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 Duration 21 hours

Course Outline

Foundations of GPU-Accelerated Containerization

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

Installation and Setup of the NVIDIA Container Toolkit

  • Installing drivers and establishing CUDA compatibility
  • Verifying GPU accessibility within containers
  • Tuning the runtime environment

Creating Docker Images with GPU Support

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

Executing GPU-Accelerated AI Tasks

  • Running training jobs on GPUs
  • Oversight of multi-GPU operations
  • Tracking GPU usage metrics

Enhancing Performance and Resource Management

  • Restricting and segregating GPU resources
  • Tuning memory, batch sizes, and device positioning
  • Performance optimization and troubleshooting

Containerized Inference and Model Hosting

  • Developing containers ready for inference
  • Handling high-demand workloads on GPUs
  • Integrating model runners and APIs

Expanding GPU Workloads with Docker

  • Approaches for distributed GPU training
  • Scaling inference microservices
  • Coordinating multi-container AI ecosystems

Security and Stability for GPU-Enabled Containers

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

Recap and Future Directions

Requirements

  • A solid grasp of deep learning core concepts
  • Proficiency with Python and prevalent AI frameworks
  • Basic knowledge of containerization principles

Target Audience

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

Testimonials (2)

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