AWS AI’s Post

Accelerate LLM training with #Meta Llama 3 and #AWSTrainium. 🦙⚡ https://go.aws/3RTFged In this post, you'll learn best practices for training LLMs on AWS Trainium, scaling the training on a cluster with over 100 nodes, improving efficiency of recovery from system and hardware failures, improving training stability, & achieving convergence. #AWS

Deploying Llama2 models can be expensive but AWS has specific hardware in the Inferential family that supports cost effective deployment. The blog post discusses the deployment of Llama 2 on Amazon EC2 Inf2 instances using AWS Inferentia2 for both training and inference. It provides detailed steps on creating, compiling, and deploying the Llama-2 model using the latest AWS Neuron SDK release, achieving high performance at low cost. https://lnkd.in/gDib5znW

One more step toward cheaper or faster LLM inference on AWS? Also what about Neuron support on Lambda and ECS Fargate? (cold start is an issue .. I know) https://lnkd.in/gyZgY-53

Check out this awesome blogpost from my colleagues Jianying Lang, Fei Chen et.al. on training SOTA LLMs on AWS Trainium, our AWS ML chips.

AWS Weekly Roundup: LlamaIndex support for Amazon Neptune, force AWS CloudFormation stack deletion, and more

A great detailed overview of Llama2 on Inferentia 2, including performance metrics and code samples on getting started. Startup up an Inf2 instance and give it a try today on EC2 or Sagemaker. #aws #generativeai #inf2 #machinelearning https://lnkd.in/ecjpV38b

Really excited to share some of our learnings integrating Amazon Web Services (AWS) Inferentia instances into our evals stack. I know a lot of our customers have trouble obtaining GPUs for quick eval iteration cycles in their development process so we've worked to remove that friction. #AWS #GenerativeAI #LLMs #Evals

AWS Weekly Roundup: LlamaIndex support for Amazon Neptune, force AWS CloudFormation stack deletion, and more

We’re excited to share our open-source framework that lets you score different generative language models across various evaluation tasks and benchmarks - used by leaderboards such as Hugging Face. While working with the Amazon Web Services (AWS) team to train our models on AWS Tranium, we realized we were restricted to both VRAM and the availability of GPU instances when it came to the mainstream tool for LLM evaluation. Our open source solution overcomes these challenges—integrating AWS Neuron, the library behind AWS Inferentia and Trainium, into lm-evaluation-harness. Take a look at: ✅ How We Broke Down Our Tests ✅ The Challenges We Encountered ✅ An Example of Using the Testing Harness on AWS Inferentia A huge thank you Michael Feil and Jim Burtoft for the partnership and collaboration, giving back to the developer community. #Gradient #GradientAI #AWS #LLM #OpenSource #LLMEvaluation

AWS Weekly Roundup: LlamaIndex support for Amazon Neptune, force AWS CloudFormation stack deletion, and more