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 Duration 21 hours

Course Outline

Overview of GPU-Accelerated Containerization

  • Examining GPU utilization within deep learning pipelines
  • The role of Docker in supporting GPU-based workloads
  • Essential performance metrics and considerations

Installation and Configuration of the NVIDIA Container Toolkit

  • Establishing drivers and ensuring CUDA compatibility
  • Verifying GPU access within container environments
  • Setting up the necessary runtime environment

Creating GPU-Enabled Docker Images

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

Executing GPU-Accelerated AI Workloads

  • Running training jobs utilizing GPU resources
  • Oversight of multi-GPU operations
  • Tracking and monitoring GPU usage

Enhancing Performance and Resource Management

  • Restricting and isolating GPU resources effectively
  • Refining memory usage, batch sizes, and device placement
  • Conducting performance tuning and diagnostic analysis

Containerized Inference and Model Serving

  • Constructing containers optimized for inference
  • Handling high-volume workloads on GPUs
  • Integrating model runners and API interfaces

Scaling GPU Workloads with Docker

  • Strategies for distributed GPU training setups
  • Expanding the capacity of inference microservices
  • Orchestrating multi-container AI systems

Security and Reliability for GPU-Enabled Containers

  • Securing GPU access in shared computing environments
  • Strengthening the security posture of container images
  • Oversight of updates, version control, and compatibility

Conclusion and Future Directions

Requirements

  • Foundational knowledge of deep learning principles
  • Proficiency in Python and widely used AI frameworks
  • Basic understanding of containerization concepts

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

2026-11-23 09:30
21 hours
Kuala Lumpur KL Sentral
21661 MYR (Online)
22636 MYR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-07 09:30
21 hours
Bayan Lepas, iDEAL
21661 MYR (Online)
22396 MYR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-21 09:30
21 hours
Kuala Lumpur KL Sentral
21661 MYR (Online)
22636 MYR (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-04 09:30
21 hours
Bayan Lepas, iDEAL
21661 MYR (Online)
22396 MYR (Classroom)

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