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Course Outline

Introduction to GPU-Accelerated Containerization

  • Comprehending the role of GPUs in deep learning workflows
  • How Docker facilitates GPU-based workloads
  • Key factors impacting performance

Installing and Configuring NVIDIA Container Toolkit

  • Establishing drivers and ensuring CUDA compatibility
  • Verifying GPU accessibility within containers
  • Configuring the runtime environment

Building GPU-Enabled Docker Images

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

Running GPU-Accelerated AI Workloads

  • Executing training jobs utilizing GPUs
  • Managing workloads across multiple GPUs
  • Monitoring GPU utilization metrics

Optimizing Performance and Resource Allocation

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

Containerized Inference and Model Serving

  • Developing inference-ready containers
  • Handling 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 for security
  • Managing updates, versions, and compatibility issues

Summary and Next Steps

Requirements

  • A foundational understanding of deep learning concepts
  • Practical experience with Python and popular 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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