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Course Outline
Introduction to GPU-Accelerated Containerisation
- Comprehending GPU usage within deep learning workflows
- The role of Docker in supporting GPU-based workloads
- Essential performance considerations
Installing and Configuring the NVIDIA Container Toolkit
- Setting up drivers and ensuring CUDA compatibility
- Verifying GPU access within containers
- Configuring the runtime environment
Creating GPU-Enabled Docker Images
- Leveraging CUDA base images
- Packaging AI frameworks into GPU-ready containers
- Managing dependencies for training and inference
Executing GPU-Accelerated AI Workloads
- Running training jobs utilizing GPUs
- Handling multi-GPU workloads
- Monitoring GPU utilisation
Optimising Performance and Resource Allocation
- Restricting and isolating GPU resources
- Optimising memory, batch sizes, and device placement
- Performance tuning and diagnostics
Containerised Inference and Model Serving
- Developing inference-ready containers
- Serving high-volume 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
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- A solid understanding of deep learning fundamentals
- Experience with Python and standard AI frameworks
- Knowledge of basic 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