Introducing the Lambda EchelonLambda Echelon [https://lambdalabs.com/gpu-cluster/echelon] is a GPU cluster designed for AI. It comes with the compute, storage, network, power, and support you need to tackle large scale deep learning tasks. Echelon offers a turn-key solution to faster training, faster hyperparameter search, and faster inference.
The Lambda Deep Learning Blog
Voltron Data Case Study: Why ML teams are using Lambda Reserved Cloud Clusters
November 01, 2022
How to fine tune stable diffusion: how we made the text-to-pokemon model at Lambda
September 28, 2022
Lambda Cloud Storage is now in open beta: a high speed filesystem for our GPU instances
April 18, 2022
Lambda Teams Up With Razer to Launch the World’s Most Powerful Laptop for Deep Learning
April 11, 2022
This presentation is a high-level overview of the different types of training regimes that you'll encounter as you move from single GPU to multi GPU to multi node distributed training. It briefly describes where the computation happens, how the gradients are communicated, and how the models are updated and communicated.
BERT is Google's SOTA pre-training language representations. This blog is about running BERT with multiple GPUs. Specifically, we will use the Horovod framework to parrallelize the tasks. We ill list all the changes to the original BERT implementation and highlight a few places that will make or break the performance.