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 Duration 21 hours

Course Outline

Introduction to GPU-Accelerated Containerization

  • The role of GPUs in deep learning pipelines
  • Docker’s support for GPU-based workloads
  • Essential performance factors to consider

Installation and Configuration of the NVIDIA Container Toolkit

  • Installing drivers and ensuring CUDA compatibility
  • Verifying GPU accessibility within containers
  • Configuring the necessary runtime environment

Creating GPU-Enabled Docker Images

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

Executing GPU-Accelerated AI Workloads

  • Running training jobs leveraging GPU power
  • Handling multi-GPU workloads
  • Tracking GPU utilization metrics

Performance Optimization and Resource Management

  • Controlling and isolating GPU resources
  • Optimizing memory usage, batch sizes, and device placement
  • Conducting performance tuning and diagnostics

Containerized Inference and Model Serving

  • Constructing containers ready for inference
  • Handling high-load GPU workloads
  • Integrating model runners and API interfaces

Scaling GPU Workloads with Docker

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

Security and Reliability for GPU-Enabled Containers

  • Securing GPU access in shared environments
  • Hardening container images for security
  • Managing updates, versioning, and compatibility

Summary and Next Steps

Requirements

  • Working knowledge of deep learning fundamentals
  • Practical experience with Python and standard AI frameworks
  • Basic understanding of containerization principles

Target 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

2026-11-23 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-07 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-21 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-04 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

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