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Duration 21 hours
Course Outline
Introduction to GPU-Accelerated Containerization
- The role of GPUs in deep learning workflows
- How Docker facilitates GPU-based workloads
- Critical performance factors to consider
Installation and Configuration of the NVIDIA Container Toolkit
- Establishing driver setups and CUDA compatibility
- Verifying GPU access within containers
- Setting up the runtime environment
Creating GPU-Enabled Docker Images
- Leveraging CUDA base images
- Packaging AI frameworks into GPU-ready containers
- Handling dependencies for training and inference
Executing GPU-Accelerated AI Workloads
- Running training jobs with GPU support
- Managing multi-GPU operations
- Monitoring GPU usage metrics
Performance Optimization and Resource Management
- Restricting and isolating GPU resources
- Refining memory usage, batch sizes, and device placement
- Performance tuning and diagnostic procedures
Containerized Inference and Model Serving
- Constructing containers prepared for inference
- Serving high-demand workloads on GPUs
- Integrating model runners and APIs
Scaling GPU Workloads via Docker
- Approaches for distributed GPU training
- Expanding inference microservices
- Coordinating multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Ensuring secure GPU access in shared environments
- Strengthening the security of container images
- Overseeing updates, version control, and compatibility
Summary and Next Steps
Requirements
- A solid grasp of deep learning fundamentals
- Practical experience with Python and prevalent AI frameworks
- Basic familiarity with 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.
