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Course Outline
Overview of GPU-Accelerated Containerization
- The role of GPUs in deep learning workflows
- The way Docker facilitates GPU-based tasks
- Essential performance factors to consider
Installation and Setup of the NVIDIA Container Toolkit
- Installing drivers and ensuring CUDA compatibility
- Verifying GPU access within containers
- Configuring the runtime environment
Creating GPU-Capable Docker Images
- Leveraging CUDA base images
- Packaging AI frameworks into GPU-ready containers
- Handling dependencies for training and inference
Executing GPU-Accelerated AI Tasks
- Running training jobs on GPUs
- Handling multi-GPU workloads
- Tracking GPU utilization
Performance Optimization and Resource Management
- Controlling and isolating GPU resources
- Refining memory usage, batch sizes, and device assignment
- Tuning performance and conducting diagnostics
Containerized Inference and Model Deployment
- Constructing containers ready for inference
- Serving high-demand workloads on GPUs
- Integrating model runners and APIs
Scaling GPU Workloads via Docker
- Approaches for distributed GPU training
- Scaling inference microservices
- Managing multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Safeguarding GPU access in shared environments
- Enhancing the security of container images
- Overseeing updates, versions, and compatibility
Conclusion and Future Directions
Requirements
- A solid grasp of deep learning fundamentals
- Proficiency with Python and popular AI frameworks
- Basic knowledge of containerization principles
Target Audience
- Deep learning engineers
- Research and development teams
- AI model trainers
21 Hours
Testimonials (3)
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
the trainer had a lot of knowledge and patience to share with us
Bogdan Olaru
Course - Introduction to Docker
The knowledge and exchanges with Augustin