Deploying Many Models Efficiently with Ray Serve
Short Summary:
This presentation focuses on efficiently deploying many models using Ray Serve, a distributed computing framework. Key points include model composition (combining multiple models within a single application for tasks like computer vision), multi-application (managing independent deployments of multiple applications on a single Ray cluster), and multiplexing (on-demand model loading for large numbers of models). These techniques address challenges like efficient hardware usage, independent scaling, and operational overhead. Real-world examples from Samsara and AnyScale endpoints, along with a Clarify use case, demonstrate significant cost savings and performance improvements achieved through Ray Serve's features. The detailed explanation covers the processes of model composition, multi-application deployment, and multiplexing, including resource allocation strategies and performance benchmarks.
Detailed Summary:
The presentation is divided into three main sections:
Section 1: Model Composition: This section introduces the challenges of serving many models, such as efficient hardware usage, independent scaling, and operational overhead. The speaker contrasts the monolithic approach (all models in one container) and the microservices approach (each model as a separate service), highlighting the drawbacks of both. Model composition in Ray Serve is presented as a solution that combines the advantages of both, allowing independent scaling and upgrades while sharing resources efficiently. A computer vision example (image preprocessing, classification, detection, and business logic) is used to illustrate how to define and link models within a single Python file for deployment. The speaker emphasizes the ability to use fractional resources and provides a real-world example of Samsara saving 50% on ML infrastructure costs by switching to Ray Serve's model composition. A key takeaway is the ease of resource sharing and independent scaling within a single application.
Section 2: Multi-Application: This section builds upon model composition, addressing the need for independent upgrades across different teams or projects. The speaker argues that combining all models into a single application limits flexibility and increases deployment risk. Multi-application in Ray Serve allows multiple applications to coexist on the same cluster, each with its own lifecycle and upgrade path. The AnyScale endpoints example demonstrates the ease of adding or removing models (like the Llama family of LLMs) without affecting other applications. The focus here is on independent upgrades and lifecycle management, contrasting it with the limitations of the single-application approach. The speaker highlights the continued benefits of efficient resource allocation and easy testing/monitoring across all applications.
Section 3: Multiplexing: This section addresses the challenge of serving a very large number of models, potentially exceeding the capacity of a single cluster. The speaker introduces Ray Serve's multiplexing API as a solution. The core idea is to intelligently load models on demand, caching them in memory to minimize loading times and maximize resource utilization. A comparison with SageMaker's multimodal endpoint is presented, showing Ray Serve's multiplexing achieving a 30% better throughput due to a higher model cache hit rate. The application of multiplexing to AnyScale endpoints, combined with LoRA techniques for efficient model sharing, is also discussed. The Clarify use case, involving thousands of customer-specific forecasting models, illustrates the significant performance gains (80% reduction in training time and cold-start latency) achieved through Ray Serve's multiplexing and auto-scaling capabilities. The speaker emphasizes the ease of integration with existing code (using Python decorators) and the overall efficiency of the system.
Throughout the presentation, the speakers repeatedly emphasize the benefits of Ray Serve in terms of cost savings, performance improvements, and simplified operational overhead when dealing with many models. The use of real-world examples and benchmarks strengthens the claims made about the effectiveness of the described techniques.