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Duration 21 hours
Course Outline
Introduction to GPU-Accelerated Containerization
- The role of GPU usage in deep learning workflows
- How Docker facilitates GPU-based workloads
- Essential performance considerations
Installing and Configuring NVIDIA Container Toolkit
- Establishing drivers and ensuring CUDA compatibility
- Verifying GPU access within containers
- Setting up the runtime environment
Building GPU-Enabled Docker Images
- Leveraging CUDA base images
- Packaging AI frameworks into GPU-ready containers
- Handling dependencies for training and inference
Running GPU-Accelerated AI Workloads
- Executing training jobs via GPUs
- Overseeing multi-GPU workloads
- Tracking GPU utilization metrics
Optimizing Performance and Resource Allocation
- Controlling and isolating GPU resources
- Refining memory usage, batch sizes, and device placement
- Performance tuning and diagnostic techniques
Containerized Inference and Model Serving
- Creating 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
- Safeguarding GPU access in shared environments
- Hardening container images
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- A solid grasp of deep learning fundamentals
- Practical experience with Python and mainstream AI frameworks
- Basic knowledge of containerization principles
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.