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Course Outline
Introduction
- Overview of Challenges in Scaling Deep Learning Models
- Overview of DeepSpeed and Its Key Features
- Comparison of DeepSpeed with Other Distributed Deep Learning Libraries
Getting Started
- Setting Up the Development Environment for Government Use
- Installing PyTorch and DeepSpeed in a Secure Environment
- Configuring DeepSpeed for Distributed Training in Government Systems
DeepSpeed Optimization Features
- The DeepSpeed Training Pipeline for Enhanced Efficiency
- ZeRO (Memory Optimization) for Efficient Resource Utilization
- Activation Checkpointing to Reduce Memory Consumption
- Gradient Checkpointing for Improved Training Performance
- Pipeline Parallelism to Accelerate Model Training
Scaling Models with DeepSpeed
- Basic Scaling Techniques Using DeepSpeed for Government Projects
- Advanced Scaling Methods and Best Practices for Large-Scale Deployments
- Performance Considerations and Optimization Strategies for Government Applications
- Debugging and Troubleshooting Techniques for DeepSpeed in a Government Context
Advanced DeepSpeed Topics
- Advanced Optimization Techniques for Enhanced Performance in Government Models
- Using DeepSpeed with Mixed Precision Training to Improve Efficiency
- Deploying DeepSpeed on Different Hardware Platforms (e.g., GPUs, TPUs) for Government Use
- Implementing DeepSpeed with Multiple Training Nodes for Large-Scale Government Projects
Integrating DeepSpeed with PyTorch
- Seamlessly Integrating DeepSpeed into Existing PyTorch Workflows for Government Applications
- Leveraging DeepSpeed with PyTorch Lightning for Simplified Development and Deployment in Government Systems
Troubleshooting
- Debugging Common Issues Encountered While Using DeepSpeed in Government Projects
- Monitoring and Logging Techniques to Ensure Reliable Operation of DeepSpeed in a Government Setting
Summary and Next Steps
- Recap of Key Concepts and Features for Effective Use of DeepSpeed in Government Operations
- Best Practices for Implementing DeepSpeed in Production Environments for Government
- Further Resources for Continued Learning and Expertise Development in DeepSpeed for Government Applications
Requirements
- Intermediate understanding of deep learning principles
- Experience with PyTorch or other comparable deep learning frameworks
- Familiarity with Python programming
Audience for government
- Data scientists
- Machine learning engineers
- Developers
21 Hours
Testimonials (2)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
