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Course Outline

Foundations of GPU-Accelerated Containerization

  • Exploring the role of GPUs in deep learning pipelines
  • The function of Docker in supporting 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
  • Adjusting the runtime environment settings

Creating GPU-Enabled Docker Images

  • Leveraging CUDA base images
  • Packaging AI frameworks into GPU-ready containers
  • Handling dependencies for training and inference tasks

Executing GPU-Accelerated AI Workloads

  • Running training jobs utilizing GPU resources
  • Handling workloads across multiple GPUs
  • Tracking and monitoring GPU usage

Performance Optimization and Resource Management

  • Controlling and isolating GPU resources
  • Tuning memory usage, batch sizes, and device placement
  • Conducting performance tuning and diagnostic checks

Containerized Inference and Model Deployment

  • Constructing containers optimized for inference
  • Managing high-load inference workloads on GPUs
  • Connecting model runners with API integrations

Scaling GPU Workloads via Docker

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

Security and Reliability in GPU-Enabled Containers

  • Securing GPU access in shared computing environments
  • Strengthening the security posture of container images
  • Oversight of 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
  • Research and development teams
  • AI model trainers
 21 Hours

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