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 Duration 21 hours

Course Outline

Introduction to GPU-Accelerated Containerization

  • Exploring GPU roles in deep learning pipelines
  • The role of Docker in supporting GPU-based workloads
  • Essential performance metrics and considerations

Installing and Configuring the NVIDIA Container Toolkit

  • Configuring drivers and ensuring CUDA compatibility
  • Verifying GPU accessibility within containers
  • Setting up the runtime environment for GPU tasks

Creating GPU-Enabled Docker Images

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

Executing GPU-Accelerated AI Workloads

  • Running training jobs utilizing GPU resources
  • Managing workloads that span multiple GPUs
  • Tracking and monitoring GPU utilization levels

Optimizing Performance and Resource Management

  • Enforcing limits and isolating GPU resources
  • Tuning memory usage, batch sizes, and device placement
  • Conducting performance tuning and diagnostic analysis

Containerized Inference and Model Serving

  • Developing containers optimized for inference
  • Handling high-load workload serving on GPUs
  • Integrating model runners and API interfaces

Scaling GPU Workloads with Docker

  • Implementing strategies for distributed GPU training
  • Scaling inference microservices for growth
  • Coordinating complex, multi-container AI systems

Security and Reliability for GPU-Enabled Containers

  • Securing GPU access in shared computing environments
  • Hardening container images against vulnerabilities
  • Managing updates, version control, and compatibility

Summary and Future Directions

Requirements

  • A solid grasp of deep learning fundamentals
  • Proficiency with Python and popular AI frameworks
  • Basic knowledge of containerization concepts

Audience

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

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Testimonials (2)

As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .

Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes

multi-tiered, structured course programme.

Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker

Upcoming Courses

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-11-17 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-01 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-15 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-29 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-12 09:30
21 hours
Green Heart Complex
506 EUR (Online)
506 EUR (Classroom)

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