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diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md deleted file mode 100644 index 5fcfa4b988..0000000000 --- a/tensorflow/docs_src/install/install_linux.md +++ /dev/null @@ -1,714 +0,0 @@ -# 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 - -## 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. - -## 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) - -<a name="InstallingVirtualenv"></a> - -### Use `pip` in a virtual environment - -Key Point: Using a virtual environment is the recommended install method. - -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. - -##### 1. Install Python, `pip`, and `virtualenv`. - -On Ubuntu, Python is automatically installed and `pip` is *usually* installed. -Confirm the `python` and `pip` versions: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">python -V # or: python3 -V</code> - <code class="devsite-terminal">pip -V # or: pip3 -V</code> -</pre> - -To install these packages on Ubuntu: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo apt-get install python-pip python-dev python-virtualenv # for Python 2.7</code> - <code class="devsite-terminal">sudo apt-get install python3-pip python3-dev python-virtualenv # for Python 3.n</code> -</pre> - -We *recommend* using `pip` version 8.1 or higher. If using a release before -version 8.1, upgrade `pip`: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">pip install --upgrade pip</code> -</pre> - -If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is -installed, use `easy_install` to install `pip`: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">easy_install -U pip</code> -</pre> - -##### 2. Create a directory for the virtual environment and choose a Python interpreter. - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">mkdir ~/tensorflow # somewhere to work out of</code> - <code class="devsite-terminal">cd ~/tensorflow</code> - <code># Choose one of the following Python environments for the ./venv directory:</code> - <code class="devsite-terminal">virtualenv --system-site-packages <var>venv</var> # Use python default (Python 2.7)</code> - <code class="devsite-terminal">virtualenv --system-site-packages -p python3 <var>venv</var> # Use Python 3.n</code> -</pre> - -##### 3. Activate the Virtualenv environment. - -Use one of these shell-specific commands to activate the virtual environment: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">source ~/tensorflow/<var>venv</var>/bin/activate # bash, sh, ksh, or zsh</code> - <code class="devsite-terminal">source ~/tensorflow/<var>venv</var>/bin/activate.csh # csh or tcsh</code> - <code class="devsite-terminal">. ~/tensorflow/<var>venv</var>/bin/activate.fish # fish</code> -</pre> - -When the Virtualenv is activated, the shell prompt displays as `(venv) $`. - -##### 4. Upgrade `pip` in the virtual environment. - -Within the active virtual environment, upgrade `pip`: - -<pre class="prettyprint lang-bsh"> -(venv)$ pip install --upgrade pip -</pre> - -You can install other Python packages within the virtual environment without -affecting packages outside the `virtualenv`. - -##### 5. Install TensorFlow in the virtual environment. - -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 - -Within an active Virtualenv environment, use `pip` to install the package: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">pip install --upgrade tensorflow</code> -</pre> - -Use `pip list` to show the packages installed in the virtual environment. -[Validate the install](#ValidateYourInstallation) and test the version: - -<pre class="prettyprint lang-bsh"> -(venv)$ python -c "import tensorflow as tf; print(tf.__version__)" -</pre> - -Success: TensorFlow is now installed. - -Use the `deactivate` command to stop the Python virtual environment. - -#### Problems - -If the above steps failed, try installing the TensorFlow binary using the remote -URL of the `pip` package: - -<pre class="prettyprint lang-bsh"> -(venv)$ pip install --upgrade <var>remote-pkg-URL</var> # Python 2.7 -(venv)$ pip3 install --upgrade <var>remote-pkg-URL</var> # Python 3.n -</pre> - -The <var>remote-pkg-URL</var> depends on the operating system, Python version, -and GPU support. See [here](#the_url_of_the_tensorflow_python_package) for the -URL naming scheme and location. - -See [Common Installation Problems](#common_installation_problems) if you -encounter problems. - -#### Uninstall TensorFlow - -To uninstall TensorFlow, remove the Virtualenv directory you created in step 2: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">deactivate # stop the virtualenv</code> - <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. - -Since a system install is not isolated, it could interfere with other -Python-based installations. But if you understand `pip` and your Python -environment, a system `pip` install is straightforward. - -See the -[REQUIRED_PACKAGES section of setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) -for a list of packages that TensorFlow installs. - -##### 1. Install Python, `pip`, and `virtualenv`. - -On Ubuntu, Python is automatically installed and `pip` is *usually* installed. -Confirm the `python` and `pip` versions: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">python -V # or: python3 -V</code> - <code class="devsite-terminal">pip -V # or: pip3 -V</code> -</pre> - -To install these packages on Ubuntu: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">sudo apt-get install python-pip python-dev # for Python 2.7</code> - <code class="devsite-terminal">sudo apt-get install python3-pip python3-dev # for Python 3.n</code> -</pre> - -We *recommend* using `pip` version 8.1 or higher. If using a release before -version 8.1, upgrade `pip`: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">pip install --upgrade pip</code> -</pre> - -If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is -installed, use `easy_install` to install `pip`: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">easy_install -U pip</code> -</pre> - -##### 2. Install TensorFlow on system. - -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 - -And use `pip` to install the package for Python 2 or 3: - -<pre class="prettyprint lang-bsh"> - <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. -[Validate the install](#ValidateYourInstallation) and test the version: - -<pre class="prettyprint lang-bsh"> - <code class="devsite-terminal">python -c "import tensorflow as tf; print(tf.__version__)"</code> -</pre> - -Success: TensorFlow is now installed. - -#### Problems - -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">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, -and GPU support. See [here](#the_url_of_the_tensorflow_python_package) for the -URL naming scheme and location. - -See [Common Installation Problems](#common_installation_problems) if you -encounter problems. - -#### Uninstall TensorFlow - -To uninstall TensorFlow on your system, use one of following commands: - -<pre class="prettyprint lang-bsh"> - <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. - -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/). - -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: - -<pre> -$ docker run -it <i>-p hostPort:containerPort TensorFlowCPUImage</i> -</pre> - -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 - 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. - - 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: - -<pre> -$ <b>docker run -it tensorflow/tensorflow bash</b> -</pre> - -The following command also launches the latest TensorFlow CPU binary image in a -Docker container. However, in this Docker container, you can run TensorFlow -programs in a Jupyter notebook: - -<pre> -$ <b>docker run -it -p 8888:8888 tensorflow/tensorflow</b> -</pre> - -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)): - -<pre> -$ <b>nvidia-docker run -it</b> <i>-p hostPort:containerPort TensorFlowGPUImage</i> -</pre> - -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: - -<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: - -<pre> -$ <b>nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:latest-gpu</b> -</pre> - -The following command installs an older TensorFlow version (0.12.1): - -<pre> -$ <b>nvidia-docker run -it -p 8888:8888 tensorflow/tensorflow:0.12.1-gpu</b> -</pre> - -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). - -<a name="InstallingAnaconda"></a> - -### Use `pip` in Anaconda - -Anaconda provides the `conda` utility to create a virtual environment. However, -within Anaconda, we recommend installing TensorFlow using the `pip install` -command and *not* with the `conda install` command. - -Caution: `conda` is a community supported package this is not officially -maintained by the TensorFlow team. Use this package at your own risk since it is -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. - -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: - - <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: - - <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: - - <pre> - (tensorflow)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-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. - -### Prepare your environment - -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. - -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: - -<pre>$ <b>python</b></pre> - -Enter the following short program inside the python interactive shell: - -```python -# Python -import tensorflow as tf -hello = tf.constant('Hello, TensorFlow!') -sess = tf.Session() -print(sess.run(hello)) -``` - -If the system outputs the following, then you are ready to begin writing -TensorFlow programs: - -<pre>Hello, TensorFlow!</pre> - -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 - -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 -[installing TensorFlow from Sources](../install/install_sources.md). 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. - -<table> -<tr> <th>Link to GitHub or Stack Overflow</th> <th>Error Message</th> </tr> - -<tr> - <td><a href="https://stackoverflow.com/q/36159194">36159194</a></td> - <td><pre>ImportError: libcudart.so.<i>Version</i>: cannot open shared object file: - No such file or directory</pre></td> -</tr> - -<tr> - <td><a href="https://stackoverflow.com/q/41991101">41991101</a></td> - <td><pre>ImportError: libcudnn.<i>Version</i>: cannot open shared object file: - No such file or directory</pre></td> -</tr> - -<tr> - <td><a href="http://stackoverflow.com/q/36371137">36371137</a> and - <a href="#Protobuf31">here</a></td> - <td><pre>libprotobuf ERROR google/protobuf/src/google/protobuf/io/coded_stream.cc:207] A - protocol message was rejected because it was too big (more than 67108864 bytes). - To increase the limit (or to disable these warnings), see - CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.</pre></td> -</tr> - -<tr> - <td><a href="https://stackoverflow.com/q/35252888">35252888</a></td> - <td><pre>Error importing tensorflow. Unless you are using bazel, you should - not try to import tensorflow from its source directory; please exit the - tensorflow source tree, and relaunch your python interpreter from - there.</pre></td> -</tr> - -<tr> - <td><a href="https://stackoverflow.com/q/33623453">33623453</a></td> - <td><pre>IOError: [Errno 2] No such file or directory: - '/tmp/pip-o6Tpui-build/setup.py'</tt></pre> -</tr> - -<tr> - <td><a href="http://stackoverflow.com/q/42006320">42006320</a></td> - <td><pre>ImportError: Traceback (most recent call last): - File ".../tensorflow/core/framework/graph_pb2.py", line 6, in <module> - from google.protobuf import descriptor as _descriptor - ImportError: cannot import name 'descriptor'</pre> - </td> -</tr> - -<tr> - <td><a href="https://stackoverflow.com/questions/35190574">35190574</a> </td> - <td><pre>SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify - failed</pre></td> -</tr> - -<tr> - <td><a href="http://stackoverflow.com/q/42009190">42009190</a></td> - <td><pre> - Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow - Found existing installation: setuptools 1.1.6 - Uninstalling setuptools-1.1.6: - Exception: - ... - [Errno 1] Operation not permitted: - '/tmp/pip-a1DXRT-uninstall/.../lib/python/_markerlib' </pre></td> -</tr> - -<tr> - <td><a href="http://stackoverflow.com/questions/36933958">36933958</a></td> - <td><pre> - ... - Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow - Found existing installation: setuptools 1.1.6 - Uninstalling setuptools-1.1.6: - Exception: - ... - [Errno 1] Operation not permitted: - '/tmp/pip-a1DXRT-uninstall/System/Library/Frameworks/Python.framework/ - Versions/2.7/Extras/lib/python/_markerlib'</pre> - </td> -</tr> - -</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 - -This section documents the relevant values for Linux installations. - -### Python 2.7 - -CPU only: - -<pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-cp27-none-linux_x86_64.whl -</pre> - -GPU support: - -<pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-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-1.10.0-cp34-cp34m-linux_x86_64.whl -</pre> - -GPU support: - -<pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-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-1.10.0-cp35-cp35m-linux_x86_64.whl -</pre> - -GPU support: - -<pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-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). - -### Python 3.6 - -CPU only: - -<pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-cp36-cp36m-linux_x86_64.whl -</pre> - -GPU support: - -<pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-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). |