Setup Linux Binary Script

Linux binary installation with scripts

This page will walk you through the process of installing the Microsoft Cognitive Toolkit (CNTK) based on a binary distribution we have prepared and you can download from our website. It is an easy way to get you up-and-running quickly.

Note: This instructions apply to release 2.0.beta5.0.

Note: You can find an overview about all the available installation options for CNTK on [this page] (./Setup-CNTK-on-your-machine).

We will install the CNTK binaries, the CNTK prerequisites, and create a new Python 3.4 environment on your computer. The changes are as much localized as possible to not impact any other installed software. If you have already installed a previous version of CNTK2 on your machine, the script will update this installation.

Please follow the steps below to install the binaries. The installation script will additionally download the necessary dependencies, so an Internet connection is required when running the script.

The script was tested on Ubuntu 14.04 and 16.04 only. It will generate a warning about possible failures if run on any other platform.

Step 1: Download the appropriate binary package from CNTK Releases page. Unpack the tar.

Note: Choose a GPU binary download only if your machine has an NVidia GPU.

Step 2: Run bash Installation script

Below we assume that you have unpacked the CNTK Binary package to /home/username.

• Run:

  cd /home/username/cntk/Scripts/install/linux
  ./install-cntk.sh
  

The script will download some installation packages from remote locations. Expect that running it will take some time (expect at least 20 minutes on Ubuntu 16.04 and even more on Ubuntu 14.04 if none of the required pre-requisites are detected on your system).

By the end of the successful setup the script will inform you about the location of the CNTK Python environment script and of the location of CNTK Tutorials and Examples.

• For GPU Systems: Please ensure that you have the latest NVIDIA driver

Step 3: Verify the setup (Python)

• Activate CNTK environment by executing the command specified by the Installation script (see previous step). In our example it will be:

  source "/home/username/cntk/activate-cntk"
  

• Run an example from Tutorials directory to verify your installation. Run python NumpyInterop/FeedForwardNet.py. You should see the following output on the console:

  Minibatch[   1- 128]: loss = 0.564038 * 3200
  Minibatch[ 129- 256]: loss = 0.308571 * 3200
  Minibatch[ 257- 384]: loss = 0.295577 * 3200
  Minibatch[ 385- 512]: loss = 0.270765 * 3200
  Minibatch[ 513- 640]: loss = 0.252143 * 3200
  Minibatch[ 641- 768]: loss = 0.234520 * 3200
  Minibatch[ 769- 896]: loss = 0.231275 * 3200
  Minibatch[ 897-1024]: loss = 0.215522 * 3200
  Finished Epoch [1]: loss = 0.296552 * 25600
  error rate on an unseen minibatch 0.040000
  

• Run the Jupyter notebooks, which contain several tutorials, by executing the following commands:

  cd /home/username/cntk/Tutorials
  jupyter notebook
  

This will spawn a browser with all available notebooks ready to be run. Should the notebooks fail to execute, run conda install jupyter from the activated CNTK environment cntk-py34.

Step 4 (Optional): Verify the setup (BrainScript)

Perform the following command in the CNTK environment command prompt (see previous step):

cd /home/username/cntk/Tutorials/HelloWorld-LogisticRegression
cntk configFile=lr_bs.cntk makeMode=false command=Train

The last lines of the CNTK output on the console should look similar to this:

Finished Epoch[42 of 50]: [Training] lr = 0.04287672 * 1000; err = 0.01152817 * 1000; totalSamplesSeen = 42000; learningRatePerSample = 0.039999999; epochTime=0.050296s
Finished Epoch[43 of 50]: [Training] lr = 0.04388479 * 1000; err = 0.01206375 * 1000; totalSamplesSeen = 43000; learningRatePerSample = 0.039999999; epochTime=0.052143s
Finished Epoch[44 of 50]: [Training] lr = 0.04223433 * 1000; err = 0.01105073 * 1000; totalSamplesSeen = 44000; learningRatePerSample = 0.039999999; epochTime=0.057235s
Finished Epoch[45 of 50]: [Training] lr = 0.04208072 * 1000; err = 0.01140516 * 1000; totalSamplesSeen = 45000; learningRatePerSample = 0.039999999; epochTime=0.051414s
Finished Epoch[46 of 50]: [Training] lr = 0.04261674 * 1000; err = 0.01158323 * 1000; totalSamplesSeen = 46000; learningRatePerSample = 0.039999999; epochTime=0.051115s
Finished Epoch[47 of 50]: [Training] lr = 0.04326523 * 1000; err = 0.01164283 * 1000; totalSamplesSeen = 47000; learningRatePerSample = 0.039999999; epochTime=0.051611s
Finished Epoch[48 of 50]: [Training] lr = 0.04225255 * 1000; err = 0.01148774 * 1000; totalSamplesSeen = 48000; learningRatePerSample = 0.039999999; epochTime=0.0509s
Finished Epoch[49 of 50]: [Training] lr = 0.04173276 * 1000; err = 0.01124948 * 1000; totalSamplesSeen = 49000; learningRatePerSample = 0.039999999; epochTime=0.049659s
Finished Epoch[50 of 50]: [Training] lr = 0.04399402 * 1000; err = 0.01202178 * 1000; totalSamplesSeen = 50000; learningRatePerSample = 0.039999999; epochTime=0.052725s

COMPLETED.

If you have an NVidia GPU and installed a GPU build, you can also try this command:

cntk configFile=lr_bs.cntk makeMode=false command=Train deviceId=auto

To validate that the GPU was being used, look for the following line in your output:

Model has 9 nodes. Using GPU 0.