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 Duration 21 hours

Course Outline

Introduction to GPU-Accelerated Containerization

  • Comprehending GPU utilization in deep learning workflows
  • The role of Docker in supporting GPU-based workloads
  • Essential performance considerations

Installing and Configuring the NVIDIA Container Toolkit

  • Establishing drivers and CUDA compatibility
  • Verifying GPU access within containers
  • Setting up the runtime environment

Building GPU-Enabled Docker Images

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

Executing GPU-Accelerated AI Workloads

  • Running training jobs using GPU resources
  • Overseeing multi-GPU workloads
  • Tracking GPU utilization

Optimizing Performance and Resource Allocation

  • Constraining and isolating GPU resources
  • Refining memory usage, batch sizes, and device placement
  • Performance tuning and diagnostic techniques

Containerized Inference and Model Serving

  • Creating containers optimized for inference
  • Handling high-load workloads on GPUs
  • Integrating model runners and APIs

Scaling GPU Workloads with Docker

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

Security and Reliability for GPU-Enabled Containers

  • Ensuring secure GPU access in shared environments
  • Hardening container images
  • Managing updates, versions, and compatibility

Conclusion and Recommended Next Steps

Requirements

  • Foundational understanding of deep learning principles
  • Practical experience with Python and standard AI frameworks
  • Basic familiarity with containerization concepts

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

2026-11-17 10:00
21 hours
Seoul Center 1
₩ 7500000 (Online)
₩ 15000000 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-01 10:00
21 hours
Jungang-Dong Center
₩ 7500000 (Online)
₩ 15000000 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-15 10:00
21 hours
Seoul - Gran Seoul
₩ 7500000 (Online)
₩ 15000000 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-29 10:00
21 hours
Seoul Center 1
₩ 7500000 (Online)
₩ 15000000 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-12 10:00
21 hours
Jungang-Dong Center
₩ 7500000 (Online)
₩ 15000000 (Classroom)

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