Benchmarking Deep learning platforms by means of Tensorflow and Docker

Description

The goal of the DEEP Hybrid DataCloud project [1] is to prepare a new generation of e-Infrastructures that harness latest generation technologies, supporting e.g. deep learning. In order to assess our solutions for new e-Infrastructures, a set of benchmark tools is needed.

Aim of this project is to prepare a set of benchmarks in form of Docker image(s) based on Tensorflow [2] and a number of pre-existing Tensorflow scripts (e.g. [3]), run them by means of various container tools and in different environments, compare performance.

Tasks

get acquanted with Docker containers and best practices [4, 5].
adapt existing Convolutional Neural Network (CNN) benchmarks [3] and pack them in Docker image(s) such that they can be run on GPUs in the DEEP testbeds.
run these benchmarks in HPC-like environment by means of udocker and singularity and on DEEP testbeds, use syntetic and real data, compare performance with bare-metal case.
(optional) extend the benchmarks for another neural network type (neural network code will be provided). Run same experiments as for CNN scripts.
at the end of Praktikum you write a short report with results of the performance tests (similar to [3]).