Bring Batch Capability Into Kubernetes, Using AI and Big Data as an Example

Overview

Explore advanced scheduling features for batch workloads in Kubernetes, focusing on AI and big data applications using frameworks like TensorFlow and Spark. Learn about fair-share scheduling and the Volcano project.

Syllabus

• Introduction to Kubernetes
Overview of Kubernetes architecture
Basics of Kubernetes scheduling
Key components: nodes, pods, deployments
• Batch Workloads in Kubernetes
Understanding batch processing
Comparison of batch vs. real-time processing
Use cases in AI and big data
• Advanced Scheduling Features
Overview of Kubernetes scheduler
Scheduling policies and constraints
Resource management and allocation
• Fair-Share Scheduling
Introduction to fair-share scheduling concepts
Importance in shared compute environments
Implementing fair-share scheduling in Kubernetes
• The Volcano Project
Introduction to Volcano and its purpose
Key features and benefits for batch workloads
Integration with Kubernetes
• AI Workloads on Kubernetes
Running TensorFlow on Kubernetes
Distributed training strategies
Resource allocation and scaling for AI applications
• Big Data Workloads on Kubernetes
Running Apache Spark on Kubernetes
Configuring Spark clusters
Managing data pipelines effectively
• Case Study: Real-world Implementation
Examples of AI and big data workloads
Evaluation of fair-share scheduling impact
Success stories using Volcano and Kubernetes
• Best Practices and Future Trends
Best practices for managing batch workloads
Emerging trends in AI and big data on Kubernetes
Preparing for future capabilities in Kubernetes scheduling
• Conclusion and Resources
Recap of key concepts
Further reading and resources for deep dives
Community support and collaboration opportunities

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Subjects

Conference Talks