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 Duration 21 hours

Course Outline

Fundamentals of GPU-Accelerated Containerization

  • The role of GPUs in deep learning processes
  • The mechanism by which Docker facilitates GPU-based tasks
  • Essential factors affecting performance

Setup and Configuration of the NVIDIA Container Toolkit

  • Establishing drivers and ensuring CUDA compatibility
  • Confirming GPU accessibility within container environments
  • Defining the runtime settings

Creating GPU-Ready Docker Images

  • Leveraging CUDA-based foundational images
  • Bundling AI frameworks into containers prepared for GPU use
  • Handling dependencies for both training and inference phases

Executing GPU-Accelerated AI Tasks

  • Carrying out training operations utilizing GPU resources
  • Overseeing workloads that span multiple GPUs
  • Tracking GPU usage metrics

Enhancing Performance and Resource Management

  • Controlling and separating GPU resource usage
  • Tuning memory usage, batch dimensions, and device positioning
  • Performance optimization and troubleshooting

In-Container Inference and Model Deployment

  • Developing containers optimized for inference
  • Handling high-volume requests on GPU hardware
  • Incorporating model execution engines and API interfaces

Expanding GPU Workloads via Docker

  • Tactics for distributed training across GPUs
  • Scaling inference microservices
  • Orchestrating complex, multi-container AI architectures

Security and Stability for GPU-Powered Containers

  • Safeguarding GPU access in shared infrastructure
  • Strengthening the security posture of container images
  • Managing software updates, versioning, and compatibility

Concluding Remarks and Future Directions

Requirements

  • A solid grasp of deep learning core concepts
  • Practical experience with Python and standard AI frameworks
  • Basic knowledge of containerization principles

Target Learners

  • Deep learning engineers
  • Research and development groups
  • Professionals responsible for AI model training

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

2026-11-30 09:30
21 hours
Oslo
4500 EUR (Online)
5100 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-14 09:30
21 hours
Oslo
4500 EUR (Online)
5100 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-28 09:30
21 hours
Oslo
4500 EUR (Online)
5100 EUR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-11 09:30
21 hours
Oslo
4500 EUR (Online)
5100 EUR (Classroom)

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