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diff --git a/tensorflow/g3doc/get_started/os_setup.md b/tensorflow/g3doc/get_started/os_setup.md index e5cd016544..f7776ca4de 100644 --- a/tensorflow/g3doc/get_started/os_setup.md +++ b/tensorflow/g3doc/get_started/os_setup.md @@ -1,8 +1,8 @@ -# Download and Setup <a class="md-anchor" id="AUTOGENERATED-download-and-setup"></a> +# Download and Setup You can install TensorFlow using our provided binary packages or from source. -## Binary Installation <a class="md-anchor" id="AUTOGENERATED-binary-installation"></a> +## Binary Installation The TensorFlow Python API currently requires Python 2.7: we are [working](https://github.com/tensorflow/tensorflow/issues/1) on adding support @@ -16,7 +16,7 @@ If you encounter installation errors, see installation, please consider using our virtualenv-based instructions [here](#virtualenv_install). -### Ubuntu/Linux 64-bit <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux-64-bit"></a> +### Ubuntu/Linux 64-bit ```bash # For CPU-only version @@ -26,7 +26,7 @@ $ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5 $ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl ``` -### Mac OS X <a class="md-anchor" id="AUTOGENERATED-mac-os-x"></a> +### Mac OS X On OS X, we recommend installing [homebrew](http://brew.sh) and `brew install python` before proceeding, or installing TensorFlow within [virtualenv](#virtualenv_install). @@ -36,7 +36,7 @@ python` before proceeding, or installing TensorFlow within [virtualenv](#virtual $ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl ``` -## Docker-based installation <a class="md-anchor" id="AUTOGENERATED-docker-based-installation"></a> +## Docker-based installation We also support running TensorFlow via [Docker](http://docker.com/), which lets you avoid worrying about setting up dependencies. @@ -51,7 +51,7 @@ $ docker run -it b.gcr.io/tensorflow/tensorflow This will start a container with TensorFlow and all its dependencies already installed. -### Additional images <a class="md-anchor" id="AUTOGENERATED-additional-images"></a> +### Additional images The default Docker image above contains just a minimal set of libraries for getting up and running with TensorFlow. We also have the following container, @@ -62,7 +62,7 @@ which you can use in the `docker run` command above: makes it easy to experiment directly with the source, without needing to install any of the dependencies described above. -## VirtualEnv-based installation <a class="md-anchor" id="virtualenv_install"></a> +## VirtualEnv-based installation {#virtualenv_install} We recommend using [virtualenv](https://pypi.python.org/pypi/virtualenv) to create an isolated container and install TensorFlow in that container -- it is @@ -121,9 +121,9 @@ then run an example TensorFlow program like: $ # Your prompt should change back ``` -## Try your first TensorFlow program <a class="md-anchor" id="AUTOGENERATED-try-your-first-tensorflow-program"></a> +## Try your first TensorFlow program -### (Optional) Enable GPU Support <a class="md-anchor" id="AUTOGENERATED--optional--enable-gpu-support"></a> +### (Optional) Enable GPU Support If you installed the GPU-enabled TensorFlow pip binary, you must have the correct versions of the CUDA SDK and CUDNN installed on your @@ -138,7 +138,7 @@ export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" export CUDA_HOME=/usr/local/cuda ``` -### Run TensorFlow <a class="md-anchor" id="AUTOGENERATED-run-tensorflow"></a> +### Run TensorFlow Open a python terminal: @@ -158,9 +158,9 @@ Hello, TensorFlow! ``` -## Installing from sources <a class="md-anchor" id="source"></a> +## Installing from sources {#source} -### Clone the TensorFlow repository <a class="md-anchor" id="AUTOGENERATED-clone-the-tensorflow-repository"></a> +### Clone the TensorFlow repository ```bash $ git clone --recurse-submodules https://github.com/tensorflow/tensorflow @@ -169,9 +169,9 @@ $ git clone --recurse-submodules https://github.com/tensorflow/tensorflow `--recurse-submodules` is required to fetch the protobuf library that TensorFlow depends on. -### Installation for Linux <a class="md-anchor" id="AUTOGENERATED-installation-for-linux"></a> +### Installation for Linux -#### Install Bazel <a class="md-anchor" id="AUTOGENERATED-install-bazel"></a> +#### Install Bazel Follow instructions [here](http://bazel.io/docs/install.html) to install the @@ -190,13 +190,13 @@ downloaded the installer. Finally, follow the instructions in that script to place bazel into your binary path. -#### Install other dependencies <a class="md-anchor" id="AUTOGENERATED-install-other-dependencies"></a> +#### Install other dependencies ```bash $ sudo apt-get install python-numpy swig python-dev ``` -#### Optional: Install CUDA (GPUs on Linux) <a class="md-anchor" id="install_cuda"></a> +#### Optional: Install CUDA (GPUs on Linux) {#install_cuda} In order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and CUDNN 6.5 V2 from NVIDIA need to be installed. @@ -208,13 +208,13 @@ TensorFlow GPU support requires having a GPU card with NVidia Compute Capability * NVidia K20 * NVidia K40 -##### Download and install Cuda Toolkit 7.0 <a class="md-anchor" id="AUTOGENERATED-download-and-install-cuda-toolkit-7.0"></a> +##### Download and install Cuda Toolkit 7.0 https://developer.nvidia.com/cuda-toolkit-70 Install the toolkit into e.g. `/usr/local/cuda` -##### Download and install CUDNN Toolkit 6.5 <a class="md-anchor" id="AUTOGENERATED-download-and-install-cudnn-toolkit-6.5"></a> +##### Download and install CUDNN Toolkit 6.5 https://developer.nvidia.com/rdp/cudnn-archive @@ -227,7 +227,7 @@ sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64 ``` -##### Configure TensorFlow's canonical view of Cuda libraries <a class="md-anchor" id="AUTOGENERATED-configure-tensorflow-s-canonical-view-of-cuda-libraries"></a> +##### Configure TensorFlow's canonical view of Cuda libraries From the root of your source tree, run: ``` bash @@ -252,7 +252,7 @@ This creates a canonical set of symbolic links to the Cuda libraries on your sys Every time you change the Cuda library paths you need to run this step again before you invoke the bazel build command. -##### Build your target with GPU support. <a class="md-anchor" id="AUTOGENERATED-build-your-target-with-gpu-support."></a> +##### Build your target with GPU support. From the root of your source tree, run: ```bash @@ -268,7 +268,7 @@ $ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu Note that "--config=cuda" is needed to enable the GPU support. -##### Enabling Cuda 3.0. <a class="md-anchor" id="AUTOGENERATED-enabling-cuda-3.0."></a> +##### Enabling Cuda 3.0. TensorFlow officially supports Cuda devices with 3.5 and 5.2 compute capabilities. In order to enable earlier Cuda devices such as Grid K520, you need to target Cuda 3.0. This can be done through TensorFlow unofficial @@ -296,7 +296,7 @@ Setting up Cuda nvvm Configuration finished ``` -##### Known issues <a class="md-anchor" id="AUTOGENERATED-known-issues"></a> +##### Known issues * Although it is possible to build both Cuda and non-Cuda configs under the same source tree, we recommend to run "bazel clean" when switching between these two @@ -307,30 +307,30 @@ will fail with a clear error message. In the future, we might consider making this more conveninent by including the configure step in our build process, given necessary bazel new feature support. -### Installation for Mac OS X <a class="md-anchor" id="AUTOGENERATED-installation-for-mac-os-x"></a> +### Installation for Mac OS X -Mac needs the same set of dependencies as Linux, however their installing those +Mac needs the same set of dependencies as Linux, however installing those dependencies is different. Here is a set of useful links to help with installing the dependencies on Mac OS X : -#### Bazel <a class="md-anchor" id="AUTOGENERATED-bazel"></a> +#### Bazel Look for installation instructions for Mac OS X on [this](http://bazel.io/docs/install.html) page. -#### SWIG <a class="md-anchor" id="AUTOGENERATED-swig"></a> +#### SWIG [Mac OS X installation](http://www.swig.org/Doc3.0/Preface.html#Preface_osx_installation). Notes : You need to install [PCRE](ftp://ftp.csx.cam.ac.uk/pub/software/programming/pcre/) and *NOT* PCRE2. -#### Numpy <a class="md-anchor" id="AUTOGENERATED-numpy"></a> +#### Numpy Follow installation instructions [here](http://docs.scipy.org/doc/numpy/user/install.html). -### Create the pip package and install <a class="md-anchor" id="create-pip"></a> +### Create the pip package and install {#create-pip} ```bash $ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package @@ -344,7 +344,7 @@ $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg $ pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl ``` -## Train your first TensorFlow neural net model <a class="md-anchor" id="AUTOGENERATED-train-your-first-tensorflow-neural-net-model"></a> +## Train your first TensorFlow neural net model Starting from the root of your source tree, run: @@ -372,9 +372,9 @@ Validation error: 7.0% ... ``` -## Common Problems <a class="md-anchor" id="common_install_problems"></a> +## Common Problems {#common_install_problems} -### GPU-related issues <a class="md-anchor" id="AUTOGENERATED-gpu-related-issues"></a> +### GPU-related issues If you encounter the following when trying to run a TensorFlow program: @@ -384,9 +384,9 @@ ImportError: libcudart.so.7.0: cannot open shared object file: No such file or d Make sure you followed the the GPU installation [instructions](#install_cuda). -### Pip installation issues <a class="md-anchor" id="AUTOGENERATED-pip-installation-issues"></a> +### Pip installation issues -#### Can't find setup.py <a class="md-anchor" id="AUTOGENERATED-can-t-find-setup.py"></a> +#### Can't find setup.py If, during `pip install`, you encounter an error like: @@ -403,7 +403,7 @@ pip install --upgrade pip This may require `sudo`, depending on how `pip` is installed. -#### SSLError: SSL_VERIFY_FAILED <a class="md-anchor" id="AUTOGENERATED-sslerror--ssl_verify_failed"></a> +#### SSLError: SSL_VERIFY_FAILED If, during pip install from a URL, you encounter an error like: @@ -414,7 +414,7 @@ SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed Solution: Download the wheel manually via curl or wget, and pip install locally. -### On Linux <a class="md-anchor" id="AUTOGENERATED-on-linux"></a> +### On Linux If you encounter: @@ -427,7 +427,7 @@ SyntaxError: invalid syntax Solution: make sure you are using Python 2.7. -### On MacOSX <a class="md-anchor" id="AUTOGENERATED-on-macosx"></a> +### On MacOSX If you encounter: |