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Duration 21 hours
Course Outline
Foundations of GPU-Accelerated Containerization
- The role of GPUs in deep learning pipelines
- The way Docker facilitates GPU-based tasks
- Essential performance factors to consider
Installation and Setup of the NVIDIA Container Toolkit
- Installing drivers and establishing CUDA compatibility
- Verifying GPU accessibility within containers
- Tuning the runtime environment
Creating Docker Images with GPU Support
- Utilizing CUDA base images
- Encapsulating AI frameworks into GPU-ready containers
- Handling dependencies for training and inference
Executing GPU-Accelerated AI Tasks
- Running training jobs on GPUs
- Oversight of multi-GPU operations
- Tracking GPU usage metrics
Enhancing Performance and Resource Management
- Restricting and segregating GPU resources
- Tuning memory, batch sizes, and device positioning
- Performance optimization and troubleshooting
Containerized Inference and Model Hosting
- Developing containers ready for inference
- Handling high-demand workloads on GPUs
- Integrating model runners and APIs
Expanding GPU Workloads with Docker
- Approaches for distributed GPU training
- Scaling inference microservices
- Coordinating multi-container AI ecosystems
Security and Stability for GPU-Enabled Containers
- Securing GPU access in shared environments
- Strengthening container image security
- Managing updates, versions, and compatibility
Recap and Future Directions
Requirements
- A solid grasp of deep learning core concepts
- Proficiency with Python and prevalent AI frameworks
- Basic knowledge of containerization principles
Target 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.