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Course Outline
Introduction to GPU-Accelerated Containerization
- The role of GPUs in deep learning workflows
- The way Docker facilitates GPU-based workloads
- Essential performance factors to consider
Installation and Configuration of the NVIDIA Container Toolkit
- Establishing driver and CUDA compatibility
- Verifying GPU access within containers
- Adjusting the runtime environment settings
Creating GPU-Enabled Docker Images
- Utilizing CUDA base images
- Packaging AI frameworks into GPU-ready containers
- Handling dependencies for training and inference processes
Executing GPU-Accelerated AI Workloads
- Running training jobs utilizing GPU resources
- Handling multi-GPU workload management
- Tracking and monitoring GPU usage
Performance Optimization and Resource Allocation
- Restricting and isolating GPU resources
- Enhancing memory usage, batch sizes, and device placement
- Conducting performance tuning and diagnostic analysis
Containerized Inference and Model Serving
- Developing containers ready for inference
- Handling high-load workloads on GPUs
- Integrating model runners and APIs
Scaling GPU Workloads with Docker
- Strategies for implementing distributed GPU training
- Expanding inference microservices
- Managing complex multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Securing GPU access in shared environments
- Strengthening the security of container images
- Overseeing updates, versioning, and compatibility
Wrap-up and Future Directions
Requirements
- A foundational understanding of deep learning principles
- Proficiency with Python and standard AI frameworks
- A solid grasp of basic containerization concepts
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