Gradient’s Post

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

Want to take your LLM game to the next level without breaking the bank? Gradient has cracked the code with AWS Inferentia! I just stumbled upon this gem of an article that breaks down how Gradient is making LLM benchmarking a breeze. And let me tell you, it's a game-changer! Here are the key takeaways: • Gradient's AI Development Lab is helping enterprises build custom LLMs and AI co-pilots. Pretty cool, right? • They faced some challenges with the mainstream benchmarking tool, but AWS Neuron came to the rescue! ️ • By integrating AWS Neuron into lm-evaluation-harness, Gradient can now benchmark their models against public ones during training and after. Talk about efficiency! • Moving to Amazon EC2 Inf2 instances powered by AWS Inferentia2 gave them access to a whopping 384 GB of shared accelerator memory. Plus, they saved up to 90% with AWS Spot Instances! • The article even provides a step-by-step guide on deploying Gradient's model using AWS Inferentia2 instances. It's like having a cheat sheet! If you're into LLMs and want to up your evaluation game, this is a must-read! Check out the full article here: "Gradient makes LLM benchmarking cost-effective and effortless with AWS Inferentia" https://lnkd.in/gW753SFp Happy Wednesday! Let me know your thoughts. Casey Jones #LLM #Benchmarking #AWSInferentia #Gradient #AI

Excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Using AWS Trainium and Inferentia based instances, through SageMaker, can help users lower fine-tuning costs by up to 50%, and lower deployment costs by 4.7x, while lowering per token latency. https://lnkd.in/dhAjnwtM #amazonsagemaker #llama2

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

Scale and simplify ML workload monitoring on Amazon EKS with AWS Neuron Monitor container. 🧠 As machine learning workloads become more complex, monitoring them can be challenging. 🤯 AWS Neuron Monitor container helps by providing real-time metrics and alerts for ML jobs running on Amazon EKS clusters with AWS Inferentia chips. 💡 Key benefits: - Easy to deploy - drops into EKS clusters and starts monitoring. 👍 - Visualize GPU utilization, batch sizes, throughput etc for each job. ✨ - Set alerts based on metrics to catch issues early. 🚨 - Works with popular frameworks like PyTorch, TensorFlow, MXNet. 🤝 With Neuron Monitor, you get full visibility into your ML workloads on EKS and Inferentia. This allows you to optimize resource usage, troubleshoot faster and reduce costs. 💰 Read more below on how to get started! There are also some great graphs showing the rich metrics you get. 📈 Let me know if you have any questions! 👇 https://lnkd.in/eA_JG43y #aws #amazon #ai #artificialintelligence #bigdata #ml #machinelearning #dataanalytics #datascience #genai #generativeai #llm #amazoneks #awsneuronmonitorcontainer #awsinferentia #awstrainium

Explore AWS's approach of fine-tuning the powerful Llama 2 model using QLoRA and deploying it on Amazon SageMaker, supercharged by the new AWS EC2 Inferentia2 instances. This approach not only enhances model performance but also ensures cost-efficient, high-speed ML inference, marking significant improvements in your AI applications. #finetuning #inf2 #QLoRA #SageMaker https://lnkd.in/eK5Vmuyx

📖 Gradient makes LLM benchmarking cost-effective and effortless with AWS Inferentia This is a guest post co-written with Michael Feil at Gradient. Evaluating the performance of large language models (LLMs) is an important step of the pre-training and fine-tuning process before deployment. The faster and more frequent you’re able to validate performance, the higher the chances you’ll be able to improve the performance of the model. […]

Calling out to all Foundation Models developpers in Asia - academia or start ups - check out the fantastic grants AWS offers to support you in development. Apply here between January 17th and March ! Let’s #build the future #together ! #genai #llm #aws #awsresearch

AWS Serverless Product Recommendation: powered by k-NN with OpenSearch #serverless #opensearch #aws

This is useful, if you do not want the data to leave your AWS VPC , but still want to use the Claude 3 !! Planning to lead my team to do a PoC for an use case.