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Course Outline
Foundations of GPU-Accelerated Containerization
- The role of GPUs in deep learning workflows
- The function of Docker in supporting GPU-based workloads
- Critical performance factors to consider
Setup and Configuration of the NVIDIA Container Toolkit
- Establishing driver and CUDA compatibility
- Verifying GPU access within containers
- Tailoring the runtime environment
Creating GPU-Ready Docker Images
- Leveraging CUDA base images
- Encapsulating AI frameworks into GPU-optimized containers
- Handling dependencies for training and inference
Executing GPU-Accelerated AI Processes
- Running training jobs on GPUs
- Overseeing multi-GPU workloads
- Tracking GPU utilization
Enhancing Performance and Resource Management
- Controlling and isolating GPU resources
- Improving memory usage, batch sizes, and device placement
- Performance tuning and diagnostic techniques
Containerized Inference and Model Serving
- Developing containers optimized for inference
- Handling high-volume workloads on GPUs
- Connecting model runners and APIs
Scaling GPU Workloads with Docker
- Approaches for distributed GPU training
- Expanding inference microservices
- Orchestrating multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Securing GPU access in shared environments
- Strengthening container image security
- Managing updates, versioning, and compatibility
Wrap-up and Future Directions
Requirements
- A solid grasp of deep learning fundamentals
- Practical experience with Python and standard AI frameworks
- Basic knowledge of containerization principles
Target Audience
- Deep learning engineers
- R&D 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