Overview
The article discusses the integration of Ray infrastructure at Pinterest, detailing the journey, challenges, and solutions implemented to optimize machine learning workflows. It highlights the importance of a robust Ray setup in a web-scale environment and outlines the development and production processes involved.
What You'll Learn
1
How to integrate Ray into a web-scale company environment
2
Why persistent logging and metrics are crucial for Ray infrastructure
3
When to implement security measures for Ray clusters
4
How to optimize Ray infrastructure for cost efficiency
5
How to utilize Kubernetes for managing Ray clusters
Prerequisites & Requirements
- Understanding of Kubernetes and Ray architecture
- Familiarity with logging and monitoring tools like Prometheus and Grafana(optional)
Key Questions Answered
What are the key challenges faced when integrating Ray at Pinterest?
Key challenges included limited access to Kubernetes API, ephemeral logging and metrics, and the need for robust security measures. These challenges necessitated innovative solutions to ensure stable and secure Ray cluster operations within Pinterest's infrastructure.
How does Pinterest ensure security for its Ray infrastructure?
Pinterest addresses security by implementing strict authentication and authorization protocols for Ray clusters. This includes using Envoy as a service mesh and applying mutual TLS for secure communication between Ray Pods, ensuring that only authorized users can access the Ray Dashboard and execute code.
What improvements have been made in Ray infrastructure at Pinterest?
Improvements include the development of a dedicated user interface for persistent logging and metrics, the transition from client-side to server-side management of Ray clusters, and the implementation of a modular API Gateway to streamline interactions with Kubernetes.
What are the use cases for Ray infrastructure at Pinterest?
Ray infrastructure is used for multiple recommender system model training, batch inference jobs, and experimental workloads. Pinterest runs over 5000 training jobs and 300 batch inference jobs per month, showcasing the scalability and efficiency of Ray in production environments.
Key Statistics & Figures
Training Jobs per month
5000+
This statistic reflects the scale at which Pinterest is utilizing Ray for model training.
Batch Inference Jobs per month
300+
This indicates the volume of batch inference operations being conducted using Ray at Pinterest.
Reduction in job runtime
4x
Ray has enabled a reduction in job runtime from 1 hour to 15 minutes for production GPU inference jobs.
Technologies & Tools
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Backend
Ray
Used for distributed computing and machine learning tasks at Pinterest.
Orchestration
Kubernetes
Manages the deployment and scaling of Ray clusters.
Service Mesh
Envoy
Facilitates secure communication and traffic management for Ray services.
Database
Mysql
Stores state information related to Ray clusters for lifecycle management.
Key Actionable Insights
1Invest in persistent logging and metrics for your Ray infrastructure to enable better monitoring and debugging.This approach allows teams to analyze performance and stability without needing to maintain active Ray clusters, thus reducing costs associated with idle resources.
2Implement robust security measures, including mutual TLS and service mesh architectures, to protect your Ray clusters.Given the vulnerabilities associated with open access to Ray APIs, ensuring that only authorized users can interact with the infrastructure is critical for maintaining data integrity and security.
3Utilize Kubernetes effectively to manage Ray clusters, taking advantage of custom resource definitions (CRDs) to streamline operations.This can help in managing the lifecycle of Ray clusters and jobs, ensuring that resources are utilized efficiently and securely.
Common Pitfalls
1
Failing to implement persistent logging can lead to difficulties in debugging and monitoring Ray workloads.
Without persistent logs, teams may struggle to analyze performance issues or track the lifecycle of jobs, leading to inefficiencies and increased operational costs.
2
Neglecting security measures can expose Ray clusters to unauthorized access and potential vulnerabilities.
Given the flexible nature of Ray, it's crucial to enforce strict authentication and authorization protocols to protect sensitive data and maintain system integrity.
Related Concepts
Kubernetes Management
Ray Architecture
Machine Learning Workflows
Security In Distributed Systems