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Course Outline

Introduction to GPU-Accelerated Containerization

  • The role of GPUs in deep learning workflows
  • Supporting GPU-based workloads with Docker
  • Essential performance considerations

Installation and Configuration of the NVIDIA Container Toolkit

  • Establishing driver and 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
  • Handling dependencies for training and inference

Executing GPU-Accelerated AI Workloads

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

Performance and Resource Allocation Optimization

  • Restricting and isolating GPU resources
  • Tuning memory, batch sizes, and device placement
  • Performance refinement and diagnostic analysis

Containerized Inference and Model Serving

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

Scaling GPU Workloads with Docker

  • Strategies 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
  • Enhancing the security posture of container images
  • Managing updates, version control, and compatibility

Summary and Next Steps

Requirements

  • A solid grasp of deep learning principles
  • Proficiency with Python and standard AI frameworks
  • Knowledge of fundamental containerization concepts

Target Audience

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

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