Course Outline
Foundations of GPU-Accelerated Containerization
- The role of GPUs in deep learning workflows
- The mechanism by which Docker facilitates GPU-based tasks
- Essential performance factors to consider
Installation and Setup of the NVIDIA Container Toolkit
- Configuring drivers and ensuring CUDA compatibility
- Verifying GPU accessibility within containers
- Preparing the runtime environment
Creating GPU-Ready Docker Images
- Leveraging CUDA base images
- Encapsulating AI frameworks into GPU-prepared containers
- Handling dependencies for training and inference phases
Executing GPU-Accelerated AI Tasks
- Running training jobs leveraging GPU power
- Overseeing workloads that utilize multiple GPUs
- Tracking GPU usage metrics
Enhancing Performance and Resource Distribution
- Controlling and separating GPU resources
- Refining memory usage, batch sizes, and device placement
- Adjusting performance and diagnosing issues
Containerized Inference and Model Deployment
- Developing containers ready for inference
- Handling high-volume workloads on GPUs
- Connecting model runners and API interfaces
Expanding GPU Workloads Using Docker
- Approaches for distributed GPU training
- Scaling inference microservices
- Synchronizing multi-container AI systems
Security and Stability for GPU-Enabled Containers
- Guaranteeing secure GPU access in shared environments
- Strengthening the security of container images
- Managing updates, version control, and compatibility
Wrap-up and Future Directions
Requirements
- A solid grasp of deep learning core principles
- Proficiency with Python and prevalent AI frameworks
- Knowledge of fundamental containerization principles
Target Audience
- Deep learning engineers
- Research and development teams
- AI model trainers
Testimonials (2)
multi-tiered, structured course programme.
Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.