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Course Outline

Introduction to GPU-Accelerated Containerization

  • Understanding the role of GPUs in deep learning workflows
  • How Docker facilitates GPU-based workloads
  • Key performance factors to consider

Installing and Configuring the NVIDIA Container Toolkit

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

Constructing GPU-Enabled Docker Images

  • Utilizing CUDA base images
  • Packaging AI frameworks into GPU-ready containers
  • Managing dependencies for training and inference tasks

Executing GPU-Accelerated AI Workloads

  • Running training jobs utilizing GPUs
  • Managing workloads across multiple GPUs
  • Monitoring GPU utilization rates

Optimizing Performance and Resource Allocation

  • Limiting and isolating GPU resources
  • Optimizing memory usage, batch sizes, and device placement
  • Performance tuning and diagnostics

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

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

Summary and Next Steps

Requirements

  • A foundational understanding of deep learning concepts
  • Experience using Python and common AI frameworks
  • Familiarity with basic containerization principles

Target Audience

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

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