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Course Outline

Overview of GPU-Accelerated Containerization

  • The role of GPUs in deep learning workflows
  • The way Docker facilitates GPU-based tasks
  • Essential performance factors to consider

Installation and Setup of the NVIDIA Container Toolkit

  • Installing drivers and ensuring CUDA compatibility
  • Verifying GPU access within containers
  • Configuring the runtime environment

Creating GPU-Capable Docker Images

  • Leveraging CUDA base images
  • Packaging AI frameworks into GPU-ready containers
  • Handling dependencies for training and inference

Executing GPU-Accelerated AI Tasks

  • Running training jobs on GPUs
  • Handling multi-GPU workloads
  • Tracking GPU utilization

Performance Optimization and Resource Management

  • Controlling and isolating GPU resources
  • Refining memory usage, batch sizes, and device assignment
  • Tuning performance and conducting diagnostics

Containerized Inference and Model Deployment

  • Constructing containers ready for inference
  • Serving high-demand workloads on GPUs
  • Integrating model runners and APIs

Scaling GPU Workloads via Docker

  • Approaches for distributed GPU training
  • Scaling inference microservices
  • Managing multi-container AI systems

Security and Reliability for GPU-Enabled Containers

  • Safeguarding GPU access in shared environments
  • Enhancing the security of container images
  • Overseeing updates, versions, and compatibility

Conclusion and Future Directions

Requirements

  • A solid grasp of deep learning fundamentals
  • Proficiency with Python and popular AI frameworks
  • Basic knowledge of containerization principles

Target Audience

  • Deep learning engineers
  • Research and development teams
  • AI model trainers
 21 Hours

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