Join Kamran Khan and Matthew McClean as they discuss AWS Trainium and Inferentia, powerful AI accelerators offering enhanced performance and cost savings. They delve into integration with PyTorch, JAX, and Hugging Face, along with support from industry leaders like W&B. Explore the evolution and performance comparison of these AI chips, flexibility in model training with Trainium, and workflow integration with SageMaker. Discover the distinctions between inference and training on accelerators and explore AWS services for generative AI.
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Quick takeaways
AWS Trainium and Inferentia aim to offer customers enhanced availability, compute elasticity, and energy efficiency in AI workloads.
Using Inferentia and Trainium can lower training model costs by up to 46% on AWS, while optimizing performance for machine learning workloads.
Deep dives
Introduction of Inferentia and Tranium by AWS's Matt McLean and Gomran Khan
Matt McLean and Gomran Khan, representatives of AWS, discuss the purpose behind Inferentia and Tranium, AWS's purpose-built AI chips tailored for deep learning workloads. These chips aim to offer customers more choice, higher performance, and lower costs, making AI more accessible and efficient.
Comparison of Inferentia and Tranium with GPUs
Inferentia and Tranium are specialized accelerators designed for deep learning applications, offering key differences from GPUs. These accelerators feature a tensor engine to accelerate matrix multiplications efficiently and provide high bandwidth for memory, optimizing performance for machine learning workloads.
Performance Benchmarks and Cost Reduction
Through various benchmarks, it is revealed that utilizing Inferentia and Tranium can lower the cost of training models by up to 46% compared to traditional accelerators on AWS, while also reducing deployment costs and enhancing performance. These purpose-built accelerators focus solely on machine learning workloads, resulting in significant cost and efficiency benefits.
Compatibility and Deployment Options with Neuron SDK
The Neuron SDK offers compatibility with popular machine learning frameworks like PyTorch and TensorFlow, making it easier for users to leverage Inferentia and Tranium. Additionally, the SDK includes a compiler based on XLA for optimizing computational graphs and provides support for custom C++ operators, enhancing control and performance for AI workloads.
Matthew McClean is a Machine Learning Technology Leader with the leading Amazon Web Services (AWS) cloud platform. He leads the customer engineering teams at Annapurna ML helping customers adopt AWS Trainium and Inferentia for their Gen AI workloads.
Kamran Khan, Sr Technical Business Development Manager for AWS Inferentina/Trianium at AWS. He has over a decade of experience helping customers deploy and optimize deep learning training and inference workloads using AWS Inferentia and AWS Trainium.
AWS Tranium and Inferentia // MLOps podcast #238 with Kamran Khan, BD, Annapurna ML and Matthew McClean, Annapurna Labs Lead Solution Architecture at AWS.
Huge thank you to AWS for sponsoring this episode. AWS - https://aws.amazon.com/
// Abstract
Unlock unparalleled performance and cost savings with AWS Trainium and Inferentia! These powerful AI accelerators offer MLOps community members enhanced availability, compute elasticity, and energy efficiency. Seamlessly integrate with PyTorch, JAX, and Hugging Face, and enjoy robust support from industry leaders like W&B, Anyscale, and Outerbounds. Perfectly compatible with AWS services like Amazon SageMaker, getting started has never been easier. Elevate your AI game with AWS Trainium and Inferentia!
// Bio
Kamran Khan
Helping developers and users achieve their AI performance and cost goals for almost 2 decades.
Matthew McClean
Leads the Annapurna Labs Solution Architecture and Prototyping teams helping customers train and deploy their Generative AI models with AWS Trainium and AWS Inferentia
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
AWS Trainium: https://aws.amazon.com/machine-learning/trainium/
AWS Inferentia: https://aws.amazon.com/machine-learning/inferentia/
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
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Connect with Matt on LinkedIn: https://www.linkedin.com/in/matthewmcclean/
Timestamps:
[00:00] Matt's & Kamran's preferred coffee
[00:53] Takeaways
[01:57] Please like, share, leave a review, and subscribe to our MLOps channels!
[02:22] AWS Trainium and Inferentia rundown
[06:04] Inferentia vs GPUs: Comparison
[11:20] Using Neuron for ML
[15:54] Should Trainium and Inferentia go together?
[18:15] ML Workflow Integration Overview
[23:10] The Ec2 instance
[24:55] Bedrock vs SageMaker
[31:16] Shifting mindset toward open source in enterprise
[35:50] Fine-tuning open-source models, reducing costs significantly
[39:43] Model deployment cost can be reduced innovatively
[43:49] Benefits of using Inferentia and Trainium
[45:03] Wrap up
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