From 9369319434b4745df67349873236132121feb6f8 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Wed, 16 Aug 2017 08:13:37 +0000 Subject: [PATCH] Deploy to GitHub Pages: 0f8688192cfd4892c379c5f994a2d7149fa3c63d --- .../build_and_install/build_from_source_en.md.txt | 13 +++++++------ .../build_and_install/build_from_source_en.html | 10 +++++----- develop/doc/searchindex.js | 2 +- 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/develop/doc/_sources/getstarted/build_and_install/build_from_source_en.md.txt b/develop/doc/_sources/getstarted/build_and_install/build_from_source_en.md.txt index c0608ede8e5..2f146148949 100644 --- a/develop/doc/_sources/getstarted/build_and_install/build_from_source_en.md.txt +++ b/develop/doc/_sources/getstarted/build_and_install/build_from_source_en.md.txt @@ -68,7 +68,7 @@ As a simple example, consider the following: 1. **BLAS Dependencies(optional)** - CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically. + CMake will search BLAS libraries from the system. If not found, OpenBLAS will be downloaded, built and installed automatically. To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`. ```bash @@ -131,9 +131,9 @@ As a simple example, consider the following: To build GPU version, you will need the following installed: 1. a CUDA-capable GPU - 2. A supported version of Linux with a gcc compiler and toolchain + 2. A supported version of Linux with a GCC compiler and toolchain 3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) - 4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn) + 4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn) The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on @@ -172,6 +172,7 @@ export PATH=/bin:$PATH # install PaddlePaddle Python modules. sudo pip install /opt/paddle/share/wheels/*.whl ``` + ## Build on Centos 7 ### Install Dependencies @@ -192,9 +193,9 @@ sudo pip install /opt/paddle/share/wheels/*.whl To build GPU version, you will need the following installed: 1. a CUDA-capable GPU - 2. A supported version of Linux with a gcc compiler and toolchain + 2. A supported version of Linux with a GCC compiler and toolchain 3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) - 4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn) + 4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn) The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on @@ -222,7 +223,7 @@ mkdir build && cd build ``` Finally, you can build and install PaddlePaddle: - + ```bash # you can add build option here, such as: cmake3 .. -DCMAKE_INSTALL_PREFIX= diff --git a/develop/doc/getstarted/build_and_install/build_from_source_en.html b/develop/doc/getstarted/build_and_install/build_from_source_en.html index 1dcda4466bb..c724f56808c 100644 --- a/develop/doc/getstarted/build_and_install/build_from_source_en.html +++ b/develop/doc/getstarted/build_and_install/build_from_source_en.html @@ -252,7 +252,7 @@ For CUDA 8.0, GCC versions later than 5.3 are not supported!

As a simple example, consider the following:

  1. BLAS Dependencies(optional)

    -

    CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically. +

    CMake will search BLAS libraries from the system. If not found, OpenBLAS will be downloaded, built and installed automatically. To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via MKL_ROOT, OPENBLAS_ROOT or ATLAS_ROOT.

    # specify MKL
 cmake .. -DMKL_ROOT=<mkl_path>
@@ -313,9 +313,9 @@ curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz |
    GPU Dependencies (optional)
    
 
    To build GPU version, you will need the following installed:
    
 
      1. a CUDA-capable GPU
-  2. A supported version of Linux with a gcc compiler and toolchain
+  2. A supported version of Linux with a GCC compiler and toolchain
   3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
-  4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
+  4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
 
    
 
    
 
    The CUDA development environment relies on tight integration with the host development environment,
@@ -371,9 +371,9 @@ sudo pip install 'protobuf>=3.0.0'
 
  2. GPU Dependencies (optional)
    
 
    To build GPU version, you will need the following installed:
    
 
      1. a CUDA-capable GPU
-  2. A supported version of Linux with a gcc compiler and toolchain
+  2. A supported version of Linux with a GCC compiler and toolchain
   3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
-  4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
+  4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
 
    
 
    
 
    The CUDA development environment relies on tight integration with the host development environment,
diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js
index eee9f4b5243..add6f5c5315 100644
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