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Course Outline
Introduction to GPU-Accelerated Containerization
- The role of GPUs in deep learning workflows
- How Docker facilitates GPU-based workloads
- Essential performance considerations
Installation and Configuration of the NVIDIA Container Toolkit
- Setting up drivers and ensuring CUDA compatibility
- Verifying GPU access within containers
- Configuring the runtime environment
Creating GPU-Enabled Docker Images
- Leveraging CUDA base images
- Integrating AI frameworks into GPU-ready containers
- Handling dependencies for training and inference
Executing GPU-Accelerated AI Workloads
- Running training jobs via GPUs
- Overseeing multi-GPU workloads
- Tracking GPU utilization
Performance Optimization and Resource Allocation
- Restricting and isolating GPU resources
- Refining memory, batch sizes, and device placement
- Performance tuning and diagnostic techniques
Containerized Inference and Model Serving
- Constructing 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
- Orchestrating multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Ensuring secure GPU access in shared environments
- Hardening container images
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- A solid grasp of deep learning fundamentals
- Practical experience with Python and standard AI frameworks
- Basic familiarity with containerization concepts
Target Audience
- Deep learning engineers
- Research and development teams
- AI model trainers
21 Hours
Testimonials (3)
multi-tiered, structured course programme.
Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
The knowledge and exchanges with Augustin