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Course Outline

Introduction to GPU-Accelerated Containerization

  • Understanding how GPUs are used in deep learning workflows
  • How Docker facilitates GPU-based workloads
  • Key factors affecting performance

Installing and Configuring NVIDIA Container Toolkit

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

Building Docker Images with GPU Support

  • Utilizing CUDA base images
  • Packaging AI frameworks into containers ready for GPUs
  • Managing dependencies required for training and inference

Running AI Workloads Accelerated by GPUs

  • Executing training jobs using GPU power
  • Handling workloads across multiple GPUs
  • Monitoring GPU usage and performance

Optimizing Performance and Resource Allocation

  • Restricting and isolating GPU resources
  • Tuning memory, batch sizes, and device placement
  • Performance tuning and diagnostics

Inference and Model Serving in Containers

  • Creating containers prepared for inference
  • Supporting high-volume workloads on GPUs
  • Integrating model execution engines and APIs

Scaling GPU Workloads with Docker

  • Strategies for distributed GPU training
  • Scaling up inference microservices
  • Coordinating AI systems across multiple containers

Security and Reliability for Containers with GPU Support

  • Ensuring safe GPU access in shared settings
  • Strengthening container images
  • Managing updates, versions, and compatibility

Summary and Next Steps

Requirements

  • Knowledge of deep learning basics
  • Experience using Python and popular AI frameworks
  • Understanding of fundamental containerization concepts

Target Audience

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

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