AWS re:Invent 2024 - Supercharge your AI and ML workloads on Amazon ECS

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This blog post summarizes key highlights from the AWS re:Invent 2024 session "Supercharge your AI and ML workloads on Amazon ECS (SVS331)." We'll explore how to leverage Amazon ECS for building and scaling machine learning and generative AI applications, covering architectural considerations, performance optimization techniques, and a real-world example using stable diffusion for image generation

Imagine you've just built a cool new Generative AI app that can turn words into pictures. It's so popular that thousands of people are using it simultaneously. Now you're worried - will your app crash? How can you ensure it keeps working smoothly for everyone?

At AWS re:Invent 2024, Steve Kendrex (Sr. Manager, Product Management, AWS), Abhishek Nautiyal (Senior Product Manager Technical-External Services, AWS), and Frank Fan (Senior Container Specialist Solution Architect, AWS) shared insights on running machine learning workloads at scale using Amazon Elastic Container Service (Amazon ECS). This blog summarizes key takeaways from their re:Invent session.

The Core Challenge: Scalable and Reliable ML Inference

The main problem this talk addressed was: How can we build AI systems that work well for many people simultaneously? This is especially important for AI that creates content, like pictures or text.

To achieve this, the speakers emphasized three key concepts:

  • Reliability: Ensuring the AI system doesn't break or stop working, even under heavy use.
  • Performance: Making the AI work quickly to minimize wait times for results.
  • Scalability: Allowing the system to grow easily as user numbers increase, without compromising speed or cost-effectiveness.

Architectural Considerations

The speakers shared three important tips for building a robust AI system:

  1. Split Your System into Parts: Like a restaurant with separate front (ordering) and back (kitchen) areas, keep your AI system's frontend and backend separate. This allows for easier scaling of the frontend without affecting the backend.
  2. Use a Waiting List: Implement a message queue to manage high demand, similar to giving customers numbers in a busy restaurant.
  3. Be Ready to Change Your AI: Design your system to easily incorporate new AI models, much like quickly updating a restaurant menu to include popular new dishes.

Performance Optimization Techniques

They shared three main ways to enhance AI system performance:

  1. Choosing the Right Computer Power: Use CPUs for simpler AI tasks and GPUs or AWS Inferentia for complex, time-sensitive jobs
  2. Loading Your AI Faster: Speed up AI startup times by:
  3. Making the Most of Special AI Chips: For GPUs:
    • Share one GPU among different parts of your AI system.
    • Monitor GPU performance using NVIDIA's DCGM.

Scalability and Cost Optimization

The speakers shared the following strategies:

  1. Auto Scaling Strategies: Use ECS service auto scaling with Application Auto Scaling, introducing a "backlog per task" custom metric for precise scaling.

  2. Capacity Providers: Use ECS Capacity Providers to automatically scale EC2 instances with ECS tasks.

  3. Cost-Effective Scaling: Utilize EC2 Spot instances for interruptible workloads and implement predictive scaling to optimize costs.

Real-World Example: Stable Diffusion on ECS

The speakers demonstrated a practical AI system for image creation, including:

  • Building the AI Machine: Setting up an ECS task with components for user interaction, request management, system monitoring, and GPU performance tracking
  • Keeping AI Files Ready: Using Amazon EFS for quick model file access
  • Scaling When Busy: Implementing smart scaling based on queue depth and active workers
  • Showing How It Works: Showing how the system handles varying demand by adjusting worker numbers

Monitoring and Observability

They emphasized the importance of monitoring through:

  • Watching the Containers: Using CloudWatch Container Insights for container-level monitoring
  • Checking the AI's Brain: Creating custom metrics to monitor AI performance, like GPU utilization.
  • Following the Requests: Using AWS X-Ray to track request paths through the system.

What's Next for AI on ECS?

As AI creation becomes more widespread, robust systems for running these AI workloads are crucial. Amazon ECS offers flexibility and strong integration with other AWS services, making it an excellent choice for AI deployments

The presenters encouraged further exploration through hands-on workshops and detailed documentation to deepen understanding of ML workloads on ECS

For those interested in more details, the full session recording is available on the AWS Youtube Channel, featuring Steve Kendrex, Abhishek Nautiyal, and Frank Fan.