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Course Outline
Introduction to GPU-Accelerated Containerization
- Understanding the role of GPUs in deep learning workflows
- How Docker facilitates GPU-based workloads
- Key performance factors to consider
Installing and Configuring the NVIDIA Container Toolkit
- Setting up drivers and ensuring CUDA compatibility
- Verifying GPU access within containers
- Configuring the runtime environment
Constructing GPU-Enabled Docker Images
- Utilizing CUDA base images
- Packaging AI frameworks into GPU-ready containers
- Managing dependencies for training and inference tasks
Executing GPU-Accelerated AI Workloads
- Running training jobs utilizing GPUs
- Managing workloads across multiple GPUs
- Monitoring GPU utilization rates
Optimizing Performance and Resource Allocation
- Limiting and isolating GPU resources
- Optimizing memory usage, batch sizes, and device placement
- Performance tuning and diagnostics
Containerized Inference and Model Serving
- Creating inference-ready containers
- 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 safe GPU access in shared environments
- Hardening container images
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- A foundational understanding of deep learning concepts
- Experience using Python and common AI frameworks
- Familiarity with basic containerization principles
Target Audience
- Deep learning engineers
- Research and development teams
- AI model trainers
21 Hours
Testimonials (2)
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