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Duration 21 hours
Course Outline
Foundations of GPU-Accelerated Containerization
- The role of GPUs in deep learning pipelines
- How Docker facilitates GPU-based tasks
- Essential performance factors
Installation and Setup of the NVIDIA Container Toolkit
- Establishing drivers and CUDA compatibility
- Verifying GPU access within containers
- Adjusting the runtime environment settings
Creating GPU-Ready Docker Images
- Leveraging CUDA foundation images
- Encapsulating AI frameworks in GPU-compatible containers
- Handling dependencies for both training and inference
Executing GPU-Accelerated AI Tasks
- Running training processes with GPU support
- Overseeing multi-GPU operations
- Tracking GPU usage metrics
Enhancing Performance and Resource Management
- Restricting and isolating GPU resources
- Optimizing memory usage, batch sizes, and device placement
- Conducting performance tuning and diagnostics
Containerized Inference and Model Hosting
- Developing containers optimized for inference
- Handling high-volume workloads on GPUs
- Connecting model runners and APIs
Expanding GPU Capabilities with Docker
- Approaches for distributed GPU training
- Scaling inference microservices
- Orchestrating multi-container AI systems
Security and Stability for GPU-Equipped Containers
- Safeguarding GPU access in shared contexts
- Strengthening container image security
- Overseeing updates, versions, and compatibility issues
Conclusion and Future Directions
Requirements
- A solid grasp of deep learning principles
- Proficiency in Python and standard AI frameworks
- Basic knowledge of containerization concepts
Target Audience
- Deep learning engineers
- Research and development teams
- Specialists in AI model training
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.
