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Course Outline
Introduction to GPU-Accelerated Containerization
- Understanding how GPUs are used in deep learning workflows
- How Docker facilitates GPU-based workloads
- Key factors affecting performance
Installing and Configuring NVIDIA Container Toolkit
- Setting up drivers and ensuring CUDA compatibility
- Verifying GPU access within containers
- Configuring the runtime environment
Building Docker Images with GPU Support
- Utilizing CUDA base images
- Packaging AI frameworks into containers ready for GPUs
- Managing dependencies required for training and inference
Running AI Workloads Accelerated by GPUs
- Executing training jobs using GPU power
- Handling workloads across multiple GPUs
- Monitoring GPU usage and performance
Optimizing Performance and Resource Allocation
- Restricting and isolating GPU resources
- Tuning memory, batch sizes, and device placement
- Performance tuning and diagnostics
Inference and Model Serving in Containers
- Creating containers prepared for inference
- Supporting high-volume workloads on GPUs
- Integrating model execution engines and APIs
Scaling GPU Workloads with Docker
- Strategies for distributed GPU training
- Scaling up inference microservices
- Coordinating AI systems across multiple containers
Security and Reliability for Containers with GPU Support
- Ensuring safe GPU access in shared settings
- Strengthening container images
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- Knowledge of deep learning basics
- Experience using Python and popular AI frameworks
- Understanding of fundamental 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