AIME Machine Learning Framework Container Management

To install and set up a deep learning framework in a GPU environment can get quite tedious as the requirements for installed drivers and correct version of required libraries (CUDA, CUDNN) must be met to set it up successful. This guide describes how to install and set up a eligible version of Tensorflow, Pytorch or Mxnet in minutes and master the problem once and for all.

Deep Learning GPU Benchmarks 2019

A state of the art performance overview of current high end GPUs used for Deep Learning. All tests are performed with the latest Tensorflow version 1.15 and optimized settings. The results can differ from older benchmarks as latest Tensorflow versions have some new optimizations and show new trends to achieve best training performance and turn around times. Also the performance for multi GPU setups is evaluated.

CLOUD VS. ON-PREMISE - Total Cost of Ownership Analysis

Deep learning applications require powerful multi-GPU systems for development and operation, which can be very expensive to rent in the cloud for long-term operations. Which infrastructure offers the best compromise between time-to-solution, cost-to-solution and availability of resources?