Course Outline
Introduction to GPU-Accelerated Containerization for Government
- Understanding the role of GPUs in deep learning workflows for government
- How Docker supports GPU-based workloads for government applications
- Key performance considerations for government use cases
Installing and Configuring NVIDIA Container Toolkit for Government
- Setting up drivers and ensuring CUDA compatibility for government systems
- Validating GPU access inside containers for government operations
- Configuring the runtime environment for secure government use
Building GPU-Enabled Docker Images for Government
- Using CUDA base images in government projects
- Packaging AI frameworks in GPU-ready containers for government applications
- Managing dependencies for training and inference in government environments
Running GPU-Accelerated AI Workloads for Government
- Executing training jobs using GPUs for government projects
- Managing multi-GPU workloads in government settings
- Monitoring GPU utilization for efficient resource management in government
Optimizing Performance and Resource Allocation for Government
- Limiting and isolating GPU resources for secure government operations
- Optimizing memory, batch sizes, and device placement for government workloads
- Performance tuning and diagnostics for government applications
Containerized Inference and Model Serving for Government
- Building inference-ready containers for government use
- Serving high-load workloads on GPUs in government environments
- Integrating model runners and APIs for government applications
Scaling GPU Workloads with Docker for Government
- Strategies for distributed GPU training in government projects
- Scaling inference microservices for government operations
- Coordinating multi-container AI systems for government use
Security and Reliability for GPU-Enabled Containers in Government
- Ensuring safe GPU access in shared environments for government
- Hardening container images for government security standards
- Managing updates, versions, and compatibility for government systems
Summary and Next Steps for Government
Requirements
- An understanding of deep learning fundamentals for government applications
- Experience with Python and common artificial intelligence frameworks
- Familiarity with basic containerization concepts
Audience
- Deep learning engineers in the public sector
- Research and development teams for government projects
- AI model trainers working on governmental initiatives
Testimonials (5)
OC is new to us and we learnt alot and the labs were excellent
sharkey dollie
Course - OpenShift 4 for Administrators
Very informative and to the point. Hands on pratice
Gil Matias - FINEOS
Course - Introduction to Docker
Labs and technical discussions.
Dinesh Panchal - AXA XL
Course - Advanced Docker
It gave a good grounding for Docker and Kubernetes.
Stephen Dowdeswell - Global Knowledge Networks UK
Course - Docker (introducing Kubernetes)
I mostly enjoyed the knowledge of the trainer.
