Installing TensorFlow on Ubuntu 1.3

Installing TensorFlow on Ubuntu

This guide explains how to install TensorFlow on Ubuntu. These instructionsmight also work on other Linux variants, but we have only tested (and weonly support) these instructions on Ubuntu 14.04 or higher.

Determine which TensorFlow to install

You must choose one of the following types of TensorFlow to install:

TensorFlow with CPU support only. If your system does not have a NVIDIA® GPU, you must install this version. Note that this version of TensorFlow is typically much easier to install (typically, in 5 or 10 minutes), so even if you have an
NVIDIA GPU, we recommend installing this version first.
TensorFlow with GPU support. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA® GPU meeting the prerequisites shown below and you need to run performance-critical applications,
you should ultimately install this version.

NVIDIA requirements to run TensorFlow with GPU support

If you are installing TensorFlow with GPU support using one of themechanisms described in this guide, then the following NVIDIA softwaremust 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 v6. 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.

The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support. To install this library, issue the following command:

$ sudo apt-get install libcupti-dev

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

Install TensorFlow from sources as documented in
Installing TensorFlow from Sources.
Install or upgrade to at least the following NVIDIA versions:
CUDA toolkit 8.0 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. Thesupported choices are as follows:

virtualenv
"native" pip
Docker
Anaconda
installing from sources, which is documented in
a separate guide.
We recommend the virtualenv installation.Virtualenvis a virtual Python environment isolated from other Python development,incapable of interfering with or being affected
by other Python programson the same machine. During the virtualenv installation process,you will install not only TensorFlow but also all the packages thatTensorFlow requires. (This is actually pretty easy.)To start working with TensorFlow, you simply need
to "activate" thevirtual environment. All in all, virtualenv provides a safe andreliable mechanism for installing and running TensorFlow.

Native pip installs TensorFlow directly on your system without goingthrough any container system.
We recommend the native pip install forsystem administrators aiming to make TensorFlow available to everyone on amulti-user system. Since a native pip installation is not walled-off ina separate container, the pip installation might interfere
with otherPython-based installations on your system. However, if you understand pipand your Python environment, a native pip installation often entails onlya single command.

Docker completely isolates the TensorFlow installationfrom pre-existing packages on your machine. The Docker container containsTensorFlow and all its dependencies. Note that the Docker image can be quitelarge (hundreds of MBs). You might choose the Docker
installation if you areincorporating TensorFlow into a larger application architecture that alreadyuses 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:

Install pip and virtualenv by issuing one of the following commands:

$ sudo apt-get install python-pip python-dev python-virtualenv # for Python 2.7
$ sudo apt-get install python3-pip python3-dev python-virtualenv # for Python 3.n

Create a virtualenv environment by issuing one of the following commands:

$ virtualenv --system-site-packages targetDirectory # for Python 2.7
$ virtualenv --system-site-packages -p python3 targetDirectory # for Python 3.n

where

targetDirectory

specifies the top of the virtualenv tree. Our instructions assume that

targetDirectory

is

~/tensorflow

, but you may choose any directory.

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

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

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

(tensorflow)$

Ensure pip ≥8.1 is installed:

(tensorflow)$easy_install -U pip

Issue one of the following commands to install TensorFlow in the active virtualenv environment:

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

If the preceding command succeeds, skip Step 5. If the preceding command fails, perform Step 5.

(Optional) If Step 4 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:

(tensorflow)$pip install --upgrade tfBinaryURL   # Python 2.7
(tensorflow)$pip3 install --upgrade tfBinaryURL  # Python 3.n 

where

tfBinaryURL

identifies the URL of the TensorFlow Python package. The appropriate value of

tfBinaryURL

depends on the operating system, Python version, and GPU support. Find the appropriate value for

tfBinaryURL

for your system
here. For example, if you are installing TensorFlow for Linux, Python 3.4, and CPU-only support, issue the following command to install TensorFlow in the active virtualenv environment:

(tensorflow)$pip3 install --upgrade \ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl[/code] 
If you encounter installation problems, seeCommon Installation Problems.

Next Steps
After installing TensorFlow,validate the installation.

Note that you must activate the virtualenv environment each time youuse TensorFlow. If the virtualenv environment is not currently active,invoke one of the following commands:

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

When the virtualenv environment is active, you may runTensorFlow programs from this shell. Your prompt will becomethe following to indicate that your tensorflow environment is active:

(tensorflow)$

When you are done using TensorFlow, you may deactivate theenvironment by invoking the
deactivate
 function as follows:

(tensorflow)$deactivate 

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

Uninstalling TensorFlow
To uninstall TensorFlow, simply remove the tree you created.For example:

$ rm -r targetDirectory


Installing with native pip
You may install TensorFlow through pip, choosing between a simpleinstallation procedure or a more complex one.

Note: TheREQUIRED_PACKAGES section of setup.pylists the TensorFlow packages that pip will install or upgrade.

Prerequisite: Python and Pip
Python is automatically installed on Ubuntu. Take a moment to confirm(by issuing a
python -V
 command) that one of the following Pythonversions is already installed on your system:

Python 2.7
Python 3.3+
The pip or pip3 package manager is usually installed on Ubuntu. Take amoment to confirm (by issuing a
pip -V
 or 
pip3 -V
 command)that pip or pip3 is installed. We strongly recommend version 8.1 or higherof pip or pip3. If Version 8.1 or later is not installed, issue thefollowing command, which will either install or upgrade to the latestpip
version:

$ sudo apt-get install python-pip python-dev   # for Python 2.7
$ sudo apt-get install python3-pip python3-dev # for Python 3.n

Install TensorFlow
Assuming the prerequisite software is installed on your Linux host,take the following steps:

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.

(Optional.) If Step 1 failed, install the latest version of TensorFlow by issuing a command of the following format:

$ sudo pip  install --upgrade tfBinaryURL   # Python 2.7
$ sudo pip3 install --upgrade tfBinaryURL   # Python 3.n

where 
tfBinaryURL
 identifies the URL of the TensorFlow Python package. The appropriate value of
tfBinaryURL
 depends on the operating system, Python version, and GPU support. Find the appropriate value for
tfBinaryURL

here. For example, to install TensorFlow for Linux, Python 3.4, and CPU-only support, issue the following command:

$ sudo pip3 install --upgrade \ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl

If this step fails, see
Common Installation Problems.

Next Steps
After installing TensorFlow,
validate your installation.

Uninstalling TensorFlow
To uninstall TensorFlow, issue one of following commands:

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


Installing with Docker
Take the following steps to install TensorFlow through Docker:

Install Docker on your machine as described in the
Docker documentation.
Optionally, create a Linux group called 
docker
 to allow launching containers without sudo as described in the

Docker documentation. (If you don't do this step, you'll have to use sudo each time you invoke Docker.)
To install a version of TensorFlow that supports GPUs, you must first install
nvidia-docker, which is stored in github.
Launch a Docker container that contains one of the
TensorFlow binary images.
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, withoutGPU support), enter a command of the following format:

$ docker run -it -p hostPort:containerPort TensorFlowCPUImage

where:

-p hostPort:containerPort 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
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.

TensorFlowCPUImage is required. It identifies the Docker container. Specify one of the following values:

gcr.io/tensorflow/tensorflow, which is the TensorFlow CPU binary image.
gcr.io/tensorflow/tensorflow:latest-devel, which is the latest TensorFlow CPU Binary image plus source code.
gcr.io/tensorflow/tensorflow:version, which is the specified version (for example, 1.1.0rc1) of TensorFlow CPU binary image.
gcr.io/tensorflow/tensorflow:version-devel, which is the specified version (for example, 1.1.0rc1) of the TensorFlow GPU binary image plus source code.
gcr.io is the Google Container Registry. Note that someTensorFlow images are also available atdockerhub.

For example, the following command launches the latest TensorFlow CPU binary imagein 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 the latest TensorFlow CPU binary image in aDocker container. However, in this Docker container, you can run TensorFlowprograms 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.

GPU support
Prior to installing TensorFlow with GPU support, ensure that your system meets allNVIDIA software requirements. To launch a Docker containerwith NVidia GPU
support, enter a command of the following format:

$ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage

where:

-p hostPort:containerPort 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
hostPort and 
containerPort
 to 
8888
.
TensorFlowGPUImage specifies the Docker container. You must specify one of the following values:
gcr.io/tensorflow/tensorflow:latest-gpu, which is the latest TensorFlow GPU binary image.
gcr.io/tensorflow/tensorflow:latest-devel-gpu, which is the latest TensorFlow GPU Binary image plus source code.
gcr.io/tensorflow/tensorflow:version-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image.
gcr.io/tensorflow/tensorflow:version-devel-gpu, 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, thefollowing command launches the latest TensorFlow GPU binary image in aDocker container from which you can run TensorFlow programs in a shell:

$ nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash

The following command also launches the latest TensorFlow GPU binary imagein a Docker container. In this Docker container, you can run TensorFlowprograms in a Jupyter notebook:

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

The following command installs an older TensorFlow version (0.12.1):

$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:0.12.1-gpu

Docker will download the TensorFlow binary image the first time you launch it.For more details see theTensorFlow docker readme.

Next Steps
You should nowvalidate your i
eb8a
nstallation.

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

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

Create a conda environment named tensorflow to run a version of Python by invoking the following command:

$ conda create -n tensorflow


Activate the conda environment by issuing the following command:

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


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

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

where 
tfBinaryURL
 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/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl[/code] 
Validate your installation
To validate your TensorFlow installation, do the following:

Ensure that your environment is prepared to run TensorFlow programs.
Run a short TensorFlow program.
Prepare your environment
If you installed on native pip, virtualenv, or Anaconda, thendo the following:

Start a terminal.
If you installed with virtualenv or Anaconda, activate your container.
If you installed TensorFlow source code, navigate to any directory except one containing TensorFlow source code.
If you installed through Docker, start a Docker containerfrom which you can run 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:

# 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 writingTensorFlow 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
Commoninstallation problems.

Common installation problems
We are relying on Stack Overflow to document TensorFlow installation problemsand their remedies. The following table contains links to Stack Overflowanswers for some common installation problems.If you encounter an error message or otherinstallation problem
not listed in the following table, search for iton Stack Overflow. If Stack Overflow doesn't show the error message,ask a new question about it on Stack Overflow and specifythe
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

36371137 and

here
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.

35252888
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.

33623453
IOError: [Errno 2] No such file or directory:
'/tmp/pip-o6Tpui-build/setup.py'

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'

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'

36933958
...
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'

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:

https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp27-none-linux_x86_64.whl

GPU support:

https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp27-none-linux_x86_64.whl

Note that GPU support requires the NVIDIA hardware and software described inNVIDIA requirements to run TensorFlow with GPU support.

Python 3.4
CPU only:

https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl

GPU support:

https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp34-cp34m-linux_x86_64.whl

Note that GPU support requires the NVIDIA hardware and software described inNVIDIA requirements to run TensorFlow with GPU support.

Python 3.5
CPU only:

https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl

GPU support:

https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp35-cp35m-linux_x86_64.whl

Note that GPU support requires the NVIDIA hardware and software described inNVIDIA requirements to run TensorFlow with GPU support.

Python 3.6
CPU only:

https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl

GPU support:

https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl

Note that GPU support requires the NVIDIA hardware and software described inNVIDIA requirements to run TensorFlow with GPU support.

Protobuf pip package 3.1
You can skip this section unless you are seeing problems relatedto the protobuf pip package.

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

The TensorFlow pip package depends on protobuf pip package version 3.1. Theprotobuf pip package downloaded from PyPI (when invokingpip install protobuf) is a Python-only library containingPython implementations of proto serialization/deserialization
that can run10x-50x slower than the C++ implementation. Protobuf also supports abinary extension for the Python package that contains fastC++ based proto parsing. This extension is not available in thestandard Python-only pip package. We have
created a custom binarypip package for protobuf that contains the binary extension. To installthe 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[/code] for Python 3.5:
$ pip3 install --upgrade \ https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.1.0-cp35-none-linux_x86_64.whl[/code] Installing this protobuf package will overwrite the existing protobuf package.Note that the binary pip package already has support for protobufslarger 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.