Deep Learning GPU Benchmarks 2022

An overview of current high end GPUs and compute accelerators best for deep and machine learning tasks. Included are the latest offerings from NVIDIA: the Hopper and Ada Lovelace GPU generation. Also the performance of multi GPU setups is evaluated. Overview of the benchmarked GPUs Although we only tested a small selection of all the available GPUs, we think we covered all GPUs that are currently best suited for deep learning training and development due to their compute and memory capabilities and their compatibility to current deep learning frameworks, namely Pytorch and Tensorflow.

Exploring Stable Diffusion with an AIME Workstation using AIME MLC

Using AI to generate images from text descriptions yields impressive results. Thanks to the deep learning developer framework AIME-MLC, the installation and operation of such a model like Stable Diffusion is possible without much effort and free of charge. The installation process and the basics of Stable Diffusion are described below.

Efficient deep learning training - using the practical example of training the ResNet50 model on the ImageNet data set

In order to achieve good results with the shortest possible training times when training deep learning models, it is essential to find suitable values ​​for the training parameters such as learning rate and batch size. The search for suitable values ​​depends on the model to be trained, the amount of data used, but also the available hardware and can therefore prove to be quite time-consuming, since a training run, depending on the model and the training data used, can be very long (up to several days).

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.