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 Duration 21 hours

Course Outline

Introduction to GPU-Accelerated Containerization

  • The role of GPUs in deep learning workflows
  • How Docker facilitates 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
  • Adjusting the runtime environment

Creating GPU-Enabled Docker Images

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

Executing GPU-Accelerated AI Workloads

  • Running training jobs using GPU resources
  • Overseeing multi-GPU workload distribution
  • Tracking GPU utilization metrics

Performance Optimization and Resource Management

  • Constraining and isolating specific GPU resources
  • Tuning memory usage, batch sizes, and device placement
  • Conducting performance tuning and diagnostics

Containerized Inference and Model Serving

  • Assembling inference-ready containers
  • Serving high-volume workloads on GPUs
  • Integrating model runners with APIs

Scaling GPU Workloads via Docker

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

Enhancing Security and Reliability for GPU Containers

  • Safeguarding GPU access in shared environments
  • Strengthening container image security
  • Handling updates, versioning, and compatibility

Summary and Recommended Next Steps

Requirements

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

Target Audience

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

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

2026-11-06 09:30
21 hours
Brno
4500 EUR (Online)
4500 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-11-20 09:30
21 hours
Ostrava
4500 EUR (Online)
4500 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-04 09:30
21 hours
Prague, Prague Castle
4500 EUR (Online)
5100 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-18 09:30
21 hours
Brno
4500 EUR (Online)
4500 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-01 09:30
21 hours
Ostrava
4500 EUR (Online)
4500 EUR (Classroom)

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