Flare & Lantern - Accelerators for Spark and Deep Learning

Overview

Explore accelerators for Spark and deep learning with Flare & Lantern, enhancing performance and efficiency in data processing and machine learning tasks.

Syllabus

• Introduction to Spark and Deep Learning
Overview of Apache Spark
Introduction to Deep Learning Concepts
Importance of Accelerators in Data Processing and ML
• Introduction to Flare & Lantern
What is Flare?
What is Lantern?
The Role of Flare & Lantern in enhancing Spark and Deep Learning workloads
• Setting Up the Environment
Installing and Configuring Apache Spark
Setting up Flare and Lantern
Preparing the Development Environment for Experimentation
• Understanding Accelerators
Types of Accelerators: GPUs, TPUs, and Specialized Hardware
Comparison of Accelerator Technologies
Benefits of Using Accelerators with Spark and Deep Learning
• Integration of Flare with Spark
Architecture and Integration Process
Enhancing Spark Performance with Flare
Use Cases and Applications
• Leveraging Lantern for Deep Learning
Architecture and Integration with Popular DL Frameworks
Case Studies: Lantern-Enhanced Deep Learning Models
Hands-On: Implementing a Model with Lantern
• Optimization Techniques
Data Pipeline Optimization
Model Training Efficiency
Resource Management and Cost Efficiency
• Performance Evaluation
Benchmarking Accelerator Impact
Tools and Metrics for Performance Analysis
Case Studies: Comparing Traditional vs. Accelerator-Enhanced Workloads
• Troubleshooting and Best Practices
Common Challenges in Using Flare & Lantern
Best Practices for Implementation and Optimization
Future Trends in Accelerators for Spark and Deep Learning
• Capstone Project
Designing a Real-World Project Using Flare & Lantern
Implementation and Presentation
Peer Review and Feedback
• Course Wrap-up
Key Takeaways
Additional Resources and Further Reading
Q&A and Feedback Session

Subjects

Conference Talks