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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 7·5 min read
Atlas: Optimizing Deployment of Compound AI Workflows on Heterogeneous Clusters
TL;DR
Atlas is a framework designed to optimize the deployment of compound AI workflows on heterogeneous clusters, significantly improving accuracy estimation and reducing deployment costs. By utilizing a Markovian Accuracy Predictor, Atlas achieves high correlation with oracle accuracy while cutting profiling costs by up to 2.6 times.
✦ Why It Matters
Implement Atlas to optimize your AI workflow deployments and reduce costs today.
Key Takeaways
- Use Atlas to optimize AI workflow deployments on heterogeneous clusters.
- Leverage MAP for accurate accuracy estimation without exhaustive profiling.
- Reduce deployment costs by up to 42% with optimized execution plans.
- Achieve high correlation with oracle accuracy using Atlas framework.
How It Works
Atlas employs a Markovian Accuracy Predictor (MAP) to estimate the accuracy of AI workflows by analyzing local transitions between stages. This approach allows for the composition of accuracy profiles based on the workflow's topology, enabling efficient decision-making without the need for complete end-to-end profiling.