Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:
wget -c -v -nc https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.2.88-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1604_9.2.88-1_amd64.deb sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub sudo apt-get update sudo apt-get install cuda
Keep checking the NVIDIA CUDA webpage for new releases as applicable. This article is accurate as at the time of writing.
Ensure that you have the latest driver:
sudo add-apt-repository ppa:graphics-drivers/ppa sudo apt-get update && sudo apt-get -y upgrade
On Ubuntu 18.04LTS, this should be enough for the device driver:
sudo apt-get install nvidia-kernel-source-396 nvidia-driver-396
Failure to do this will result in a broken driver installation.
When done, create a library configuration file for cupti:
With the content:
Confirm that the library configuration file for CUDA libraries also exists with the correct settings:
The content should be:
When done, load the new configuration:
sudo ldconfig -vvvv
Useful environment variables for CUDA:
/etc/environment file and append the following:
Now, append the PATH variable with the following:
When done, remember to source the file:
You can also install CUDA manually. However, take care not to install its' bundled driver.
Step 2. Install NVIDIA cuDNN:
Once the CUDA Toolkit is installed, download the latest cuDNNN Library for Linux, based on the CUDA version you're using. In this case, we're on CUDA 9.1, so we will refer to the version name below (note that you will need to register for the Accelerated Computing Developer Program).
Once downloaded, uncompress the files and copy them into the CUDA Toolkit directory (assumed here to be in
/usr/local/cuda/ for Ubuntu 16.04LTS):
$ sudo tar -xvf cudnn-9.1-* -C /usr/local
Step 3. Install and upgrade PIP:
TensorFlow itself can be installed using the pip package manager. First, make sure that your system has pip installed and updated:
$ sudo apt-get install python-pip python-dev $ pip install --upgrade pip
Step 4. Install Bazel:
To build TensorFlow from source, the Bazel build system (and the latest available openjdk) must first be installed as follows.
$ sudo apt-get install software-properties-common swig $ sudo add-apt-repository ppa:webupd8team/java $ sudo apt-get update $ sudo apt-get install oracle-java8-installer $ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list $ curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add - $ sudo apt-get update $ sudo apt-get install bazel
Step 5. Install TensorFlow
To obtain the best performance with TensorFlow we recommend building it from source.
First, clone the TensorFlow source code repository:
$ git clone https://github.com/tensorflow/tensorflow $ cd tensorflow
The last step is no longer needed:
$ git reset --hard a23f5d7
Then run the configure script as follows:
Please specify the location of python. [Default is /usr/bin/python]: [enter] Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] n No Google Cloud Platform support will be enabled for TensorFlow Do you wish to build TensorFlow with GPU support? [y/N] y GPU support will be enabled for TensorFlow Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]: [enter] Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]: 8.0 Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: [enter] Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5 Please specify the location where cuDNN 5 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: [enter] 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"]: 5.2,6.1 [see https://developer.nvidia.com/cuda-gpus] Setting up Cuda include Setting up Cuda lib64 Setting up Cuda bin Setting up Cuda nvvm Setting up CUPTI include Setting up CUPTI lib64 Configuration finished
Then call bazel to build the TensorFlow pip package:
bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both --copt=-msse4.2 --config=cuda //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
This will build the package with optimizations for FMA, AVX and SSE.
A stock build would be as such:
bazel build //tensorflow/tools/pip_package:build_pip_package bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
You can use the stock build as shown above if you had passed the configuration flags (for optimization) directly to the configure script above. Use this string:
--copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both --copt=-msse4.2
Which will replace
--march=native (the default).
If you're on Skylake to Coffee lake, this is what you need.
And finally install the TensorFlow pip package
For Python 2.7:
$ sudo pip install --upgrade /tmp/tensorflow_pkg/tensorflow-*.whl
$ sudo pip install --upgrade /tmp/tensorflow_pkg/tensorflow-*.whl
Step 5. Upgrade protobuf:
Upgrade to the latest version of the protobuf package:
For Python 2.7:
$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0b2.post2-cp27-none-linux_x86_64.whl
For Python 3.4:
$ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0b2.post2-cp34-none-linux_x86_64.whl
Step 6. Test your installation:
To test the installation, open an interactive Python shell and import the TensorFlow module:
$ cd $ python >>> import tensorflow as tf tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
With the TensorFlow module imported, the next step to test the installation is to create a TensorFlow Session, which will initialize the available computing devices and provide a means of executing computation graphs:
>>> sess = tf.Session()
This command will print out some information on the detected hardware configuration. For example, the output on a system containing a Tesla M40 GPU is:
>>> sess = tf.Session() I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: name: Tesla M40 major: 5 minor: 2 memoryClockRate (GHz) 1.112 pciBusID 0000:04:00.0 Total memory: 11.25GiB Free memory: 11.09GiB
To manually control which devices are visible to TensorFlow, set the
CUDA_VISIBLE_DEVICES environment variable when launching Python. For example, to force the use of only GPU 0:
$ CUDA_VISIBLE_DEVICES=0 python
You should now be able to run a Hello World application:
>>> hello_world = tf.constant("Hello, TensorFlow!") >>> print sess.run(hello_world) Hello, TensorFlow! >>> print sess.run(tf.constant(123)*tf.constant(456)) 56088
To achieve similar results without building the packages, you can deploy nvidia-docker and install tensorflow from NVIDIA's NGC registry.
Use this to deploy nvidia-docker on Ubuntu: https://gist.github.com/Brainiarc7/a8ab5f89494d053003454efc3be2d2ef
Use the NGC to deploy the preconfigured containers. Optimized builds for Tensorflow, Caffe, Torch, etc are also available: https://www.nvidia.com/en-us/gpu-cloud/deep-learning-containers/
Also see the NGC panel: https://ngc.nvidia.com/registry