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Course Outline
Introduction to GPU-Accelerated Containerization
- Understanding GPU usage in deep learning workflows
- How Docker supports GPU-based workloads
- Key performance considerations
Installing and Configuring NVIDIA Container Toolkit
- Setting up drivers and CUDA compatibility
- Validating GPU access inside containers
- Configuring the runtime environment
Building GPU-Enabled Docker Images
- Using CUDA base images
- Packaging AI frameworks in GPU-ready containers
- Managing dependencies for training and inference
Running GPU-Accelerated AI Workloads
- Executing training jobs using GPUs
- Managing multi-GPU workloads
- Monitoring GPU utilization
Optimizing Performance and Resource Allocation
- Limiting and isolating GPU resources
- Optimizing memory, batch sizes, and device placement
- Performance tuning and diagnostics
Containerized Inference and Model Serving
- Building 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
- An understanding of deep learning fundamentals
- Experience with Python and common AI frameworks
- Familiarity with basic containerization concepts
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
