How to install TensorFlow on Windows?

c++ windows machine-learning visual-studio tensorflow

Solution 1:

How to install TensorFlow and to use it under Windows?

Updated on 8/4/16

Windows 10 now has a Ubuntu Bash environment, AKA Bash on Ubuntu on Windows, available as a standard option (as opposed to Insider Preview updates for developers). (StackOverflow tag wsl) This option came with the Windows 10 anniversary update (Version 1607) released on 8/2/2016. This allows the use of apt-get to install software packages such as Python and TensorFlow.

Note: Bash on Ubuntu on Windows does not have access to the GPU, so all of the GPU options for installing TensorFlow will not work.

The dated installation instructions for Bash on Ubuntu on Windows are basically correct, but only these steps are necessary:
Prerequisites
Enable the Windows Subsystem for Linux feature (GUI)
Reboot when prompted
Run Bash on Windows

Steps no longer needed:
Turn on Developer Mode
Enable the Windows Subsystem for Linux feature (command-line)

Then install TensorFlow using apt-get

sudo apt-get install python3-pip python3-dev
sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp34-cp34m-linux_x86_64.whl

and now test TensorFlow

$ python3
...
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print(sess.run(a + b))
42
>>> exit()

and run an actual neural network

python3 -m tensorflow.models.image.mnist.convolutional

Earlier Answer

After learning about the developer preview of Bash on Windows.

See Playing with TensorFlow on Windows by Scott Hanselman which uses Bash on Windows 10

Original Answer

Bazel is the problem

TensorFlow is not made with build automation tools such as make, but with Google's in-house build tool Bazel. Bazel only works on systems based on Unix such as Linux and OS X.

Since the current published/known means to build TensorFlow uses Bazel and Bazel does not work on Windows, one can not install or run TensorFlow natively on Windows.

From Bazel FAQ

What about Windows?

Due to its UNIX heritage, porting Bazel to Windows is significant work. For example, Bazel uses symlinks extensively, which has varying levels of support across Windows versions.

We are currently actively working on improving Windows support, but it's still ways from being usable.

Status

See: TensorFlow issue #17
See: Bazel issue #276

Solutions

The solutions are listed in the order of complexity and work needed; from about an hour to may not even work.

  1. Docker
    ~ 1 hour

Docker installation

Docker is a system to build self contained versions of a Linux operating system running on your machine. When you install and run TensorFlow via Docker it completely isolates the installation from pre-existing packages on your machine.

Also look at TensorFlow - which Docker image to use?

  1. OS X
    ~ 1 hour

If you have a current Mac running OS X then see: Installation for Mac OS X

  1. Linux

The recommend Linux system tends to be Ubuntu 14.04 LTS (Download page).

a. Virtual Machine - Hardware Virtualization - Full Virtualization
~ 3 hours

Download and install a virtual machine such as the commercial VMware or the free Virtual Box, after which you can install Linux and then install TensorFlow.

When you go to install TensorFlow you will be using Pip - Python's package management system. Visual Studio users should think NuGet. The packages are known as wheels.

See: Pip Installation

If you need to build from the source then see: Installing From Sources
~ 4 hours

Note: If you plan on using a Virtual Machine and have never done so before, consider using the Docker option instead, since Docker is the Virtual Machine, OS and TensorFlow all packaged together.

b. Dual boot
~ 3 hours

If you want to run TensorFlow on the same machine that you have Windows and make use of the GPU version then you will most likely have to use this option as running on a hosted Virtual Machine, Type 2 hypervisor, will not allow you access to the GPU.

  1. Remote machine
    ~ 4 hours

If you have remote access to another machine that you can install the Linux OS and TensorFlow software on and allow remote connections to, then you can use your Windows machine to present the remote machine as an application running on Windows.

  1. Cloud Service
    I have no experience with this. Please edit answer if you know.

Cloud services such as AWS are being used.

From TensorFlow Features

Want to run the model as a service in the cloud? Containerize with Docker and TensorFlow just works.

From Docker

Running Docker on AWS provides a highly reliable, low-cost way to quickly build, ship, and run distributed applications at scale. Deploy Docker using AMIs from the AWS Marketplace.

  1. Wait for Bazel to work on Windows.

Currently it appears the only hold up is Bazel, however Bazel's roadmap list working on Windows should be available this year.

There are two features listed for Windows:

2016‑02  Bazel can bootstrap itself on Windows without requiring admin privileges.  

2016‑12  Full Windows support for Android: Android feature set is identical for Windows and Linux/OS X.
  1. Build TensorFlow by hand.
    A few days or more depending on you skill level. I gave up on this one; too many subprojects to build and files to locate.

Remember that Bazel is only used to build TensorFlow. If you get the commands Bazel runs and the correct source code and libraries you should be able to build TensorFlow on Windows. See: How do I get the commands executed by Bazel.

While I have not researched this more, you can look at the continuous integration info for needed files and info on how to they build it for testing. (Readme) (site)

  1. Build Bazel on Windows
    A few days or more depending on you skill level. I gave up on this one also; could not find the necessary source files needed for Windows.

There is a public experimental source code version of Bazel that boots on Windows. You may be able to leverage this into getting Bazel to work on Windows, etc.

Also these solutions require the use of Cygwin or MinGW which adds another layer of complexity.

  1. Use alternative build system such as Make
    If you get this one to work I would like to see in on GitHub.

This currently does not exist for TensorFlow. It is a feature request.

See: TensorFlow issue 380

  1. Cross Build
    If you get this one to work I would like to see in on GitHub.

You build TensorFlow on Linux using Bazel but change the build process to output a wheel that can be installed on Windows. This will require detailed knowledge of Bazel to change the configuration, and locating the source code and libraries that work with Windows. An option I would only suggest as a last resort. It may not even be possible.

  1. Run on the new Windows Subsystem for Linux.

See: Windows Subsystem for Linux Overview

You will know as much as I do by reading the referenced article.

Can I use Bazel for Windows for production use?

Since it is experimental software I would not use on a production machine.

Remember that you only need Bazel to build TensorFlow. So use the experimental code on a non production machine to build the wheel, then install the wheel on a production machine. See: Pip Installation

TLDR;

Currently I have several versions for learning. Most use a VMWare 7.1 Workstation to host Ubuntu 14.04 LTS or Ubuntu 15 or Debian. I also have one dual boot of Ubuntu 14.04 LTS on my Windows machine to access the GPU as the machine with VMware does not have the proper GPU. I would recommend that you give these machines at least 8G of memory either as RAM or RAM and swap space as I have run out of memory a few times.

Solution 2:

I can confirm that it works in the Windows Subsystem for Linux! And it is also very straightforward.

In the Ubuntu Bash on Windows 10, first update the package index:

apt-get update

Then install pip for Python 2:

sudo apt-get install python-pip python-dev

Install tensorflow:

sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl

The package is now installed an you can run the CNN sample on the MNIST set:

cd /usr/local/lib/python2.7/dist-packages/tensorflow/models/image/mnist

python convolutional.py

I just tested the CPU package for now.

I blogged about it: http://blog.mosthege.net/2016/05/11/running-tensorflow-with-native-linux-binaries-in-the-windows-subsystem-for-linux/

cheers

~michael

Solution 3:

Sorry for the excavation, but this question is quite popular, and now it has a different answer.

Google officially announced the addition of Windows (7, 10, and Server 2016) support for TensorFlow: developers.googleblog.com

The Python module can be installed using pip with a single command:

C:\> pip install tensorflow

And if you need GPU support:

 C:\> pip install tensorflow-gpu

TensorFlow manual - How to install pip on windows

Another useful information are included in release notes: https://github.com/tensorflow/tensorflow/releases

UPD: As @m02ph3u5 right mentioned in the comments TF for windows supports only Python 3.5.x Installing TensorFlow on Windows with native pip

Solution 4:

Installing TensorFlow

TensorFlow currently supports only Python 3.5 64-bit. Both CPU and GPU are supported. Here are some installation instructions assuming you do not have Python 3.5 64-bit:

  1. Download and install Microsoft Visual C++ 2015 Redistributable Update 3: https://www.microsoft.com/en-us/download/details.aspx?id=53587 (required by Python 3.5 and TensorFlow)
  2. Download and install Python 3.5 64-bit: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe
  3. Install pip as follows: download https://bootstrap.pypa.io/get-pip.py, then run python get-pip.py
  4. Install TensorFlow with either pip install tensorflow (CPU version) or pip install tensorflow-gpu (GPU version --> requires CUDA to be installed).

Testing TensorFlow

You can now run something like following to test whether TensorFlow is working fine:

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
a = tf.constant(10)
b = tf.constant(32)
print(sess.run(a + b))

TensorFlow comes with a few models, which are located in C:\Python35\Lib\site-packages\tensorflow\models\ (assuming you installed python in C:\Python35). For example, you can run in the console: 

python -m tensorflow.models.image.mnist.convolutional

or

python C:\Python35\Lib\site-packages\tensorflow\models\image\mnist\convolutional.py

Limitations of TensorFlow on Windows

Initial support for building TensorFlow on Microsoft Windows was added on 2016-10-05 in commit 2098b9abcf20d2c9694055bbfd6997bc00b73578:

This PR contains an initial version of support for building TensorFlow (CPU only) on Windows using CMake. It includes documentation for building with CMake on Windows, platform-specific code for implementing core functions on Windows, and CMake rules for building the C++ example trainer program and a PIP package (Python 3.5 only). The CMake rules support building TensorFlow with Visual Studio 2015.

Windows support is a work in progress, and we welcome your feedback and contributions.

For full details of the features currently supported and instructions for how to build TensorFlow on Windows, please see the file tensorflow/contrib/cmake/README.md.

The Microsoft Windows support was introduced in TensorFlow in version 0.12 RC0 (release notes):

TensorFlow now builds and runs on Microsoft Windows (tested on Windows 10, Windows 7, and Windows Server 2016). Supported languages include Python (via a pip package) and C++. CUDA 8.0 and cuDNN 5.1 are supported for GPU acceleration. Known limitations include: It is not currently possible to load a custom op library. The GCS and HDFS file systems are not currently supported. The following ops are not currently implemented: DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropInput, Dequantize, Digamma, Erf, Erfc, Igamma, Igammac, Lgamma, Polygamma, QuantizeAndDequantize, QuantizedAvgPool, QuantizedBatchNomWithGlobalNormalization, QuantizedBiasAdd, QuantizedConcat, QuantizedConv2D, QuantizedMatmul, QuantizedMaxPool, QuantizeDownAndShrinkRange, QuantizedRelu, QuantizedRelu6, QuantizedReshape, QuantizeV2, RequantizationRange, and Requantize.

Solution 5:

Now Tensorflow is officially supported in Windows, you can install it using pip command of Python 3.5 without compile it yourself

CPU Version

pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.0-cp35-cp35m-win_amd64.whl

cp35 indicates python 3.5 wheel, 0.12.0 the version, you can edit these according your preference, or to install latest CPU version available you can use

pip install --upgrade tensorflow

GPU Version

pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-0.12.0-cp35-cp35m-win_amd64.whl

cp35 indicates python 3.5 wheel, 0.12.0 the version, you can edit these according your preference, or to install latest GPU version available you can use

pip install --upgrade tensorflow-gpu

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