Prototyping CK workflows for ML training
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compatibility License

This CK repository provides automated, portable and customizable workflows for AI/ML benchmarks. We suggest you to look at this Getting started guide before using CK.

Minimal CK installation

The minimal installation requires:

  • Python 2.7 or 3.3+ (limitation is mainly due to unitests)
  • Git command line client.

You can install CK in your local user space as follows:

$ git clone
$ export PATH=$PWD/ck/bin:$PATH

You can also install CK via PIP with sudo to avoid setting up environment variables yourself:

$ sudo pip install ck

Install this CK repository

$ ck pull repo:ck-tbd-suite

Note that CK will download other CK repositories with required dependencies including TensorFlow and ImageNet CK packages.

If you already have CK and repositories installed, we suggest you to update them as follows:

$ ck update all

Detect and test CUDA

$ ck detect platform.gpgpu --cuda

If you are prompted to choose a platform description, select the one the name of which is the same or similar to your platform or generic-linux.

Detect all platform parameters in a unified way

$ ck detect platform

Install or detect ImageNet dataset

Before running AI/ML benchmarking via CK, you need to install ImageNet data sets or detect already installed ones.

Just for a test, you can install a tiny ImageNet training data set (several MB) via CK as follows:

$ ck install package:imagenet-2012-train-min

You can later install a full ImageNet training data set (~150GB) via CK which will co-exist with the above one as follows:

$ ck install package:imagenet-2012-train

Alternatively, you can detect already installed training sets as follows:

$ ck detect soft:dataset.imagenet.train --search_dirs={root path to your training dataset}

In the same way, you can install a small demo ImageNe validation data set (a few MB) via CK:

$ ck install package:imagenet-2012-val-min

and later full ImageNet validation data set (~10GB) via CK

$ ck install package:imagenet-2012-val

or detect already installed ones as follows:

$ ck detect soft:dataset.imagenet.val --search_dirs={root path to your validation dataset}

You can see all registered virtual environments in the CK as follows:

$ ck show env

Install TF

You can install different versions of TF via CK as follows:

$ ck install package --tags=lib,tensorflow

For example, you can install pre-built CUDA version of TensorFlow 1.8.0 if you have CUDA >=9.0 as follows:

$ ck install package:lib-tensorflow-1.8.0-cuda

or TensorFlow 1.4.0 if you have CUDA < 9.0 as follows:

$ ck install package:lib-tensorflow-1.4.0-cuda

Prepare training set of TF

You can now convert your tiny demo ImageNet training set to a TF format as follows:

$ ck install package:imagenet-2012-train-min-tf

and/or the full one as follows:

$ ck install package:imagenet-2012-train-tf

However processing of the full training set requires a large amount of disk space (~100GB) and execution time (hours and even days), you can detect already processed training set in the TF format as follows (for example on a shared drive):

$ ck detect

Run image classification training using TF

Now you can try to run image classification training using TF as follows:

$ ck run program:image-classification-inception-tf

You need to select a specific command line:

  • train (default benchmarking)
  • train-and-profile (to use nvprof)
  • train-and-profile-fp32 (to use nvprof & fp32)

You can also customize your training run as follows:

$ ck run program:image-classification-inception-tf --env.LEARNING_OPTIMIZER=sgd \
   --env.BATCH_SIZE=32 \
   --env.LEARNING_RATE=0.1 \
   --env.NUM_EPOCHS_PER_DECAY=30 \
   --env.WEIGHT_DECAY=0.0001 \

You can clean all temporal check points by adding "--clean" flag:

$ ck run program:image-classification-inception-tf --clean

To run a small test use the following command line:

$ ck run program:image-classification-inception-tf --env.BATCH_SIZE=2 --env.MAX_NUMBER_OF_STEPS=100 --clean

Next steps

We plan to add a CK package for a smaller training set (to validate the functionality of this workflow), CK module to expose or calculate accuracy of a final model, and automate collection and processing of nvprof statistics.

We also plan to prepare CK modules to perform exploration of above parameters and save results in a ReQuEST format to visualize them via live CK scoreboard.