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Course Outline
Foundations of GPU-Accelerated Containerization
- The role of GPUs in deep learning pipelines
- The way Docker facilitates GPU-based workloads
- Essential performance factors to consider
Setup and Configuration of the NVIDIA Container Toolkit
- Establishing drivers and ensuring CUDA compatibility
- Confirming GPU accessibility within containers
- Tuning the runtime environment
Creating GPU-Ready Docker Images
- Leveraging CUDA base images
- Encapsulating AI frameworks into GPU-capable containers
- Handling dependencies for training and inference
Executing GPU-Accelerated AI Tasks
- Running training processes on GPUs
- Overseeing multi-GPU operations
- Tracking GPU usage levels
Enhancing Performance and Resource Management
- Restricting and isolating GPU resources
- Improving memory usage, batch sizes, and device placement
- Tuning performance and troubleshooting
Containerized Inference and Model Serving
- Constructing containers optimized for inference
- Handling high-demand workloads on GPUs
- Connecting model runners and APIs
Scaling GPU Workloads via Docker
- Approaches for distributed GPU training
- Expanding inference microservices
- Orchestrating multi-container AI systems
Security and Stability for GPU-Enabled Containers
- Safeguarding GPU access in shared environments
- Strengthening container images
- Oversight of updates, versions, and compatibility
Recap and Future Directions
Requirements
- A solid grasp of deep learning basics
- Practical experience with Python and standard AI frameworks
- Knowledge of fundamental 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