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 Duration 21 hours

Course Outline

Introduction to GPU-Accelerated Containerization

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

Installing and Configuring NVIDIA Container Toolkit

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

Building GPU-Enabled Docker Images

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

Running GPU-Accelerated AI Workloads

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

Optimizing Performance and Resource Allocation

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

Containerized Inference and Model Serving

  • Creating 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
  • Coordinating multi-container AI systems

Security and Reliability for GPU-Enabled Containers

  • Safeguarding 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 mainstream AI frameworks
  • Basic knowledge of containerization principles

Audience

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

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