Installing TensorFlow on Mac OS X

This guide explains how to install TensorFlow on Mac OS X.

Determine which TensorFlow to install

You must choose the type of TensorFlow to install. Your choices are as follows:

  • TensorFlow with CPU support only. If your system does not have a NVIDIA CUDA┬« GPU, you should install this version. Note that TensorFlow with CPU support is typically easier to install than TensorFlow with GPU support. Therefore, even if you have an NVIDIA CUDA GPU, we recommend installing this version first as a diagnostic step just in case you run into problems installing TensorFlow with GPU support.
  • TensorFlow with GPU support. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA CUDA GPU meeting the prerequisites shown below and you need to run performance-critical applications, you should ultimately install this version.

Requirements to run TensorFlow with GPU support

If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system:

  • CUDA Toolkit 8.0. For details, see NVIDIA's documentation. Ensure that you append the relevant CUDA pathnames to the LD_LIBRARY_PATH environment variable as described in the NVIDIA documentation.
  • The NVIDIA drivers associated with CUDA Toolkit 8.0.
  • cuDNN v5.1. For details, see NVIDIA's documentation. Ensure that you create the CUDA_HOME environment variable as described in the NVIDIA documentation.
  • GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards.

If you have an earlier version of the preceding packages, please upgrade to the specified versions. If upgrading is not possible, you may still run TensorFlow with GPU support, but only if you do both of the following:

  • Install TensorFlow from sources (as described in Installing TensorFlow from Sources.
  • Install or upgrade to at least the following NVIDIA versions:
    • CUDA toolkit 7.0 or greater
    • cuDNN v3 or greater
    • GPU card with CUDA Compute Capability 3.0 or higher.

Determine how to install TensorFlow

You must pick the mechanism by which you install TensorFlow. The supported choices are as follows:

  • virtualenv
  • "native" pip
  • Docker
  • installing from sources, which is for experts and is documented in a separate guide.

We recommend the virtualenv installation. Virtualenv is a virtual Python environment isolated from other Python development, incapable of interfering with or being affected by other Python programs on the same machine. During the virtualenv installation process, you will install not only TensorFlow but also all the packages that TensorFlow requires. (This is actually pretty easy.) To start working with TensorFlow, you simply need to "activate" the virtual environment. All in all, virtualenv provides a safe and reliable mechanism for installing and running TensorFlow.

Native pip installs TensorFlow directly on your system without going through any container or virtual environment system. Since a native pip installation is not walled-off, the pip installation might interfere with or be influenced by other Python-based installations on your system. Furthermore, you might need to disable System Integrity Protection (SIP) in order to install through native pip. However, if you understand SIP, pip, and your Python environment, a native pip installation is relatively easy to perform.

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.

In Anaconda, you may use conda to create a virtual environment. However, within Anaconda, we recommend installing TensorFlow with the pip install command, not with the conda install command.

NOTE: The conda package is community supported, not officially supported. That is, the TensorFlow team neither tests nor maintains the conda package. Use that package at your own risk.

Installing with virtualenv

Take the following steps to install TensorFlow with Virtualenv:

  1. Start a terminal (a shell). You'll perform all subsequent steps in this shell.

  2. Install pip and virtualenv by issuing the following command:

     $ sudo easy_install pip
     $ sudo pip install --upgrade virtualenv
     

  3. Create a virtualenv environment by issuing a command of the following format:

     $ virtualenv --system-site-packages targetDirectory
     

    The targetDirectory identifies the top of the virtualenv tree. Our instructions assume that targetDirectory is ~/tensorflow, but you may choose any directory.

  4. Activate the virtualenv environment by issuing one of the following commands:

     $ source ~/tensorflow/bin/activate      # If using bash, sh, ksh, or zsh
     $ source ~/tensorflow/bin/activate.csh  # If using csh or tcsh
     

    The preceding source command should change your prompt to the following:

     (tensorflow)$
     

  5. If pip version 8.1 or later is installed on your system, issue one of the following commands to install TensorFlow and all the packages that TensorFlow requires into the active Virtualenv environment:

     $ pip install --upgrade tensorflow      # for Python 2.7
     $ pip3 install --upgrade tensorflow     # for Python 3.n
     $ pip install --upgrade tensorflow-gpu  # for Python 2.7 and GPU
     $ pip3 install --upgrade tensorflow-gpu # for Python 3.n and GPU
     

    If the preceding command succeed, skip Step 6. If it failed, perform Step 6.

  6. Optional. If Step 5 failed (typically because you invoked a pip version lower than 8.1), install TensorFlow in the active virtualenv environment by issuing a command of the following format:

     $ pip install --upgrade TF_BINARY_URL   # Python 2.7
     $ pip3 install --upgrade TF_BINARY_URL  # Python 3.N
     

    where TF_BINARY_URL identifies the URL of the TensorFlow Python package. The appropriate value of TF_BINARY_URL depends on the operating system, Python version, and GPU support. Find the appropriate value for TF_BINARY_URL for your system here. For example, if you are installing TensorFlow for Mac OS X, Python version 3.4, and CPU-only support, the command to install TensorFlow in the active Virtualenv is as follows:

     $ pip3 install --upgrade \
     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py3-none-any.whl
     

If you encounter installation problems, see Common Installation Problems.

Next Steps

After installing TensorFlow, validate your installation to confirm that the installation worked properly.

Note that you must activate the virtualenv environment each time you use TensorFlow in a new shell. If the virtualenv environment is not currently active (that is, the prompt is not (tensorflow), invoke one of the following commands:

$ source ~/tensorflow/bin/activate      # bash, sh, ksh, or zsh
$ source ~/tensorflow/bin/activate.csh  # csh or tcsh

Your prompt will transform to the following to indicate that your tensorflow environment is active:

(tensorflow)$

When the virtualenv environment is active, you may run TensorFlow programs from this shell.

When you are done using TensorFlow, you may deactivate the environment by issuing the following command:

(tensorflow)$ deactivate

The prompt will revert back to your default prompt (as defined by PS1).

Uninstalling TensorFlow

If you want to uninstall TensorFlow, simply remove the tree you created. For example:

$ rm -r ~/tensorflow

Installing with native pip

We have uploaded the TensorFlow binaries to PyPI. Therefore, you can install TensorFlow through pip.

The REQUIRED_PACKAGES section of setup.py lists the packages that pip will install or upgrade.

Prerequisite: Python

In order to install TensorFlow, your system must contain one of the following Python versions:

  • Python 2.7
  • Python 3.3+

If your system does not already have one of the preceding Python versions, install it now.

When installing Python, you might need to disable System Integrity Protection (SIP) to permit any entity other than Mac App Store to install software.

Prerequisite: pip

Pip installs and manages software packages written in Python. If you intend to install with native pip, then one of the following flavors of pip must be installed on your system:

  • pip, for Python 2.7
  • pip3, for Python 3.n.

pip or pip3 was probably installed on your system when you installed Python. To determine whether pip or pip3 is actually installed on your system, issue one of the following commands:

$ pip -V  # for Python 2.7
$ pip3 -V # for Python 3.n 

We strongly recommend pip or pip3 version 8.1 or higher in order to install TensorFlow. If pip or pip3 8.1 or later is not installed, issue the following commands to install or upgrade:

$ sudo easy_install --upgrade pip
$ sudo easy_install --upgrade six 

Install TensorFlow

Assuming the prerequisite software is installed on your Mac, take the following steps:

  1. Ensure proper protobuf dependencies by issuing one of the following commands:

    $ sudo pip uninstall tensorflow # for Python 2.7
     $ sudo pip3 uninstall tensorflow # for Python 3.n

  2. Install TensorFlow by invoking one of the following commands:

    $ pip install tensorflow      # Python 2.7; CPU support (no GPU support)
     $ pip3 install tensorflow     # Python 3.n; CPU support (no GPU support)
     $ pip install tensorflow-gpu  # Python 2.7;  GPU support
     $ pip3 install tensorflow-gpu # Python 3.n; GPU support 

    If the preceding command runs to completion, you should now validate your installation.

  3. (Optional.) If Step 2 failed, install the latest version of TensorFlow by issuing a command of the following format:

    $ sudo pip  install --upgrade TF_BINARY_URL   # Python 2.7
     $ sudo pip3 install --upgrade TF_BINARY_URL   # Python 3.N 

    where TF_BINARY_URL identifies the URL of the TensorFlow Python package. The appropriate value of TF_BINARY_URL depends on the operating system, Python version, and GPU support. Find the appropriate value for TF_BINARY_URL here. For example, if you are installing TensorFlow for Mac OS, Python version 3.4, and CPU-only support, issue the following command:

     $ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py3-none-any.whl
     

    If the preceding command fails, see Common installation problems.

Next Steps

After installing TensorFlow, validate your installation to confirm that the installation worked properly.

Uninstalling TensorFlow

To uninstall TensorFlow, issue one of following commands:

$ pip uninstall tensorflow
$ pip3 uninstall tensorflow 

Installing with Docker

Follow these steps to install TensorFlow through Docker.

  1. Install Docker on your machine as described in the Docker documentation.

  2. Launch a Docker container that contains one of the TensorFlow binary images.

The remainder of this section explains how to launch a Docker container.

Note: You can only launch a Docker container with CPU support. (You cannot launch a Docker container with GPU support.)

To launch a Docker container that holds the TensorFlow binary image, enter a command of the following format:

$ docker run -it -p hostPort:containerPort TensorFlowImage

where:

  • -p hostPort:containerPort is optional. If you'd like to run TensorFlow programs from the shell, omit this option. If you'd like to run TensorFlow programs from Jupyter notebook, set both hostPort and containerPort to 8888. If you'd like to run TensorBoard inside the container, add a second -p flag, setting both hostPort and containerPort to 6006.
  • TensorFlowImage is required. It identifies the Docker container. You must specify one of the following values:
    • gcr.io/tensorflow/tensorflow: TensorFlow binary image.
    • gcr.io/tensorflow/tensorflow:latest-devel: TensorFlow Binary image plus source code.

gcr.io is the Google Container Registry. Note that some TensorFlow images are also available at dockerhub.

For example, the following command launches a TensorFlow CPU binary image in a Docker container from which you can run TensorFlow programs in a shell:

$ docker run -it gcr.io/tensorflow/tensorflow bash

The following command also launches a TensorFlow CPU binary image in a Docker container. However, in this Docker container, you can run TensorFlow programs in a Jupyter notebook:

$ docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow

Docker will download the TensorFlow binary image the first time you launch it.

Next Steps

You should now validate your installation.

Installing with Anaconda

The Anaconda installation is community supported, not officially supported.

Take the following steps to install TensorFlow in an Anaconda environment:

  1. Follow the instructions on the Anaconda download site to download and install Anaconda.

  2. Create a conda environment named tensorflow by invoking the following command:

    $ conda create -n tensorflow

  3. Activate the conda environment by issuing the following command:

    $ source activate tensorflow
     (tensorflow)$  # Your prompt should change

  4. Issue a command of the following format to install TensorFlow inside your conda environment:

    (tensorflow)$ pip install --ignore-installed --upgrade $TF_PYTHON_URL 

    where TF_PYTHON_URL is the URL of the TensorFlow Python package. For example, the following command installs the CPU-only version of TensorFlow for Python 3.4:

     (tensorflow)$ pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py3-none-any.whl
     

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 that runs bash. For example:

$ docker run -it gcr.io/tensorflow/tensorflow bash

Run a short TensorFlow program

Invoke python from your shell as follows:

$ python

Enter the following short program inside the python interactive shell:

>>> 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:

Hello, TensorFlow!

If you are new to TensorFlow, see Getting Started with TensorFlow.

If the system outputs an error message instead of a greeting, see Common installation problems.

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.

Stack Overflow Link Error Message
36159194
ImportError: libcudart.so.Version: cannot open shared object file:
  No such file or directory
41991101
ImportError: libcudnn.Version: cannot open shared object file:
  No such file or directory
42006320
ImportError: Traceback (most recent call last):
File ".../tensorflow/core/framework/graph_pb2.py", line 6, in 
from google.protobuf import descriptor as _descriptor
ImportError: cannot import name 'descriptor'
33623453
IOError: [Errno 2] No such file or directory:
  '/tmp/pip-o6Tpui-build/setup.py'
35190574
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify
  failed
42009190
  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' 
33622019
ImportError: No module named copyreg
37810228 During a `pip install` operation, the system returns:
OSError: [Errno 1] Operation not permitted
33622842 An import tensorflow` statement triggers an error such as the following:
Traceback (most recent call last):
  File "", line 1, in 
  File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py",
    line 4, in 
    from tensorflow.python import *
    ...
  File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py",
    line 22, in 
    serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"d\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02
      \x03(\x0b\x32
      .tensorflow.TensorShapeProto.Dim\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01
      \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tb\x06proto3')
  TypeError: __init__() got an unexpected keyword argument 'syntax'
42073336 An `import tensorflow` statement triggers the following error:
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.dylib locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.dylib locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.dylib locally
"import tensorflow" terminated by signal SIGSEGV (Address boundary error)
42075397 A `pip install` command triggers the following error:
...
You have not agreed to the Xcode license agreements, please run
'xcodebuild -license' (for user-level acceptance) or
'sudo xcodebuild -license' (for system-wide acceptance) from within a
Terminal window to review and agree to the Xcode license agreements.
...
  File "numpy/core/setup.py", line 653, in get_mathlib_info

    raise RuntimeError("Broken toolchain: cannot link a simple C program")

RuntimeError: Broken toolchain: cannot link a simple C program

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 Mac OS installations.

Python 2.7

CPU only:

https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py2-none-any.whl

GPU support:

https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.1-py2-none-any.whl

Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see Installing TensorFlow from Sources.

Python 3.4 or 3.5

CPU only:

https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py3-none-any.whl

GPU support:

https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.1-py3-none-any.whl

Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see Installing TensorFlow from Sources.

Protobuf pip package 3.1

You can skip this section unless you are seeing problems related to the protobuf pip package.

NOTE: If your TensorFlow programs are running slowly, you might have a problem related to the protobuf pip package.

The TensorFlow pip package depends on protobuf pip package version 3.1. The protobuf pip package downloaded from PyPI (when invoking pip install protobuf) is a Python-only library containing Python implementations of proto serialization/deserialization that can run 10x-50x slower than the C++ implementation. Protobuf also supports a binary extension for the Python package that contains fast C++ based proto parsing. This extension is not available in the standard Python-only pip package. We have created a custom binary pip package for protobuf that contains the binary extension. To install the custom binary protobuf pip package, invoke one of the following commands:

  • for Python 2.7:

  $ pip install --upgrade \
  https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.1.0-cp27-none-linux_x86_64.whl
  

  • for Python 3.5:

  $ pip3 install --upgrade \
  https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.1.0-cp35-none-linux_x86_64.whl
  

Installing this protobuf package will overwrite the existing protobuf package. Note that the binary pip package already has support for protobufs larger than 64MB, which should fix errors such as these:

[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.