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

Overview

Join us in exploring the integration of batch capabilities into Kubernetes with a focus on AI and big data applications. This event dives deep into advanced scheduling features tailored for batch workloads, utilizing powerful frameworks such as TensorFlow and Spark.

Participants will gain valuable insights into fair-share scheduling methods and the Volcano project, designed to enhance workload efficiency.

Ideal for professionals and enthusiasts aiming to optimize their Kubernetes environments for high-demand applications.

Hosted by:

University

Provided by:

YouTube

Categories:

Artificial Intelligence Courses, Conference Talks

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

---

Subjects

Conference Talks