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Course Outline

Foundations of GPU-Accelerated Containerization

  • The role of GPUs in deep learning pipelines
  • Docker’s support for GPU-based workloads
  • Essential performance factors to consider

Installation and Setup of the NVIDIA Container Toolkit

  • Installing drivers and ensuring CUDA compatibility
  • Verifying GPU access within container environments
  • Setting up the required runtime environment

Creating GPU-Ready Docker Images

  • Utilizing CUDA-based images
  • Packaging AI frameworks into GPU-capable containers
  • Handling dependencies for training and inference phases

Executing GPU-Accelerated AI Tasks

  • Running training jobs using GPU resources
  • Handling workloads across multiple GPUs
  • Tracking and monitoring GPU usage

Performance Optimization and Resource Management

  • Restricting and isolating GPU resources effectively
  • Tuning memory usage, batch sizes, and device placement
  • Conducting performance tuning and diagnostics

Containerized Inference and Model Serving

  • Developing containers optimized for inference
  • Handling high-volume workloads on GPUs
  • Integrating model runners and APIs

Scaling GPU Operations with Docker

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

Security and Reliability in GPU-Enabled Containers

  • Safeguarding GPU access in shared environments
  • Strengthening container image security
  • Overseeing updates, version control, and compatibility

Recap and Future Directions

Requirements

  • Foundation in deep learning principles
  • Practical experience with Python and popular AI frameworks
  • Basic knowledge of containerization concepts

Target Audience

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

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