[GitHub] yasserzamani closed pull request #11258: [MXNET-540] update python gpu build from source instructions on windows

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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 `![png](img_url)`. Like this:
+
 ```
 ![png](img_url.png)<!--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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