Setup CNTK on Linux
CNTK Production Build and Test configuration
CNTK may be successfully run in many Linux configurations, but in case you want to avoid possible compatibility issues you may get yourself familiar with CNTK Production Build and Test configuration where we list all dependency component and component versions that we use.
CNTK as a Docker container
Before moving any further you may consider deploying CNTK as a Docker container. Read the corresponding section.
Current limitations and precautions
Please, read carefully this section before you proceed with your system configuration. The information below may save you a lot of time otherwise spent on build errors debugging.
This page assumes that you are trying to build CNTK's master branch.
Expected component locations in configure and Makefile scripts
Makefile scripts support only limited set of installation paths for all dependency components listed in this section. We know, that this is a limitation and will fix it soon (also if you feel like improving these scripts yourselves and submit your proposed changes your help is welcome and much appreciated).
configure looks for all dependency components among the paths listed in
default_path_list variable defined within the script.
If you want to modify
default_path_list variable in
configure to add a custom path for a certain dependency component be sure to check the correspondent section of
Makefile. Otherwise you may get build errors due to inability of finding INCLUDE files, libraries, etc.
Installation methods and paths of dependency components
Below we list all dependency components required to build CNTK and explain how to install them. We understand that there are many other ways to get the same components. However, if you prefer an alternative way of installation, please ensure that you get the same thing, because quite often alternative installation sources, namely network distribution packages (like Debian, RPM, etc.) contain older versions of the software, miss some libraries, etc. In some sections below we specifically highlight these limitations, but please take it as a general precaution.
In most of the sections we suggest using
make -j command to invoke parallel build jobs and thus increasing the speed of the build process. However please be aware that on some systems and especially on virtual machines using
make -j may result in "Out of memory" errors. If you face this, just use "plain"
make or limit the number of jobs that run simultaneously (two simultaneous jobs usually work for the most of the systems - use the command
make -j 2).
Simultaneous installation of different versions of the same development packages
Be very careful in case you would like to have several installations of some of the development packages mentioned below on the same system. It may result in very hard to debug build errors as you can see in this post.
And now let's proceed to the setup.
If you would like to know what prerequisite configuration is used in the CNTK production environment, i.e. what we use internally for building and testing, see this section
You need a 64-bit Linux installation to use CNTK.
Ensure your installation has a C++ compiler. Many distributions do not include it by default. Refer to your platform documentation on how to check for and obtain a C++ compiler.
Example: for Ubuntu, run the following command:
dpkg --list | grep compiler
if in the output you do not see something like
g++-5 5.4.0-6ubuntu1~16.04.5 amd64 GNU C++ compiler
then a C++ compiler is not installed. If you have Ubuntu 1604, install gcc 5.4 with:
sudo apt-get install g++
Install Git on your system as described here.
We recommend installing from sources as described below because a lot of distribution packages contain older versions and miss the libraries required by CNTK. Current CNTK Open MPI version requirement is at least 1.10. Please, check whether you have older version installations on your system and if you do, either uninstall them or ensure (via, e.g. symbolic links) that CNTK build procedure is using the required version. Otherwise you may get hard to debug build errors as you can see in this post.
- Get the installation sources:
- Unpack, build and install Open MPI (to
/usr/local/mpiin this example):
tar -xzvf ./openmpi-1.10.3.tar.gz cd openmpi-1.10.3 ./configure --prefix=/usr/local/mpi make -j all sudo make install
- Add the following environment variable to your current session and your
.bashrcprofile (Prepending the new path, ensures this version is used as opposed to a default version available through the OS):
export PATH=/usr/local/mpi/bin:$PATH export LD_LIBRARY_PATH=/usr/local/mpi/lib:$LD_LIBRARY_PATH
We use Protocol Buffers for serialization. For installation please follow these steps:
- Install the required packages using
sudo apt-get install autoconf automake libtool curl make g++ unzip
- Download and unpack protobuf sources
wget https://github.com/google/protobuf/archive/v3.1.0.tar.gz tar -xzf v3.1.0.tar.gz
- Compile protobuf
cd protobuf-3.1.0 ./autogen.sh ./configure CFLAGS=-fPIC CXXFLAGS=-fPIC --disable-shared --prefix=/usr/local/protobuf-3.1.0 make -j $(nproc) sudo make install
zlib today is a part of many popular Linux distributions so with the high probability you have it preinstalled. However note, that libzip requires zlib version 1.1.2 or later and this usually is NOT preinstalled. zlib must be installed before building Boost below.
See your platform documentation on how to get the required zlib package or get it directly from zlib website.
Example: for Ubuntu use the following command:
sudo apt-get install zlib1g-dev
libzip is available in different network distribution packages, but we found that many of them contain old versions. Using these versions will likely result in build errors. So we strongly recommend to build libzip from sources as described below.
Note that the following procedure will install libzip to
/usr/local. This is exactly where CNTK build procedure expects it (see the beginning of this page for details). If you want to install libzip to a different path see instructions in
INSTALL file in the root of libzip distribution folder. However beware that in this case you have to manually edit
Makefile of CNTK to support this path.
Use the following commands:
wget http://nih.at/libzip/libzip-1.1.2.tar.gz tar -xzvf ./libzip-1.1.2.tar.gz cd libzip-1.1.2 ./configure make -j all sudo make install
Add the following environment variable to your current session and your
The Boost Library is a prerequisite for building the Microsoft Cognitive Toolkit. Follow these steps to install the Boost Library on your system:
sudo apt-get install libbz2-dev sudo apt-get install python-dev wget -q -O - https://sourceforge.net/projects/boost/files/boost/1.60.0/boost_1_60_0.tar.gz/download | tar -xzf - cd boost_1_60_0 ./bootstrap.sh --prefix=/usr/local/boost-1.60.0 sudo ./b2 -d0 -j"$(nproc)" install
GPU Specific Packages
If you intend to use CNTK with GPU support, follow this page to install and configure the environment accordingly.
If you want to take advantage of CNTK from Python, you will need to install SWIG.
SWIG is also a requirement to build the CNTK Evaluation libraries for Java.
To install it, run the script:
[CNTK clone root]/Tools/devInstall/Linux/install-swig.sh.
This creates the installed version in the folder
OPTIONAL. CNTK v2 Python support
This section describes how to build CNTK v2 with Python support.
Step 1: Build Python APIs
- Install the SWIG tool if you have not done so yet.
- Install Anaconda3 4.1.1 (64-bit)
- If you already have a CNTK Python environment (called
cntk-py27) you can update it with the latest required packages with the following commands:
# For cntk-py36: conda env update --file [CNTK clone root]/Scripts/install/linux/conda-linux-cntk-py36-environment.yml --name cntk-py36 # For cntk-py35: conda env update --file [CNTK clone root]/Scripts/install/linux/conda-linux-cntk-py35-environment.yml --name cntk-py35 # For cntk-py27: conda env update --file [CNTK clone root]/Scripts/install/linux/conda-linux-cntk-py27-environment.yml --name cntk-py27
- If you do not have a CNTK Python environment yet, you may choose between a Python 2.7, 3.5 or 3.6 based CNTK Python environment.
- Create your Python environment of choice in your existing Python 3.5 Anaconda or Miniconda installation using one of the following commands, depending on your desired Python version:
# For a Python 3.6 based version: conda env create --file [CNTK clone root]/Scripts/install/linux/conda-linux-cntk-py36-environment.yml # For a Python 3.5 based version: conda env create --file [CNTK clone root]/Scripts/install/linux/conda-linux-cntk-py35-environment.yml # For a Python 2.7 based version: conda env create --file [CNTK clone root]/Scripts/install/linux/conda-linux-cntk-py27-environment.yml
Note: Make sure that the Python environment updated or created above is activated for the remainder of the instructions.
For example, if you have Python 3.5 based environment called
cntk-py35 run this command:
source activate cntk-py35
Similarly, for a Python 3.6, or 2.7 based environment.
Step 2: Uninstall previous CNTK package
- If you previously installed any version of the CNTK pip-package on your machine, uninstall it by running:
pip uninstall cntk
Step 3: Build Python Package
- To configure a build with Python, include this two option when running
and one of the following (whatever applies to your environment):
--with-py36-path[=directory] --with-py35-path[=directory] --with-py27-path[=directory]
- Only Release builds are supported at this stage. For example, if you installed SWIG to
/usr/local/swig-3.0.10and your Python environment is located at
$HOME/anaconda3/envs/cntk-py35provide these additional parameters to
- Afterwards, run make as you normally would, which will build the CNTK Python module inside
bindings/python/cntkand also produce a package (.whl) in a subfolder python of your build output folder (e.g.,
cd [CNTK clone root] export PYTHONPATH=$PWD/bindings/python:$PYTHONPATH cd [CNTK clone root]/bindings/python export LD_LIBRARY_PATH=$PWD/cntk/libs:$LD_LIBRARY_PATH
In contrast to the setup shown for the Pip package installation, here we will load the CNTK module from the CNTK repository clone, not as an installed package in your Python environment. (Hence also the difference in setting up
Step 4: Verify setup
- Run the Python examples from inside the
[CNTK clone root]/Tutorialsor
[CNTK clone root]/Examplesdirectories, to verify your installation. For example, go to the folder
[CNTK clone root]/Tutorials/NumpyInteropand run
python FeedForwardNet.py. You should see the following output on the console:
Minibatch: 0, Train Loss: 0.7915553283691407, Train Evaluation Criterion: 0.48 Minibatch: 20, Train Loss: 0.6266774368286133, Train Evaluation Criterion: 0.48 Minibatch: 40, Train Loss: 1.0378565979003906, Train Evaluation Criterion: 0.64 Minibatch: 60, Train Loss: 0.6558118438720704, Train Evaluation Criterion: 0.56
The configure script provides
--with-jdk option to specify the JDK directory manually, if it cannot be found by default.
Getting CNTK Source code
Before proceeding further, please note, that if you plan on making modifications to the CNTK code you should read the information on Developing and Testing.
Use Git to clone the CNTK Repository and access the source code:
git clone https://github.com/Microsoft/cntk cd cntk git submodule update --init external/gsl git submodule update --init Source/CNTKv2LibraryDll/proto/onnx/onnx_repo git submodule update --init -- Source/Multiverso
Submodule Multiverso is used for enable DataParallelASGD for training.
Optional If you don't need DataParallelASGD support, then pass the option
--asgd=no to the configure command.
To build CNTK use the following commands (we assume that the CNTK repository was cloned to
cd ~/Repos/cntk mkdir build/release -p cd build/release ../../configure
Ensure that the
configure output corresponds to the packages you installed in the previous sections. I.e. ensure that
configure finds CUDA if installed, etc.
Do the following to build CNTK using all cores to minimize build time. Note that on some computer, this can overwhelm your system leading to hangs or breaks during the build.
make -j all
If the above overwhelms your computer, try specifying fewer cores. For example if you have more than 2 cores, and would like to keep 2 cores free from the build, you can try:
make -j"$(($(nproc) - 2))" all
If you want to be absolutely safe, just use 1 core:
This should produce a release build of CNTK. In case you would like to get a debug build use the following parameter when invoking
Quick test of CNTK build functionality
To ensure that CNTK is working properly in your system, you can quickly run an example from the Hello World - Logistic Regression tutorial. This example trains a simple network and can be directed to use either CPU or GPU, which helps quickly ensure that CNTK is functioning properly.
Below we assume that the CNTK repository is cloned to
build/release was used as a sub-directory for the build.
- Provide the path to the CNTK binaries and change to the
export PATH=$HOME/Repos/cntk/build/release/bin:$PATH cd ~/Repos/cntk/Tutorials/HelloWorld-LogisticRegression
First try the example:
cntk configFile=lr_bs.cntk makeMode=false
If the sample runs, i.e., if there are no error messages, you will get output related first to reading the configuration, followed by the output of the actual network training.
Trying CNTK with GPU
If you built CNTK for GPU usage, try using the GPU by executing the following commands:
cntk configFile=lr_bs.cntk makeMode=false deviceId=auto
Near the beginning of the output you should see a line confirming a GPU was used:
Model has 9 nodes. Using GPU 0.
Note that GPU ID may be different. The
deviceId parameter defines what processor to use for computation.
deviceId=-1means use CPU. Default value
deviceId=Xwhere X is an integer >=0 means use GPU X, i.e.
deviceId=0means GPU 0, etc.
deviceId=automeans use GPU, select GPU automatically
Contributing to CNTK code
If you plan modifications to the code you should read the information on Developing and Testing.