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Course Outline
Introduction to GPU-Accelerated Containerisation
- Exploring GPU utilisation within deep learning workflows
- Understanding how Docker facilitates GPU-based workloads
- Key performance factors to consider
Installing and Configuring the NVIDIA Container Toolkit
- Configuring drivers and ensuring CUDA compatibility
- Verifying GPU access within containers
- Setting up the runtime environment
Creating GPU-Enabled Docker Images
- Utilising CUDA base images
- Packageing AI frameworks into GPU-ready containers
- Managing dependencies for training and inference
Executing GPU-Accelerated AI Workloads
- Running training jobs using GPUs
- Managing workloads across multiple GPUs
- Monitoring GPU utilisation metrics
Optimising Performance and Resource Allocation
- Limiting and isolating GPU resources
- Optimising memory usage, batch sizes, and device placement
- Performance tuning and diagnostic techniques
Containerised Inference and Model Serving
- Building containers ready for inference tasks
- Serving high-load workloads on GPUs
- Integrating model runners and APIs
Scaling GPU Workloads with Docker
- Strategies for distributed GPU training
- Scaling inference microservices
- Coordinating multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Ensuring secure GPU access in shared environments
- Hardening container images for security
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- A solid grasp of deep learning fundamentals
- Proficiency with Python and common AI frameworks
- Basic familiarity with containerisation 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
