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From TensorFlow Official docs
Download and Setup
You can install TensorFlow using our provided binary packages or from source.
Binary Installation
The TensorFlow Python API currently requires Python 2.7: we are working on adding support for Python 3.
The simplest way to install TensorFlow is using pip for both Linux and Mac.
If you encounter installation errors, see common problems for some solutions. To simplify installation, please consider using our virtualenv-based instructions here.
Ubuntu/Linux 64-bit
# For CPU-only version
$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
# For GPU-enabled version (only install this version if you have the CUDA sdk installed)
$ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
Mac OS X
On OS X, we recommend installing homebrew and brew install python before proceeding, or installing TensorFlow within virtualenv.
# Only CPU-version is available at the moment.
$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
Docker-based installation
We also support running TensorFlow via Docker, which lets you avoid worrying about setting up dependencies.
First, install Docker. Once Docker is up and running, you can start a container with one command:
$ docker run -it b.gcr.io/tensorflow/tensorflow
This will start a container with TensorFlow and all its dependencies already installed.
Additional images
The default Docker image above contains just a minimal set of libraries for
getting up and running with TensorFlow. We also have the following container,
which you can use in the docker run command above:
b.gcr.io/tensorflow/tensorflow-full: Contains a complete TensorFlow source installation, including all utilities needed to build and run TensorFlow. This makes it easy to experiment directly with the source, without needing to install any of the dependencies described above.
VirtualEnv-based installation
We recommend using virtualenv to create an isolated container and install TensorFlow in that container -- it is optional but makes verifying installation issues easier.
First, install all required tools:
# On Linux:
$ sudo apt-get install python-pip python-dev python-virtualenv
# On Mac:
$ sudo easy_install pip # If pip is not already installed
$ sudo pip install --upgrade virtualenv
Next, set up a new virtualenv environment. To set it up in the
directory ~/tensorflow, run:
$ virtualenv --system-site-packages ~/tensorflow
$ cd ~/tensorflow
Then activate the virtualenv:
$ source bin/activate # If using bash
$ source bin/activate.csh # If using csh
(tensorflow)$ # Your prompt should change
Inside the virtualenv, install TensorFlow:
# For CPU-only linux x86_64 version
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
# For GPU-enabled linux x86_64 version
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
# For Mac CPU-only version
(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
Make sure you have downloaded the source code for TensorFlow, and then you can then run an example TensorFlow program like:
(tensorflow)$ cd tensorflow/models/image/mnist
(tensorflow)$ python convolutional.py
# When you are done using TensorFlow:
(tensorflow)$ deactivate # Deactivate the virtualenv
$ # Your prompt should change back
Try your first TensorFlow program
(Optional) Enable GPU Support
If you installed the GPU-enabled TensorFlow pip binary, you must have the correct versions of the CUDA SDK and CUDNN installed on your system. Please see the CUDA installation instructions.
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
Open a python terminal:
$ 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
>>>
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 their 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
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.
On MacOSX
If you encounter:
import six.moves.copyreg as copyreg
ImportError: No module named copyreg
Solution: TensorFlow depends on protobuf, which requires six-1.10.0. Apple's
default python environment has six-1.4.1 and may be difficult to upgrade.
There are several ways to fix this:
-
Upgrade the system-wide copy of
six:sudo easy_install -U six -
Install a separate copy of python via homebrew:
brew install python -
Build or use TensorFlow within
virtualenv.
If you encounter:
>>> 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:
brew reinstall --devel protobuf