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Setup Linux Python

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Required Packages

OpenMPI

CNTK requires OpenMPI 1.10.x to be installed on your system. On Ubuntu 16.04 install it like this:

sudo apt-get install openmpi-bin

Make sure that its libraries can be found, e.g., by setting up LD_LIBRARY_PATH.

Installing CNTK for Python on Linux

This page will walk you through the process of installing the Microsoft Cognitive Toolkit (CNTK) to use from Python in Linux. Note that Ubuntu 14.04 is supported for CNTK 2.3.1 and lower. All releases 2.4+ officially only support Ubuntu 16.04.

If you are looking for any other kind of support to setup a CNTK build environment or installing CNTK on your system, you should go here instead.

We offer three ways to install CNTK for Python:

  1. PyPI installation
  2. Wheel (.whl) files for each release
  3. Nightly builds

1. Install from PyPI

As of the CNTK 2.5 release, users can now install CNTK via PyPI. Note that only Ubuntu 16.04 is officially supported.

To install the CPU-only version of CNTK:

C:\> pip install cntk

To install the GPU version of CNTK:

C:\> pip install cntk-gpu

Upgrade an existing CNTK installation

If you already have a previous version (2.5+) of CNTK installed, you can install a new version of CNTK over your existing installation.

To upgrade the CPU-only version of CNTK:

C:\> pip install --upgrade --no-deps cntk

To upgrade the GPU version of CNTK:

C:\> pip install --upgrade --no-deps cntk-gpu

Note: we advise that you do not have both cntk and cntk-gpu packages installed simultaneously.

2. Install from Wheel Files

Depending on the Python and CNTK version (CPU or GPU) we supply different wheel (.whl) files to install CNTK. Please select the correct installation from the list below, and substitute the name and/or link during the installation. For CNTK 2.5+, we recommend you simply install through PyPI instead.

Python Flavor URL
2.7 CPU-Only https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp27-cp27mu-linux_x86_64.whl
GPU https://cntk.ai/PythonWheel/GPU/cntk_gpu-2.7.post1-cp27-cp27mu-linux_x86_64.whl
3.5 CPU-Only https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp35-cp35m-linux_x86_64.whl
GPU https://cntk.ai/PythonWheel/GPU/cntk_gpu-2.7.post1-cp35-cp35m-linux_x86_64.whl
3.6 CPU-Only https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp36-cp36m-linux_x86_64.whl
GPU https://cntk.ai/PythonWheel/GPU/cntk_gpu-2.7.post1-cp36-cp36m-linux_x86_64.whl

Anaconda3

We have been testing CNTK with Anaconda3 4.1.1 and Python versions 2.7, 3.5, as well as Anaconda3 4.3.1 with Python version 3.6. If you do not have an Anaconda3 Python installation, install Anaconda3 4.1.1 Python for Linux (64-bit).

Below we assume that the prerequisites above are satisfied. If you plan on using a GPU enabled version of CNTK, you will need a CUDA 9 compliant graphics card and up-to-date graphics drivers installed on your system. Also, we assume Anaconda is installed and that it is listed before any other Python installations in your PATH.

pip install without an environment

This is the easiest option and the only reason to avoid it is if you require specific versions of certain packages. If you have other packages that require an old version of numpy skip to this section.

First time CNTK installation

If this is the first time you install CNTK then run

$ pip install <url>

where <url> is the corresponding wheel file URL in the table on the top of this page. E.g., if you have Python 3.5 run

$ pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.6-cp35-cp35m-linux_x86_64.whl

Continue with a quick installation test

Upgrade an existing CNTK installation

If you already have a previous version of CNTK installed, you can install a new version of CNTK over your existing installation. It is important to supply the --upgrade and --no-deps options.

$ pip install --upgrade --no-deps <url>

where <url> is the corresponding wheel file URL in the table on the top of this page. Once you have completed this upgrade step, you can start working with CNTK in Python or install samples and tutorials.

Quick installation test

A quick test that the installation succeeded can be done by querying the CNTK version:

$ python -c "import cntk; print(cntk.__version__)"

You now have successfully installed CNTK, and you can start developing / training / evaluating with CNTK in Python!

Continue with installing samples and tutorials

pip install in an environment

Below we will create a new Python 3.5 environment inside Anaconda called cntk-py35 and will pip-install CNTK into this environment. If you want a different CNTK version, Python version, or environment name, please adjust the parameters accordingly.

Open a command shell, create the environment, make it active, and pip-install CNTK:

$ conda create --name cntk-py35 python=3.5 numpy scipy h5py jupyter
$ activate cntk-py35
$ pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.6-cp35-cp35m-linux_x86_64.whl

A quick test that the installation succeeded can be done by querying the CNTK version:

$ python -c "import cntk; print(cntk.__version__)"

You now have successfully installed CNTK, you can start developing / training / evaluating with CNTK in Python!

Continue with a installing samples and tutorials

Anaconda2

If you require a Python 2.7 root environment, we recommend you install Anaconda2 4.3.0 Python for Linux (64-bit). Below we assume that the prerequisites above are satisfied. If you plan on using a GPU enabled version of CNTK, you will need a CUDA 9 compliant graphics card and up-to-date graphics drivers installed on your system. Also, we assume Anaconda2 is installed and that it is listed before any other Python installations in your PATH.

Anaconda2: pip install

The installation steps for CNTK on Anaconda2 are identical to

Just make sure you select Python 2.7 compatible wheel files from the URL table at the top of this page.

3. Install from Nightly Builds

If you prefer to install or upgrade CNTK from the latest nightly build rather than an official release, we offer CNTK nightly packages. You can access the CNTK packages from the latest nightly builds here.

If you are using a nightly build, you will need to separately install some third-party packages and them to your PATH environment variable (in addition to the OpenMPI requirement listed in here). Follow the below section for instructions. E.g. if you are installing the GPU version of CNTK, you will also need to install the GPU-specific packages listed in the following section.

Environment Variables and Required Packages

OPTIONAL: GPU-Specific Packages

If you intend to use CNTK with GPU support, follow this page to install and configure the environment accordingly.

After installing the aforementioned GPU packages, add them into your PATH environment variable, e.g.

MKL

The default CNTK math library is the Intel Math Kernel Library (Intel MKL). Follow this page to install it

  • Export its path to environment variable LD_LIBRARY_PATH, e.g.:
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
OPTIONAL: OpenCV

CNTK 2.2 requires Open Source Computer Vision (OpenCV) to be installed but it is optional for CNTK 2.3+. Follow this page to install it.

You will need to install OpenCV for CNTK 2.3+ if you want to use the following components:

  • CNTK Image Reader
  • CNTK Image Writer - required to use TensorBoard's Image feature.

Export the environment variable LD_LIBRARY_PATH pointing to the OpenCV build folder, e.g.

export LD_LIBRARY_PATH=/usr/local/opencv-3.1.0/lib:$LD_LIBRARY_PATH

Installing Samples and Tutorials

We provide various samples and tutorials with CNTK. After you installed CNTK you can install the samples/tutorials and Jupyter notebooks. If you installed CNTK into a Python environment, make sure you activated the environment before running this command:

$ python -m cntk.sample_installer

This will download the samples/tutorials, install the required Python packages, and copy the samples into a directory named CNTK-Samples-VERSION (VERSION is replaced with the actual CNTK version) beneath your current working directory.

You can now follow the standard description to test your installation from Python and run the tutorials or Jupyter notebooks.