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diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index f21c073a1b..3a9a01c57e 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -1,38 +1,38 @@ -# Installing TensorFlow on Ubuntu +# Install TensorFlow on Ubuntu This guide explains how to install TensorFlow on Ubuntu Linux. While these -instructions may work on other Linux variants, they are tested and supported with -the following system requirements: - -* 64-bit desktops or laptops -* Ubuntu 16.04 or higher +instructions may work on other Linux variants, they are tested and supported +with the following system requirements: +* 64-bit desktops or laptops +* Ubuntu 16.04 or higher ## Choose which TensorFlow to install The following TensorFlow variants are available for installation: -* __TensorFlow with CPU support only__. If your system does not have a - NVIDIA® GPU, you must install this version. This version of TensorFlow is - usually easier to install, so even if you have an NVIDIA GPU, we recommend - installing this version first. -* __TensorFlow with GPU support__. TensorFlow programs usually run much faster on - a GPU instead of a CPU. If you run performance-critical applications and your - system has an NVIDIA® GPU that meets the prerequisites, you should install - this version. See [TensorFlow GPU support](#NVIDIARequirements) for details. - +* __TensorFlow with CPU support only__. If your system does not have a + NVIDIA® GPU, you must install this version. This version of TensorFlow + is usually easier to install, so even if you have an NVIDIA GPU, we + recommend installing this version first. +* __TensorFlow with GPU support__. TensorFlow programs usually run much faster + on a GPU instead of a CPU. If you run performance-critical applications and + your system has an NVIDIA® GPU that meets the prerequisites, you should + install this version. See [TensorFlow GPU support](#NVIDIARequirements) for + details. ## How to install TensorFlow There are a few options to install TensorFlow on your machine: -* [Use pip in a virtual environment](#InstallingVirtualenv) *(recommended)* -* [Use pip in your system environment](#InstallingNativePip) -* [Configure a Docker container](#InstallingDocker) -* [Use pip in Anaconda](#InstallingAnaconda) -* [Install TensorFlow from source](/install/install_sources) +* [Use pip in a virtual environment](#InstallingVirtualenv) *(recommended)* +* [Use pip in your system environment](#InstallingNativePip) +* [Configure a Docker container](#InstallingDocker) +* [Use pip in Anaconda](#InstallingAnaconda) +* [Install TensorFlow from source](/install/install_sources) <a name="InstallingVirtualenv"></a> + ### Use `pip` in a virtual environment Key Point: Using a virtual environment is the recommended install method. @@ -41,8 +41,8 @@ The [Virtualenv](https://virtualenv.pypa.io/en/stable/) tool creates virtual Python environments that are isolated from other Python development on the same machine. In this scenario, you install TensorFlow and its dependencies within a virtual environment that is available when *activated*. Virtualenv provides a -reliable way to install and run TensorFlow while avoiding conflicts with the rest -of the system. +reliable way to install and run TensorFlow while avoiding conflicts with the +rest of the system. ##### 1. Install Python, `pip`, and `virtualenv`. @@ -62,10 +62,10 @@ To install these packages on Ubuntu: </pre> We *recommend* using `pip` version 8.1 or higher. If using a release before -version 8.1, upgrade `pip`: +version 8.1, upgrade `pip`: <pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo pip install -U pip</code> + <code class="devsite-terminal">pip install --upgrade pip</code> </pre> If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -102,7 +102,7 @@ When the Virtualenv is activated, the shell prompt displays as `(venv) $`. Within the active virtual environment, upgrade `pip`: <pre class="prettyprint lang-bsh"> -(venv)$ pip install -U pip +(venv)$ pip install --upgrade pip </pre> You can install other Python packages within the virtual environment without @@ -112,15 +112,15 @@ affecting packages outside the `virtualenv`. Choose one of the available TensorFlow packages for installation: -* `tensorflow` —Current release for CPU -* `tensorflow-gpu` —Current release with GPU support -* `tf-nightly` —Nightly build for CPU -* `tf-nightly-gpu` —Nightly build with GPU support +* `tensorflow` —Current release for CPU +* `tensorflow-gpu` —Current release with GPU support +* `tf-nightly` —Nightly build for CPU +* `tf-nightly-gpu` —Nightly build with GPU support Within an active Virtualenv environment, use `pip` to install the package: <pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">pip install -U tensorflow</code> + <code class="devsite-terminal">pip install --upgrade tensorflow</code> </pre> Use `pip list` to show the packages installed in the virtual environment. @@ -160,14 +160,14 @@ To uninstall TensorFlow, remove the Virtualenv directory you created in step 2: <code class="devsite-terminal">rm -r ~/tensorflow/<var>venv</var></code> </pre> - <a name="InstallingNativePip"></a> + ### Use `pip` in your system environment Use `pip` to install the TensorFlow package directly on your system without using a container or virtual environment for isolation. This method is -recommended for system administrators that want a TensorFlow installation that is -available to everyone on a multi-user system. +recommended for system administrators that want a TensorFlow installation that +is available to everyone on a multi-user system. Since a system install is not isolated, it could interfere with other Python-based installations. But if you understand `pip` and your Python @@ -195,10 +195,10 @@ To install these packages on Ubuntu: </pre> We *recommend* using `pip` version 8.1 or higher. If using a release before -version 8.1, upgrade `pip`: +version 8.1, upgrade `pip`: <pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo pip install -U pip</code> + <code class="devsite-terminal">pip install --upgrade pip</code> </pre> If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -212,16 +212,16 @@ installed, use `easy_install` to install `pip`: Choose one of the available TensorFlow packages for installation: -* `tensorflow` —Current release for CPU -* `tensorflow-gpu` —Current release with GPU support -* `tf-nightly` —Nightly build for CPU -* `tf-nightly-gpu` —Nightly build with GPU support +* `tensorflow` —Current release for CPU +* `tensorflow-gpu` —Current release with GPU support +* `tf-nightly` —Nightly build for CPU +* `tf-nightly-gpu` —Nightly build with GPU support And use `pip` to install the package for Python 2 or 3: <pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo pip install -U tensorflow # Python 2.7</code> - <code class="devsite-terminal">sudo pip3 install -U tensorflow # Python 3.n</code> + <code class="devsite-terminal">pip install --upgrade --user tensorflow # Python 2.7</code> + <code class="devsite-terminal">pip3 install --upgrade --user tensorflow # Python 3.n</code> </pre> Use `pip list` to show the packages installed on the system. @@ -239,8 +239,8 @@ If the above steps failed, try installing the TensorFlow binary using the remote URL of the `pip` package: <pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo pip install --upgrade <var>remote-pkg-URL</var> # Python 2.7</code> - <code class="devsite-terminal">sudo pip3 install --upgrade <var>remote-pkg-URL</var> # Python 3.n</code> + <code class="devsite-terminal">pip install --user --upgrade <var>remote-pkg-URL</var> # Python 2.7</code> + <code class="devsite-terminal">pip3 install --user --upgrade <var>remote-pkg-URL</var> # Python 3.n</code> </pre> The <var>remote-pkg-URL</var> depends on the operating system, Python version, @@ -255,42 +255,41 @@ encounter problems. To uninstall TensorFlow on your system, use one of following commands: <pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo pip uninstall tensorflow # for Python 2.7</code> - <code class="devsite-terminal">sudo pip3 uninstall tensorflow # for Python 3.n</code> + <code class="devsite-terminal">pip uninstall tensorflow # for Python 2.7</code> + <code class="devsite-terminal">pip3 uninstall tensorflow # for Python 3.n</code> </pre> <a name="InstallingDocker"></a> + ### Configure a Docker container -Docker completely isolates the TensorFlow installation -from pre-existing packages on your machine. The Docker container contains -TensorFlow and all its dependencies. Note that the Docker image can be quite -large (hundreds of MBs). You might choose the Docker installation if you are -incorporating TensorFlow into a larger application architecture that already -uses Docker. +Docker completely isolates the TensorFlow installation from pre-existing +packages on your machine. The Docker container contains TensorFlow and all its +dependencies. Note that the Docker image can be quite large (hundreds of MBs). +You might choose the Docker installation if you are incorporating TensorFlow +into a larger application architecture that already uses Docker. Take the following steps to install TensorFlow through Docker: - 1. Install Docker on your machine as described in the - [Docker documentation](http://docs.docker.com/engine/installation/). - 2. Optionally, create a Linux group called <code>docker</code> to allow - launching containers without sudo as described in the - [Docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/). - (If you don't do this step, you'll have to use sudo each time - you invoke Docker.) - 3. To install a version of TensorFlow that supports GPUs, you must first - install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker), which - is stored in github. - 4. Launch a Docker container that contains one of the - [TensorFlow binary images](https://hub.docker.com/r/tensorflow/tensorflow/tags/). +1. Install Docker on your machine as described in the + [Docker documentation](http://docs.docker.com/engine/installation/). +2. Optionally, create a Linux group called <code>docker</code> to allow + launching containers without sudo as described in the + [Docker documentation](https://docs.docker.com/engine/installation/linux/linux-postinstall/). + (If you don't do this step, you'll have to use sudo each time you invoke + Docker.) +3. To install a version of TensorFlow that supports GPUs, you must first + install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker), which is + stored in github. +4. Launch a Docker container that contains one of the + [TensorFlow binary images](https://hub.docker.com/r/tensorflow/tensorflow/tags/). The remainder of this section explains how to launch a Docker container. - #### CPU-only -To launch a Docker container with CPU-only support (that is, without -GPU support), enter a command of the following format: +To launch a Docker container with CPU-only support (that is, without GPU +support), enter a command of the following format: <pre> $ docker run -it <i>-p hostPort:containerPort TensorFlowCPUImage</i> @@ -298,29 +297,31 @@ $ docker run -it <i>-p hostPort:containerPort TensorFlowCPUImage</i> where: - * <tt><i>-p hostPort:containerPort</i></tt> is optional. - If you plan to run TensorFlow programs from the shell, omit this option. - If you plan to run TensorFlow programs as Jupyter notebooks, set both - <tt><i>hostPort</i></tt> and <tt><i>containerPort</i></tt> - to <tt>8888</tt>. If you'd like to run TensorBoard inside the container, - add a second `-p` flag, setting both <i>hostPort</i> and <i>containerPort</i> - to 6006. - * <tt><i>TensorFlowCPUImage</i></tt> is required. It identifies the Docker +* <tt><i>-p hostPort:containerPort</i></tt> is optional. If you plan to run + TensorFlow programs from the shell, omit this option. If you plan to run + TensorFlow programs as Jupyter notebooks, set both <tt><i>hostPort</i></tt> + and <tt><i>containerPort</i></tt> to <tt>8888</tt>. If you'd like to run + TensorBoard inside the container, add a second `-p` flag, setting both + <i>hostPort</i> and <i>containerPort</i> to 6006. +* <tt><i>TensorFlowCPUImage</i></tt> is required. It identifies the Docker container. Specify one of the following values: - * <tt>tensorflow/tensorflow</tt>, which is the TensorFlow CPU binary image. - * <tt>tensorflow/tensorflow:latest-devel</tt>, which is the latest - TensorFlow CPU Binary image plus source code. - * <tt>tensorflow/tensorflow:<i>version</i></tt>, which is the - specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image. - * <tt>tensorflow/tensorflow:<i>version</i>-devel</tt>, which is - the specified version (for example, 1.1.0rc1) of the TensorFlow GPU - binary image plus source code. + + * <tt>tensorflow/tensorflow</tt>, which is the TensorFlow CPU binary + image. + * <tt>tensorflow/tensorflow:latest-devel</tt>, which is the latest + TensorFlow CPU Binary image plus source code. + * <tt>tensorflow/tensorflow:<i>version</i></tt>, which is the specified + version (for example, 1.1.0rc1) of TensorFlow CPU binary image. + * <tt>tensorflow/tensorflow:<i>version</i>-devel</tt>, which is the + specified version (for example, 1.1.0rc1) of the TensorFlow GPU binary + image plus source code. TensorFlow images are available at [dockerhub](https://hub.docker.com/r/tensorflow/tensorflow/). -For example, the following command launches the latest TensorFlow CPU binary image -in a Docker container from which you can run TensorFlow programs in a shell: +For example, the following command launches the latest TensorFlow CPU binary +image in a Docker container from which you can run TensorFlow programs in a +shell: <pre> $ <b>docker run -it tensorflow/tensorflow bash</b> @@ -336,10 +337,11 @@ $ <b>docker run -it -p 8888:8888 tensorflow/tensorflow</b> Docker will download the TensorFlow binary image the first time you launch it. - #### GPU support -To launch a Docker container with NVidia GPU support, enter a command of the following format (this [does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)): +To launch a Docker container with NVidia GPU support, enter a command of the +following format (this +[does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)): <pre> $ <b>nvidia-docker run -it</b> <i>-p hostPort:containerPort TensorFlowGPUImage</i> @@ -347,34 +349,34 @@ $ <b>nvidia-docker run -it</b> <i>-p hostPort:containerPort TensorFlowGPUImage</ where: - * <tt><i>-p hostPort:containerPort</i></tt> is optional. If you plan - to run TensorFlow programs from the shell, omit this option. If you plan - to run TensorFlow programs as Jupyter notebooks, set both - <tt><i>hostPort</i></tt> and <code><em>containerPort</em></code> to `8888`. - * <i>TensorFlowGPUImage</i> specifies the Docker container. You must - specify one of the following values: - * <tt>tensorflow/tensorflow:latest-gpu</tt>, which is the latest - TensorFlow GPU binary image. - * <tt>tensorflow/tensorflow:latest-devel-gpu</tt>, which is - the latest TensorFlow GPU Binary image plus source code. - * <tt>tensorflow/tensorflow:<i>version</i>-gpu</tt>, which is the - specified version (for example, 0.12.1) of the TensorFlow GPU - binary image. - * <tt>tensorflow/tensorflow:<i>version</i>-devel-gpu</tt>, which is - the specified version (for example, 0.12.1) of the TensorFlow GPU - binary image plus source code. - -We recommend installing one of the `latest` versions. For example, the -following command launches the latest TensorFlow GPU binary image in a -Docker container from which you can run TensorFlow programs in a shell: +* <tt><i>-p hostPort:containerPort</i></tt> is optional. If you plan to run + TensorFlow programs from the shell, omit this option. If you plan to run + TensorFlow programs as Jupyter notebooks, set both <tt><i>hostPort</i></tt> + and <code><em>containerPort</em></code> to `8888`. +* <i>TensorFlowGPUImage</i> specifies the Docker container. You must specify + one of the following values: + * <tt>tensorflow/tensorflow:latest-gpu</tt>, which is the latest + TensorFlow GPU binary image. + * <tt>tensorflow/tensorflow:latest-devel-gpu</tt>, which is the latest + TensorFlow GPU Binary image plus source code. + * <tt>tensorflow/tensorflow:<i>version</i>-gpu</tt>, which is the + specified version (for example, 0.12.1) of the TensorFlow GPU binary + image. + * <tt>tensorflow/tensorflow:<i>version</i>-devel-gpu</tt>, which is the + specified version (for example, 0.12.1) of the TensorFlow GPU binary + image plus source code. + +We recommend installing one of the `latest` versions. For example, the following +command launches the latest TensorFlow GPU binary image in a Docker container +from which you can run TensorFlow programs in a shell: <pre> $ <b>nvidia-docker run -it tensorflow/tensorflow:latest-gpu bash</b> </pre> -The following command also launches the latest TensorFlow GPU binary image -in a Docker container. In this Docker container, you can run TensorFlow -programs in a Jupyter notebook: +The following command also launches the latest TensorFlow GPU binary image in a +Docker container. In this Docker container, you can run TensorFlow programs in a +Jupyter notebook: <pre> $ <b>nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu</b> @@ -390,14 +392,12 @@ Docker will download the TensorFlow binary image the first time you launch it. For more details see the [TensorFlow docker readme](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker). - #### Next Steps -You should now -[validate your installation](#ValidateYourInstallation). - +You should now [validate your installation](#ValidateYourInstallation). <a name="InstallingAnaconda"></a> + ### Use `pip` in Anaconda Anaconda provides the `conda` utility to create a virtual environment. However, @@ -410,61 +410,59 @@ not tested on new TensorFlow releases. Take the following steps to install TensorFlow in an Anaconda environment: - 1. Follow the instructions on the - [Anaconda download site](https://www.continuum.io/downloads) - to download and install Anaconda. +1. Follow the instructions on the + [Anaconda download site](https://www.continuum.io/downloads) to download and + install Anaconda. - 2. Create a conda environment named <tt>tensorflow</tt> to run a version - of Python by invoking the following command: +2. Create a conda environment named <tt>tensorflow</tt> to run a version of + Python by invoking the following command: <pre>$ <b>conda create -n tensorflow pip python=2.7 # or python=3.3, etc.</b></pre> - 3. Activate the conda environment by issuing the following command: +3. Activate the conda environment by issuing the following command: <pre>$ <b>source activate tensorflow</b> (tensorflow)$ # Your prompt should change </pre> - 4. Issue a command of the following format to install - TensorFlow inside your conda environment: +4. Issue a command of the following format to install TensorFlow inside your + conda environment: <pre>(tensorflow)$ <b>pip install --ignore-installed --upgrade</b> <i>tfBinaryURL</i></pre> - where <code><em>tfBinaryURL</em></code> is the - [URL of the TensorFlow Python package](#the_url_of_the_tensorflow_python_package). - For example, the following command installs the CPU-only version of - TensorFlow for Python 3.4: + where <code><em>tfBinaryURL</em></code> is the + [URL of the TensorFlow Python package](#the_url_of_the_tensorflow_python_package). + For example, the following command installs the CPU-only version of + TensorFlow for Python 3.4: <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl</b></pre> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl</b></pre> <a name="ValidateYourInstallation"></a> + ## Validate your installation To validate your TensorFlow installation, do the following: - 1. Ensure that your environment is prepared to run TensorFlow programs. - 2. Run a short TensorFlow program. - +1. Ensure that your environment is prepared to run TensorFlow programs. +2. Run a short TensorFlow program. ### Prepare your environment -If you installed on native pip, Virtualenv, or Anaconda, then -do the following: +If you installed on native pip, Virtualenv, or Anaconda, then do the following: - 1. Start a terminal. - 2. If you installed with Virtualenv or Anaconda, activate your container. - 3. If you installed TensorFlow source code, navigate to any - directory *except* one containing TensorFlow source code. +1. Start a terminal. +2. If you installed with Virtualenv or Anaconda, activate your container. +3. If you installed TensorFlow source code, navigate to any directory *except* + one containing TensorFlow source code. -If you installed through Docker, start a Docker container -from which you can run bash. For example: +If you installed through Docker, start a Docker container from which you can run +bash. For example: <pre> $ <b>docker run -it tensorflow/tensorflow bash</b> </pre> - ### Run a short TensorFlow program Invoke python from your shell as follows: @@ -486,94 +484,71 @@ TensorFlow programs: <pre>Hello, TensorFlow!</pre> -If the system outputs an error message instead of a greeting, see [Common -installation problems](#common_installation_problems). +If the system outputs an error message instead of a greeting, see +[Common installation problems](#common_installation_problems). To learn more, see the [TensorFlow tutorials](../tutorials/). <a name="NVIDIARequirements"></a> -## TensorFlow GPU support - -To install TensorFlow with GPU support, configure the following NVIDIA® software -on your system: - -* [CUDA Toolkit 9.0](http://nvidia.com/cuda). For details, see - [NVIDIA's documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/). - Append the relevant CUDA pathnames to the `LD_LIBRARY_PATH` environmental - variable as described in the NVIDIA documentation. -* [cuDNN SDK v7](http://developer.nvidia.com/cudnn). For details, see - [NVIDIA's documentation](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/). - Create the `CUDA_HOME` environment variable as described in the NVIDIA - documentation. -* A GPU card with CUDA Compute Capability 3.0 or higher for building TensorFlow - from source. To use the TensorFlow binaries, version 3.5 or higher is required. - See the [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a - list of supported GPU cards. -* [GPU drivers](http://nvidia.com/drivers) that support your version of the CUDA - Toolkit. -* The `libcupti-dev` library is the NVIDIA CUDA Profile Tools Interface. This - library provides advanced profiling support. To install this library, - use the following command for CUDA Toolkit >= 8.0: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo apt-get install cuda-command-line-tools</code> -</pre> - -Add this path to the `LD_LIBRARY_PATH` environmental variable: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}/usr/local/cuda/extras/CUPTI/lib64</code> -</pre> - -* *OPTIONAL*: For optimized performance during inference, install - *NVIDIA TensorRT 3.0*. To install the minimal amount of TensorRT - runtime components required to use with the pre-built `tensorflow-gpu` package: -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb</code> - <code class="devsite-terminal">sudo dpkg -i nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb</code> - <code class="devsite-terminal">sudo apt-get update</code> - <code class="devsite-terminal">sudo apt-get install -y --allow-downgrades libnvinfer-dev libcudnn7-dev=7.0.5.15-1+cuda9.0 libcudnn7=7.0.5.15-1+cuda9.0</code> -</pre> - -Note: For compatibility with the pre-built `tensorflow-gpu` package, use the -Ubuntu *14.04* package of TensorRT (shown above). Use this even when installing -on an Ubuntu 16.04 system. - -To build the TensorFlow-TensorRT integration module from source instead of using -the pre-built binaries, see the -[module documentation](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensorrt#using-tensorrt-in-tensorflow). -For detailed TensorRT installation instructions, see -[NVIDIA's TensorRT documentation](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html). - -To avoid cuDNN version conflicts during later system upgrades, hold the cuDNN -version at 7.0.5: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo apt-mark hold libcudnn7 libcudnn7-dev</code> -</pre> - -To allow upgrades, remove the this hold: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo apt-mark unhold libcudnn7 libcudnn7-dev</code> -</pre> - -If you have an earlier version of the preceding packages, upgrade to the -specified versions. If upgrading is not possible, you can still run TensorFlow -with GPU support by @{$install_sources}. +## TensorFlow GPU support +Note: Due to the number of libraries required, using [Docker](#InstallingDocker) +is recommended over installing directly on the host system. + +The following NVIDIA® <i>hardware</i> must be installed on your system: + +* GPU card with CUDA Compute Capability 3.5 or higher. See + [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a list of + supported GPU cards. + +The following NVIDIA® <i>software</i> must be installed on your system: + +* [GPU drivers](http://nvidia.com/driver). CUDA 9.0 requires 384.x or higher. +* [CUDA Toolkit 9.0](http://nvidia.com/cuda). +* [cuDNN SDK](http://developer.nvidia.com/cudnn) (>= 7.0). Version 7.1 is + recommended. +* [CUPTI](http://docs.nvidia.com/cuda/cupti/) ships with the CUDA Toolkit, but + you also need to append its path to the `LD_LIBRARY_PATH` environment + variable: `export + LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64` +* *OPTIONAL*: [NCCL 2.2](https://developer.nvidia.com/nccl) to use TensorFlow + with multiple GPUs. +* *OPTIONAL*: + [TensorRT](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html) + which can improve latency and throughput for inference for some models. + +To use a GPU with CUDA Compute Capability 3.0, or different versions of the +preceding NVIDIA libraries see +@{$install_sources$installing TensorFlow from Sources}. If using Ubuntu 16.04 +and possibly other Debian based linux distros, `apt-get` can be used with the +NVIDIA repository to simplify installation. + +```bash +# Adds NVIDIA package repository. +sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub +wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb +wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb +sudo dpkg -i cuda-repo-ubuntu1604_9.1.85-1_amd64.deb +sudo dpkg -i nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb +sudo apt-get update +# Includes optional NCCL 2.x. +sudo apt-get install cuda9.0 cuda-cublas-9-0 cuda-cufft-9-0 cuda-curand-9-0 \ + cuda-cusolver-9-0 cuda-cusparse-9-0 libcudnn7=7.1.4.18-1+cuda9.0 \ + libnccl2=2.2.13-1+cuda9.0 cuda-command-line-tools-9-0 +# Optionally install TensorRT runtime, must be done after above cuda install. +sudo apt-get update +sudo apt-get install libnvinfer4=4.1.2-1+cuda9.0 +``` ## Common installation problems We are relying on Stack Overflow to document TensorFlow installation problems -and their remedies. The following table contains links to Stack Overflow -answers for some common installation problems. -If you encounter an error message or other -installation problem not listed in the following table, search for it -on Stack Overflow. If Stack Overflow doesn't show the error message, -ask a new question about it on Stack Overflow and specify -the `tensorflow` tag. +and their remedies. The following table contains links to Stack Overflow answers +for some common installation problems. If you encounter an error message or +other installation problem not listed in the following table, search for it on +Stack Overflow. If Stack Overflow doesn't show the error message, ask a new +question about it on Stack Overflow and specify the `tensorflow` tag. <table> <tr> <th>Link to GitHub or Stack Overflow</th> <th>Error Message</th> </tr> @@ -657,74 +632,67 @@ the `tensorflow` tag. </table> - <a name="TF_PYTHON_URL"></a> + ## The URL of the TensorFlow Python package A few installation mechanisms require the URL of the TensorFlow Python package. The value you specify depends on three factors: - * operating system - * Python version - * CPU only vs. GPU support +* operating system +* Python version +* CPU only vs. GPU support This section documents the relevant values for Linux installations. - ### Python 2.7 CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp27-none-linux_x86_64.whl </pre> - GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp27-none-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements). - ### Python 3.4 CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl </pre> - GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp34-cp34m-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements). - ### Python 3.5 CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp35-cp35m-linux_x86_64.whl </pre> - GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp35-cp35m-linux_x86_64.whl </pre> - Note that GPU support requires the NVIDIA hardware and software described in [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements). @@ -733,16 +701,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp36-cp36m-linux_x86_64.whl </pre> - GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp36-cp36m-linux_x86_64.whl </pre> - Note that GPU support requires the NVIDIA hardware and software described in [NVIDIA requirements to run TensorFlow with GPU support](#NVIDIARequirements). |