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Duration 21 hours
Course Outline
Introduction to GPU-Accelerated Containerisation
- Understanding GPU usage within deep learning workflows
- How Docker supports GPU-based workloads
- Key performance considerations
Installing and Configuring the NVIDIA Container Toolkit
- Setting up drivers and ensuring CUDA compatibility
- Validating GPU access inside containers
- Configuring the runtime environment
Building GPU-Enabled Docker Images
- Utilising CUDA base images
- Packaging AI frameworks into GPU-ready containers
- Managing dependencies for training and inference
Running GPU-Accelerated AI Workloads
- Executing training jobs using GPUs
- Managing multi-GPU workloads
- Monitoring GPU utilisation
Optimising Performance and Resource Allocation
- Limiting and isolating GPU resources
- Optimising memory, batch sizes, and device placement
- Performance tuning and diagnostics
Containerised Inference and Model Serving
- Building inference-ready containers
- Serving 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 safe GPU access in shared environments
- Hardening container images
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- A solid understanding of deep learning fundamentals
- Practical experience with Python and common AI frameworks
- Basic familiarity with containerisation concepts
Audience
- Deep learning engineers
- Research and development teams
- AI model trainers
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.
