STT-tensorflow/tensorflow/g3doc/get_started/os_setup.md

Download and Setup

You can install TensorFlow either from our provided binary packages or from the github source.

Requirements

The TensorFlow Python API currently requires Python 2.7. We are adding support for Python 3.

The GPU version (Linux only) currently requires the Cuda Toolkit 7.0 and CUDNN 6.5 V2. Please see Cuda installation.

Overview

We support different ways to install TensorFlow:

• Pip install: Install TensorFlow on your machine, possibly upgrading previously installed Python packages. May impact existing Python programs on your machine.
• Virtualenv install: Install TensorFlow in its own directory, not impacting any existing Python programs on your machine.
• Docker install: Run TensorFlow in a Docker container isolated from all other programs on your machine.

If you are familiar with Pip, Virtualenv, or Docker, please feel free to adapt the instructions to your particular needs. The names of the pip and Docker images are listed in the corresponding installation sections.

If you encounter installation errors, see common problems for some solutions.

Pip Installation

Pip is a package management system used to install and manage software packages written in Python.

The packages that will be installed or upgraded during the pip install are listed in the REQUIRED_PACKAGES section of setup.py

Install pip if not already installed:

# Ubuntu/Linux 64-bit
$ sudo apt-get install python-pip python-dev

# Mac OS X
$ sudo easy_install pip

Install TensorFlow:

# Ubuntu/Linux 64-bit, CPU only:
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

# Ubuntu/Linux 64-bit, GPU enabled:
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

# Mac OS X, CPU only:
$ sudo easy_install --upgrade six
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl

You can now test your installation.

Virtualenv installation

Virtualenv is a tool to keep the dependencies required by different Python projects in separate places. The Virtualenv installation of TensorFlow will not override pre-existing version of the Python packages needed by TensorFlow.

With Virtualenv the installation is as follows:

• Install pip and Virtualenv.
• Create a Virtualenv environment.
• Activate the Virtualenv environment and install TensorFlow in it.
• After the install you will activate the Virtualenv environment each time you want to use TensorFlow.

Install pip and Virtualenv:

# Ubuntu/Linux 64-bit
$ sudo apt-get install python-pip python-dev python-virtualenv

# Mac OS X
$ sudo easy_install pip
$ sudo pip install --upgrade virtualenv

Create a Virtualenv environment in the directory ~/tensorflow:

$ virtualenv --system-site-packages ~/tensorflow

Activate the environment and use pip to install TensorFlow inside it:

$ source ~/tensorflow/bin/activate  # If using bash
$ source ~/tensorflow/bin/activate.csh  # If using csh
(tensorflow)$  # Your prompt should change

# Ubuntu/Linux 64-bit, CPU only:
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

# Ubuntu/Linux 64-bit, GPU enabled:
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

# Mac OS X, CPU only:
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl

With the Virtualenv environment activated, you can now test your installation.

# When you are done using TensorFlow, deactivate the environment.
(tensorflow)$ deactivate

$  # Your prompt should change back

To use TensorFlow later you will have to activate the Virtualenv environment again:

$ source ~/tensorflow/bin/activate  # If using bash.
$ source ~/tensorflow/bin/activate.csh  # If using csh.
(tensorflow)$  # Your prompt should change.
# Run Python programs that use TensorFlow.
...
# When you are done using TensorFlow, deactivate the environment.
(tensorflow)$ deactivate

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.

We provide 2 Docker images:

• b.gcr.io/tensorflow/tensorflow: TensorFlow CPU binary image.
• b.gcr.io/tensorflow/tensorflow-full: CPU Binary image plus source code.

With Docker the installation is as follows:

• Install Docker on your machine.
• Launch a Docker container with the TensorFlow image. The image gets downloaded automatically on first launch.

See installing Docker for instructions on installing Docker on your machine.

Also create a Docker group to allow launching containers without sudo.

After Docker is installed, launch a Docker container with the TensorFlow binary image as follows.

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

Within the Docker container, you can now test your installation.

You can alternatively launch the TensorFlow source image, for example if you want to experiment directly with the source.

$ docker run -it b.gcr.io/tensorflow/tensorflow-full

Test the TensorFlow installation

(Optional, Linux) Enable GPU Support

If you installed the GPU version of TensorFlow, you must also install the Cuda Toolkit 7.0 and CUDNN 6.5 V2. Please see Cuda installation.

You also need to set the LD_LIBRARY_PATH and CUDA_HOME environment variables. Consider adding the commands below to your ~/.bash_profile. These assume your CUDA installation is in /usr/local/cuda:

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda

Run TensorFlow from the Command Line

See common problems if some error happens.

Open a terminal and type the following:

$ python
...
>>> 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
>>>

Run a TensorFlow demo model

All TensorFlow packages, including the demo models, are installed in the Python library. The exact location of the Python library depends on your system, but is usually one of:

/usr/local/lib/python2.7/dist-packages/tensorflow
/usr/local/lib/python2.7/site-packages/tensorflow

You can find out the directory with the following command:

$ python -c 'import site; print "\n".join(site.getsitepackages())'

The simple demo model for classifying handwritten digits from the MNIST dataset is in the sub-directory models/image/mnist/convolutional.py. You can run it from the command line as follows:

# Using 'python -m' to find the program in the python search path:
$ python -m tensorflow.models.image.mnist.convolutional
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
...etc...

# You can alternatively pass the path to the model program file to the python interpreter.
$ python /usr/local/lib/python2.7/dist-packages/tensorflow/models/image/mnist/convolutional.py
...

Installing from sources

Clone the TensorFlow repository

$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow

--recurse-submodules is required to fetch the protobuf library that TensorFlow depends on.

Installation for Linux

Install Bazel

Follow instructions here to install the dependencies for Bazel. Then download bazel version 0.1.1 using the installer for your system and run the installer as mentioned there:

$ chmod +x PATH_TO_INSTALL.SH
$ ./PATH_TO_INSTALL.SH --user

Remember to replace PATH_TO_INSTALL.SH to point to the location where you downloaded the installer.

Finally, follow the instructions in that script to place bazel into your binary path.

Install other dependencies

$ sudo apt-get install python-numpy swig python-dev

Optional: Install CUDA (GPUs on Linux)

In order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and CUDNN 6.5 V2 from NVIDIA need to be installed.

TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.5. Supported cards include but are not limited to:

• NVidia Titan
• NVidia Titan X
• NVidia K20
• NVidia K40

Download and install Cuda Toolkit 7.0

https://developer.nvidia.com/cuda-toolkit-70

Install the toolkit into e.g. /usr/local/cuda

Download and install CUDNN Toolkit 6.5

https://developer.nvidia.com/rdp/cudnn-archive

Uncompress and copy the cudnn files into the toolkit directory. Assuming the toolkit is installed in /usr/local/cuda:

tar xvzf cudnn-6.5-linux-x64-v2.tgz
sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include
sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64

Configure TensorFlow's canonical view of Cuda libraries

From the root of your source tree, run:

$ ./configure
Do you wish to build TensorFlow with GPU support? [y/n] y
GPU support will be enabled for TensorFlow

Please specify the location where CUDA 7.0 toolkit is installed. Refer to
README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda

Please specify the location where CUDNN 6.5 V2 library is installed. Refer to
README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda

Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Configuration finished

This creates a canonical set of symbolic links to the Cuda libraries on your system. Every time you change the Cuda library paths you need to run this step again before you invoke the bazel build command.

Build your target with GPU support

From the root of your source tree, run:

$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer

$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu
# Lots of output. This tutorial iteratively calculates the major eigenvalue of
# a 2x2 matrix, on GPU. The last few lines look like this.
000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]

Note that "--config=cuda" is needed to enable the GPU support.

Enabling Cuda 3.0

TensorFlow officially supports Cuda devices with 3.5 and 5.2 compute capabilities. In order to enable earlier Cuda devices such as Grid K520, you need to target Cuda 3.0. This can be done through TensorFlow unofficial settings with "configure".

$ TF_UNOFFICIAL_SETTING=1 ./configure

# Same as the official settings above

WARNING: You are configuring unofficial settings in TensorFlow. Because some
external libraries are not backward compatible, these settings are largely
untested and unsupported.

Please specify a list of comma-separated Cuda compute capabilities you want to
build with. You can find the compute capability of your device at:
https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases
your build time and binary size. [Default is: "3.5,5.2"]: 3.0

Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Configuration finished

Known issues

• Although it is possible to build both Cuda and non-Cuda configs under the same source tree, we recommend to run "bazel clean" when switching between these two configs in the same source tree.

• You have to run configure before running bazel build. Otherwise, the build will fail with a clear error message. In the future, we might consider making this more conveninent by including the configure step in our build process, given necessary bazel new feature support.

Installation for Mac OS X

Mac needs the same set of dependencies as Linux, however installing those dependencies is different. Here is a set of useful links to help with installing the dependencies on Mac OS X :

Bazel

Look for installation instructions for Mac OS X on this page.

SWIG

Mac OS X installation.

Notes : You need to install PCRE and NOT PCRE2.

Numpy

Follow installation instructions here.

Create the pip package and install

$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package

# To build with GPU support:
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

# The name of the .whl file will depend on your platform.
$ pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

Train your first TensorFlow neural net model

Starting from the root of your source tree, run:

$ cd tensorflow/models/image/mnist
$ python convolutional.py
Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
Initialized!
Epoch 0.00
Minibatch loss: 12.054, learning rate: 0.010000
Minibatch error: 90.6%
Validation error: 84.6%
Epoch 0.12
Minibatch loss: 3.285, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 7.0%
...
...

Common Problems

GPU-related issues

If you encounter the following when trying to run a TensorFlow program:

ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory

Make sure you followed the the GPU installation instructions.

Pip installation issues

Can't find setup.py

If, during pip install, you encounter an error like:

...
IOError: [Errno 2] No such file or directory: '/tmp/pip-o6Tpui-build/setup.py'

Solution: upgrade your version of pip:

pip install --upgrade pip

This may require sudo, depending on how pip is installed.

SSLError: SSL_VERIFY_FAILED

If, during pip install from a URL, you encounter an error like:

...
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed

Solution: Download the wheel manually via curl or wget, and pip install locally.

On Linux

If you encounter:

...
 "__add__", "__radd__",
             ^
SyntaxError: invalid syntax

Solution: make sure you are using Python 2.7.

Mac OS X: ImportError: No module named copyreg

On Mac OS X, you may encounter the following when importing tensorflow.

>>> import tensorflow as tf
...
ImportError: No module named copyreg

Solution: TensorFlow depends on protobuf, which requires the Python package six-1.10.0. Apple's default Python installation only provides six-1.4.1.

You can resolve the issue in one of the following ways:

• Upgrade the Python installation with the current version six:

$ sudo easy_install -U six

• Install TensorFlow with a separate Python library:

  • Using Virtualenv.
  • Using Docker.

• Install a separate copy of Python via Homebrew or MacPorts and re-install TensorFlow in that copy of Python.

Mac OS X: TypeError: __init__() got an unexpected keyword argument 'syntax'

On Mac OS X, you may encounter the following when importing tensorflow.

>>> import tensorflow as tf
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py", line 4, in <module>
    from tensorflow.python import *
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 13, in <module>
    from tensorflow.core.framework.graph_pb2 import *
...
  File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py", line 22, in <module>
    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'

This is due to a conflict between protobuf versions (we require protobuf 3.0.0). The best current solution is to make sure older versions of protobuf are not installed, such as:

$ pip install --upgrade protobuf