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 Duration 21 hours

Course Outline

Introduction to GPU-Accelerated Containerization

  • The role of GPUs in deep learning pipelines
  • How Docker facilitates GPU-based operations
  • Essential performance factors to consider

Setup and Configuration of the NVIDIA Container Toolkit

  • Installing drivers and ensuring CUDA compatibility
  • Verifying GPU accessibility within containers
  • Adjusting the runtime environment settings

Creating GPU-Enabled Docker Images

  • Leveraging CUDA foundation images
  • Encapsulating AI frameworks into GPU-prepared containers
  • Handling dependencies for both training and inference stages

Executing GPU-Accelerated AI Tasks

  • Running training jobs leveraging GPU power
  • Oversight of workloads spanning multiple GPUs
  • Tracking GPU usage metrics

Enhancing Performance and Resource Distribution

  • Regulating and segregating GPU resources
  • Optimizing memory usage, batch dimensions, and device assignment
  • Conducting performance analysis and troubleshooting

Containerized Inference and Model Delivery

  • Constructing containers ready for inference
  • Handling high-volume workloads on GPU hardware
  • Connecting model execution engines and API interfaces

Scaling GPU Operations with Docker

  • Approaches for distributed GPU training
  • Expanding the capacity of inference microservices
  • Orchestrating multi-container AI architectures

Security and Stability for GPU-Enabled Containers

  • Safeguarding GPU access in shared settings
  • Strengthening the security posture of container images
  • Handling updates, version control, and compatibility issues

Summary and Future Directions

Requirements

  • A solid grasp of core deep learning principles
  • Practical experience using Python and standard AI frameworks
  • Comfort with fundamental containerization concepts

Target Audience

  • Deep learning specialists
  • Research and development units
  • Professionals focused on 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

2026-11-10 09:30
21 hours
Empire Tower
94820 THB (Online)
94820 THB (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-11-24 09:30
21 hours
Empire Tower
94820 THB (Online)
94820 THB (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-08 09:30
21 hours
Empire Tower
94820 THB (Online)
94820 THB (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-22 09:30
21 hours
Empire Tower
94820 THB (Online)
94820 THB (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-05 09:30
21 hours
Empire Tower
94820 THB (Online)
94820 THB (Classroom)

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