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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/20 00:52:00 UTC
[GitHub] piiswrong closed pull request #9147: Install docs - default to install CUDA 9 and cuDNN 7
piiswrong closed pull request #9147: Install docs - default to install CUDA 9 and cuDNN 7
URL: https://github.com/apache/incubator-mxnet/pull/9147
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diff --git a/docs/install/index.md b/docs/install/index.md
index 24d6aeeea1..c9ff073460 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -229,7 +229,7 @@ $ sudo apt-get install -y libopencv-dev
**Step 4** Download MXNet sources and build MXNet core shared library.
```bash
-$ git clone --recursive https://github.com/apache/incubator-mxnet
+$ git clone --recursive https://github.com/apache/incubator-mxnet
$ cd incubator-mxnet
$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
```
@@ -284,8 +284,8 @@ The following installation instructions have been tested on Ubuntu 14.04 and 16.
Install the following NVIDIA libraries to setup *MXNet* with GPU support:
-1. Install CUDA 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
-2. Install cuDNN 5 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
+1. Install CUDA 9.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
+2. Install cuDNN 7 for CUDA 9.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
**Note:** Make sure to add CUDA install path to `LD_LIBRARY_PATH`.
@@ -304,10 +304,10 @@ $ sudo apt-get install -y wget python
$ wget https://bootstrap.pypa.io/get-pip.py && sudo python get-pip.py
```
-**Step 2** Install *MXNet* with GPU support using CUDA 8.0
+**Step 2** Install *MXNet* with GPU support using CUDA 9.0
```bash
-$ pip install mxnet-cu80
+$ pip install mxnet-cu90
```
**Step 3** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
@@ -320,7 +320,7 @@ pip install graphviz
**Experimental Choice** If You would like to install mxnet with Intel MKL, try the experimental pip package with MKL:
```bash
-$ pip install mxnet-cu80mkl
+$ pip install mxnet-cu90mkl
```
</div>
@@ -364,10 +364,10 @@ Installing *MXNet* with pip requires a latest version of `pip`. Install the late
(mxnet)$ pip install --upgrade pip
```
-Install *MXNet* with GPU support using CUDA 8.0.
+Install *MXNet* with GPU support using CUDA 9.0.
```bash
-(mxnet)$ pip install mxnet-cu80
+(mxnet)$ pip install mxnet-cu90
```
**Step 4** Install [Graphviz](http://www.graphviz.org/). (Optional, needed for graph visualization using `mxnet.viz` package).
@@ -692,7 +692,7 @@ $ bash install-mxnet-osx-python.sh
More details and verified installation instructions for macOS, with GPUs, coming soon.
-*MXNet* is expected to be compatible on macOS with NVIDIA GPUs. Please install CUDA 8.0 and cuDNN 5.0, prior to installing GPU version of *MXNet*.
+*MXNet* is expected to be compatible on macOS with NVIDIA GPUs. Please install CUDA 9.0 and cuDNN 7, prior to installing GPU version of *MXNet*.
</div>
</div>
@@ -814,8 +814,8 @@ The following installation instructions have been tested on Ubuntu 14.04 and 16.
Install the following NVIDIA libraries to setup *MXNet* with GPU support:
-1. Install CUDA 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
-2. Install cuDNN 5 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
+1. Install CUDA 9.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
+2. Install cuDNN 7 for CUDA 9.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
**Note:** Make sure to add CUDA install path to `LD_LIBRARY_PATH`.
@@ -1077,7 +1077,7 @@ Clone the MXNet source code repository using the following ```git``` command in
Edit the Makefile to install the MXNet with CUDA bindings to leverage the GPU on the Jetson:
```bash
cp make/config.mk .
- echo "USE_CUDA=1" >> config.mk
+ echo "USE_CUDA=1" >> config.mk
echo "USE_CUDA_PATH=/usr/local/cuda" >> config.mk
echo "USE_CUDNN=1" >> config.mk
```
@@ -1110,7 +1110,7 @@ Add the mxnet folder to the path:
```bash
cd ..
- export MXNET_HOME=$(pwd)
+ export MXNET_HOME=$(pwd)
echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.bashrc
source ~/.bashrc
```
@@ -1458,15 +1458,13 @@ Will be available soon.
</div>
<div class="gpu">
-The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
-
**Prerequisites**
Install the following NVIDIA libraries to setup *MXNet* with GPU support:
-1. Install CUDA 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows).
-2. Install cuDNN 7 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
+1. Install CUDA 9.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows).
+2. Install cuDNN 7 for CUDA 9.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
**Note:** Make sure to add CUDA install path to `PATH`.
@@ -1477,10 +1475,10 @@ Install the following NVIDIA libraries to setup *MXNet* with GPU support:
Recommend install ```Anaconda3``` [here](https://www.anaconda.com/download/)
-**Step 2** Install *MXNet* with GPU support using CUDA 8.0
+**Step 2** Install *MXNet* with GPU support using CUDA 9.0
```bash
-$ pip install mxnet-cu80
+$ pip install mxnet-cu90
```
</div>
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