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Course Outline

Foundations of GPU-Accelerated Containerization

  • The role of GPUs in deep learning pipelines
  • The way Docker facilitates GPU-based workloads
  • Essential performance factors to consider

Setup and Configuration of the NVIDIA Container Toolkit

  • Establishing drivers and ensuring CUDA compatibility
  • Confirming GPU accessibility within containers
  • Tuning the runtime environment

Creating GPU-Ready Docker Images

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

Executing GPU-Accelerated AI Tasks

  • Running training processes on GPUs
  • Overseeing multi-GPU operations
  • Tracking GPU usage levels

Enhancing Performance and Resource Management

  • Restricting and isolating GPU resources
  • Improving memory usage, batch sizes, and device placement
  • Tuning performance and troubleshooting

Containerized Inference and Model Serving

  • Constructing containers optimized for inference
  • Handling high-demand workloads on GPUs
  • Connecting model runners and APIs

Scaling GPU Workloads via Docker

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

Security and Stability for GPU-Enabled Containers

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

Recap and Future Directions

Requirements

  • A solid grasp of deep learning basics
  • Practical experience with Python and standard AI frameworks
  • Knowledge of fundamental containerization principles

Target Audience

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

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