In Microsoft/CNTK-R: R Interface to the Microsoft Cognitive Toolkit (CNTK)

Installing CNTK Python Binaries in an Anaconda Virtual Environment

The R bindings for CNTK rely on the reticulate package to connect to CNTK and run operations. In order to make sure that your environment is set up correctly, you'll need to first install CNTK in a Python environment locally, and then set that Python environment as your default reticulate environment. CNTK is available in a variety of precompiled binaries, which you can install using the pip installer. Please view the link with the precompiled binaries to ensure you have all the necessary prerequisites installed prior to installing CNTK. It is recommended you use the Anaconda Python distribution as this will install all the necessary core math libraries for you automatically. For macOS users, you are recommended to use CNTK docker images available here.

Create Conda Virtual Environment

In this vignette, we will assume you are using the conda virtual environment mechanism to create and modify Python virtual environments. You can create a Python 3.5 environment with conda by using the conda create command:

conda create -n cntk-py35 python=3.5 anaconda

The environment will be named cntk-py35 and the additional flag anaconda ensures that the distribution will contain over a 100 prebuilt Python packages for scientific computing (list here).

Install CNTK Using pip Binary Wheels

We can activate that environment by running

source activate cntk-py35

Now that we are in our virutla environment for CNTK, let's install the appropriate Python binary using a Python "wheel". For example, here are the installation instructions for CNTK on a Python 3.5 environment with Ubuntu 16.04 system with GPU support:

pip install https://cntk.ai/PythonWheel/GPU/cntk-2.1-cp35-cp35m-linux_x86_64.whl

Installing the CNTK R Package

Now we can install the CNTK R package. Let's open an R session, and set cntk-py35 as our RETICULATE_PYTHON environment:

Sys.setenv(RETICULATE_PYTHON="/home/alizaidi/anaconda3/envs/cntk-py35/bin/python")

You can check your environment:

reticulate::py_config()
python:         /home/alizaidi/anaconda3/envs/cntk-py35/bin/python
libpython:      /home/alizaidi/anaconda3/envs/cntk-py35/lib/libpython3.5m.so
pythonhome:     /home/alizaidi/anaconda3/envs/cntk-py35:/home/alizaidi/anaconda3/envs/cntk-py35
version:        3.5.2 |Continuum Analytics, Inc.| (default, Jul  2 2016, 17:53:06)  [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
numpy:          /home/alizaidi/anaconda3/envs/cntk-py35/lib/python3.5/site-packages/numpy
numpy_version:  1.11.2

python versions found: 
 /home/alizaidi/anaconda3/envs/cntk-py35/bin/python
 /usr/bin/python
 /usr/bin/python3
 /home/alizaidi/anaconda3/bin/python
 /home/alizaidi/anaconda3/envs/chainer3/bin/python
 /home/alizaidi/anaconda3/envs/cntkkeraspy35/bin/python
 /home/alizaidi/anaconda3/envs/cs231n36/bin/python
 /home/alizaidi/anaconda3/envs/jupyterlab/bin/python
 /home/alizaidi/anaconda3/envs/magenta/bin/python
 /home/alizaidi/anaconda3/envs/mxnet/bin/python
 /home/alizaidi/anaconda3/envs/pydcn/bin/python
 /home/alizaidi/anaconda3/envs/pytorch/bin/python
 /home/alizaidi/anaconda3/envs/snorkel/bin/python
 /home/alizaidi/anaconda3/envs/stackgan/bin/python
 /home/alizaidi/anaconda3/envs/tensorflow/bin/python
 /home/alizaidi/anaconda3/envs/tensorflow-py35/bin/python

and ensure you have the Python CNTK module in this environment:

reticulate::py_module_available("cntk")
[1] TRUE

Install CNTK R Package

Now we are ready to install the R bindings. To do so, we'll rely on the github package installer from the devtools package. In order to use devtools, Windows users also need to install Rtools (and make sure you select "yes" to append the Rtools compilers to your system PATH variable).

devtools::install_github("Microsoft/CNTK-R")

Microsoft/CNTK-R documentation built on May 28, 2019, 1:52 p.m.