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Course Outline

Introduction to GPU-Accelerated Containerization

  • The role of GPUs in deep learning workflows
  • How Docker facilitates GPU-based workloads
  • Essential performance considerations

Installation and Configuration of the NVIDIA Container Toolkit

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

Creating GPU-Enabled Docker Images

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

Executing GPU-Accelerated AI Workloads

  • Running training jobs via GPUs
  • Overseeing multi-GPU workloads
  • Tracking GPU utilization

Performance Optimization and Resource Allocation

  • Restricting and isolating GPU resources
  • Refining memory, batch sizes, and device placement
  • Performance tuning and diagnostic techniques

Containerized Inference and Model Serving

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

Scaling GPU Workloads with Docker

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

Security and Reliability for GPU-Enabled Containers

  • Ensuring secure GPU access in shared environments
  • Hardening container images
  • Managing updates, versions, and compatibility

Summary and Next Steps

Requirements

  • A solid grasp of deep learning fundamentals
  • Practical experience with Python and standard AI frameworks
  • Basic familiarity with containerization concepts

Target Audience

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

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