7 @@ Example applications or scripts should be submitted in this
`example` folder.
### Tutorials
-If you have a tutorial idea for the website, download the [ Jupyter notebook
tutorial
template](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb).
+If you have a tutorial idea for the website, download the [Jupyter notebook
tutorial
template](https://github.com/dmlc/mxnet/tree/master/example/MXNetTutorialTemplate.ipynb).
#### Tutorial location
@@ -45,9 +45,11 @@ The site expects the format to be markdown, so export your
notebook as a .md via
```
If you want some lines to show-up in the markdown but not in the generated
notebooks, add this comment `<!--notebook-skip-line-->` after your
``. Like this:
+
```
<!--notebook-skip-line-->
```
+
Typically when you have a `plt.imshow()` you want the image tag
`[png](img.png)` in the `.md` but not in the downloaded notebook as the user
will re-generate the plot at run-time.
#### Tutorial tests
@@ -151,7 +153,8 @@ If your tutorial depends on specific packages, simply add
them to this provision
* [LSTM Human Activity
Recognition](https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/HumanActivityRecognition)
by [Ldpe2G](https://github.com/Ldpe2G)
* [Visual Question
Answering](https://github.com/liuzhi136/Visual-Question-Answering) by
[liuzhi136](https://github.com/liuzhi136)
* [Deformable ConvNets](https://arxiv.org/abs/1703.06211)
([github](https://github.com/msracver/Deformable-ConvNets)) by
[MSRACVer](https://github.com/msracver)
-
+* [OCR with bi-LSTM and CTC Loss in
Gluon](https://github.com/ThomasDelteil/Gluon_OCR_LSTM_CTC) by
[ThomasDelteil](https://github.com/ThomasDelteil)
+* [Visual Search with Gluon and
HNSWlib](https://github.com/ThomasDelteil/VisualSearch_MXNet), by
[ThomasDelteil](https://github.com/ThomasDelteil), online demo
[here](https://thomasdelteil.github.io/VisualSearch_MXNet/)
### <a name="ipython-notebooks"></a>IPython Notebooks
-----------------
@@ -164,7 +167,7 @@ If your tutorial depends on specific packages, simply add
them to this provision
* [class active
maps](https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/class_active_maps.ipynb)
- A demo of how to localize the discriminative regions in an image using
global average pooling (GAP) in CNNs.
* [DMLC MXNet Notebooks](https://github.com/dmlc/mxnet-notebooks) DMLC's repo
for various notebooks ranging from basic usages of MXNet to state-of-the-art
deep learning applications.
* [AWS Seoul Summit 2017
Demos](https://github.com/sxjscience/aws-summit-2017-seoul) The demo codes and
ipython notebooks in AWS Seoul Summit 2017.
-* [Character-level CNN for text
classification](https://github.com/ThomasDelteil/CNN_NLP_MXNet) Performing
category classification on Amazon reviews using Gluon and character-level
Convolutional Neural Networks
+* [Character-level CNN for text
classification](https://github.com/ThomasDelteil/CNN_NLP_MXNet) Performing
category classification on Amazon reviews using Gluon and character-level
Convolutional Neural Networks. Online demo
[here](https://thomasdelteil.github.io/CNN_NLP_MXNet/)
### <a name="mobile-apps-examples"></a>Mobile App Examples
-------------------
@@ -220,4 +223,3 @@ If your tutorial depends on specific packages, simply add
them to this provision
* [MXnet-face](https://github.com/tornadomeet/mxnet-face) - Using mxnet for
face-related algorithm by [tornadomeet](https://github.com/tornadomeet) where
the single model get 97.13%+-0.88% accuracy on LFW, and with only 20MB size.
* [MinPy](https://github.com/dmlc/minpy) - Pure numpy practice with third
party operator Integration and MXnet as backend for GPU computing
* [MXNet Model Server](https://github.com/awslabs/mxnet-model-server) - a
flexible and easy to use tool for serving Deep Learning models
-* [ONNX-MXNet](https://github.com/onnx/onnx-mxnet) - implements ONNX model
format support for Apache MXNet
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