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 Duration 21 hours

Course Outline

Foundations of GPU-Accelerated Containerization

  • Grasping the role of GPUs in deep learning pipelines
  • The way Docker facilitates GPU-centric tasks
  • Essential performance factors to consider

Setup and Configuration of the NVIDIA Container Toolkit

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

Creating Docker Images with GPU Support

  • Utilizing CUDA-based foundational images
  • Encapsulating AI frameworks in containers ready for GPU use
  • Handling dependencies for training and inference tasks

Executing AI Workloads with GPU Acceleration

  • Running training processes using GPUs
  • Handling tasks across multiple GPUs
  • Tracking GPU usage and performance

Enhancing Performance and Managing Resources

  • Controlling and separating GPU resources
  • Optimizing memory usage, batch sizing, and device assignment
  • Performance adjustments and troubleshooting

Inference and Model Delivery via Containers

  • Developing containers prepared for inference
  • Handling high-demand workloads on GPUs
  • Connecting model execution engines and APIs

Expanding GPU Workloads with Docker

  • Approaches for distributed GPU training
  • Scaling inference microservices
  • Orchestrating AI systems with multiple containers

Safety and Reliability for GPU-Enabled Containers

  • Guaranteeing secure GPU access in shared setups
  • Strengthening container security
  • Handling updates, version control, and compatibility issues

Overview and Future Steps

Requirements

  • A solid grasp of deep learning basics
  • Practical experience with Python and popular AI frameworks
  • Knowledge of fundamental containerization principles

Target Audience

  • Deep learning engineers
  • Research and development groups
  • Professionals specializing in 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
Puebla - Triangulo Las Animas
109,890 MXN (Online)
139,890 MXN (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-11-24 09:30
21 hours
Puebla - Fratta Center
109,890 MXN (Online)
139,890 MXN (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-08 09:30
21 hours
Mexico City - Torre Concreta
109,890 MXN (Online)
139,890 MXN (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2026-12-22 09:30
21 hours
Mexico City - Samara Shops Tower A
109,890 MXN (Online)
139,890 MXN (Classroom)

GPU-Accelerated AI & Deep Learning with Docker Containers

2027-01-05 09:30
21 hours
Mexico City - Spaces Santa Fé
109,890 MXN (Online)
139,890 MXN (Classroom)

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