James is an ML Engineer and Head of Engagement Marketing at Paperspace, who has spent 4 years as a Machine Learning marketing specialist. BSc in Psychology from University of St. Andrews, Scotland.
This blog post details the concept of mixed precision training, its benefits, and how to implement it automatically with popular Deep Learning frameworks PyTorch and TensorFlow.
Follow this guide to learn what makes the Ampere GPUs so powerful. We will then show how the A4000, A5000, and A6000 are the most cost effective GPU offerings at Paperspace.
In this new tutorial, we will examine YOLOR object detection with PyTorch in detail to see how it combines implicit and explicit information with a unified representation. We then demonstrate how to use YOLOR with Gradient Notebooks.
Follow this guide to learn how to use built in and third party tools to monitor your GPU utilization with Deep Learning in real time.
These benchmarks show how the single GPU instances for Gradient Notebooks perform against one another in terms of cost, throughput, GPU memory, and more!
Follow our latest tutorial to see how to implement use Colossal AI with Gradient Notebooks to train a ResNet34 classifier on a multi-GPU machine.
Follow this tutorial to learn how GLIDE works and see how to implement it in a Gradient Notebook
Neural Machine Translation is the practice of using Deep Learning to generate an accurate translation of text from one language to another.
Follow this guide to learn about the JAX library, and learn how to directly implement it in Gradient.
