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Course Outline

Foundations of GPU-Accelerated Containerization

  • The role of GPUs in deep learning workflows
  • The function of Docker in supporting GPU-based workloads
  • Critical performance factors to consider

Setup and Configuration of the NVIDIA Container Toolkit

  • Establishing driver and CUDA compatibility
  • Verifying GPU access within containers
  • Tailoring the runtime environment

Creating GPU-Ready Docker Images

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

Executing GPU-Accelerated AI Processes

  • Running training jobs on GPUs
  • Overseeing multi-GPU workloads
  • Tracking GPU utilization

Enhancing Performance and Resource Management

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

Containerized Inference and Model Serving

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

Scaling GPU Workloads with Docker

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

Security and Reliability for GPU-Enabled Containers

  • Securing GPU access in shared environments
  • Strengthening container image security
  • Managing updates, versioning, and compatibility

Wrap-up and Future Directions

Requirements

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

Target Audience

  • Deep learning engineers
  • R&D teams
  • AI model trainers
 21 Hours

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