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 Duration 21 hours

Course Outline

Foundations of GPU-Accelerated Containerization

  • Grasping GPU roles in deep learning processes
  • The role of Docker in supporting GPU-based tasks
  • Essential performance factors to consider

Installation and Setup of the NVIDIA Container Toolkit

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

Constructing GPU-Ready Docker Images

  • Leveraging CUDA-based foundational images
  • Encapsulating AI frameworks into GPU-capable containers
  • Handling dependencies for both training and inference

Executing GPU-Accelerated AI Tasks

  • Running training jobs with GPU support
  • Overseeing workloads across multiple GPUs
  • Tracking GPU usage metrics

Refining Performance and Resource Management

  • Setting limits and isolating GPU resources
  • Tuning memory usage, batch sizes, and device assignment
  • Conducting performance adjustments and diagnostic checks

Inference and Model Delivery via Containers

  • Creating containers optimized for inference
  • Handling high-volume workloads on GPU hardware
  • Connecting model execution engines and APIs

Expanding GPU Workloads with Docker

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

Security and Stability for GPU-Based Containers

  • Guaranteeing secure GPU access in shared setups
  • Strengthening the security of container images
  • Overseeing updates, version control, and compatibility

Review and Future Directions

Requirements

  • A solid grasp of deep learning principles
  • Proficiency with Python and widely used AI frameworks
  • Basic knowledge of containerization concepts

Intended Audience

  • Deep learning engineers
  • Research and development teams
  • 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

2026-11-09 09:30
21 hours
João Pessoa - Hub Plural
$ 8730 (Online)
$ 10230 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-11-23 09:30
21 hours
Natal - Hub Plural
$ 8730 (Online)
$ 10230 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-07 09:30
21 hours
Manaus - Adrianòpolis
$ 8730 (Online)
$ 10230 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-21 09:30
21 hours
Rio de Janeiro - Candelaria
$ 8730 (Online)
$ 10230 (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-04 09:30
21 hours
Rio de Janeiro-Lagoa Rodrigo de Freitas Humaita
$ 8730 (Online)
$ 10230 (Classroom)

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