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Course Outline

Introduction to GPU-Accelerated Containerization

  • Examining the role of GPUs in deep learning processes
  • How Docker facilitates GPU-based workloads
  • Essential performance factors to consider

Setup and Configuration of the NVIDIA Container Toolkit

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

Creating GPU-Enabled Docker Images

  • Leveraging CUDA base images
  • Encapsulating AI frameworks in GPU-ready containers
  • Managing dependencies for training and inference tasks

Executing GPU-Accelerated AI Workloads

  • Running training jobs leveraging GPUs
  • Handling multi-GPU workloads
  • Tracking GPU utilization

Performance Optimization and Resource Allocation

  • Restricting and isolating GPU resources
  • Refining memory, batch sizes, and device placement
  • Tuning performance and running diagnostics

Containerized Inference and Model Serving

  • Developing inference-ready containers
  • Serving high-traffic workloads on GPUs
  • Integrating model runners and APIs

Scaling GPU Workloads with Docker

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

Security and Reliability for GPU-Enabled Containers

  • Safeguarding GPU access in shared environments
  • Strengthening container images
  • Managing updates, versions, and compatibility

Summary and Future Steps

Requirements

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

Target Audience

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

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