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Scaling Machine Learning Models with AWS
Model Complexity and Computational Needs
- Machine learning models have seen exponential growth in the number of parameters, from perceptron models in the 1950s to GPT models with trillions of parameters today.
- This growth in model complexity has led to an increased need for high computational power, reaching petaflops of computing power to train large models like GPT-4.
- Training these large models can take days or even months, highlighting the challenge of scaling machine learning.
AWS Generative AI Stack
- The AWS Generative AI stack for training large-scale models consists of several key components:
- Infrastructure: GPU instances like P4d, P5, and P5e, along with Trainium and EFA for networking and acceleration.
- Managed services: Amazon Sagemaker, EC2 Capacity Reservations, and Nitro for serving and inference.
- Orchestration and tools: Amazon Bedrock, Amazon Q suite for building and deploying AI applications.
Orchestrating ML Training on AWS
- Key challenges in large-scale distributed training include:
- Cluster provisioning and management
- Infrastructure stability and resilience to failures
- Optimizing distributed training performance
- The SageMaker HyperPod service addresses these challenges by:
- Providing a turnkey solution for cluster creation and management
- Implementing auto-resume capabilities for fault-tolerance
- Optimizing the training environment for high performance
NinjaTech's Journey with AWS
- NinjaTech, an AI startup, has built its platform on top of the AWS Generative AI stack.
- They use a multi-step process to train their models, including code generation, execution verification, and fine-tuning.
- AWS services like CloudWatch, DynamoDB, and SageMaker have been critical in enabling NinjaTech's "agentic compound AI" architecture.
- NinjaTech's "Super Agent" model, which leverages multiple external and internal models, has achieved state-of-the-art results on various benchmarks.
Additional Resources
- The "Awesome Distributed Training" GitHub repository provides guidelines, examples, and best practices for running distributed machine learning workloads on AWS.
- The repository includes information on cluster provisioning, infrastructure stability, performance optimization, and observability.
- Upcoming sessions on the AWS Insight profiling tool are recommended for a deeper dive into distributed training performance.