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Course Outline
Introduction to GPU-Accelerated Containerization
- The role of GPUs in deep learning workflows
- Supporting GPU-based workloads with Docker
- Essential performance considerations
Installation and Configuration of the NVIDIA Container Toolkit
- Establishing driver and CUDA compatibility
- Verifying GPU access within containers
- Setting up the runtime environment
Creating GPU-Enabled Docker Images
- Leveraging CUDA base images
- Encapsulating AI frameworks in GPU-ready containers
- Handling dependencies for training and inference
Executing GPU-Accelerated AI Workloads
- Running training jobs utilizing GPUs
- Handling multi-GPU workloads
- Tracking GPU utilization
Performance and Resource Allocation Optimization
- Restricting and isolating GPU resources
- Tuning memory, batch sizes, and device placement
- Performance refinement and diagnostic analysis
Containerized Inference and Model Serving
- Developing containers optimized for inference
- Serving high-volume workloads on GPUs
- Integrating model runners and APIs
Scaling GPU Workloads with Docker
- Strategies for distributed GPU training
- Expanding inference microservices
- Orchestrating multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Securing GPU access in shared environments
- Enhancing the security posture of container images
- Managing updates, version control, and compatibility
Summary and Next Steps
Requirements
- A solid grasp of deep learning principles
- Proficiency with Python and standard AI frameworks
- Knowledge of fundamental containerization concepts
Target Audience
- Deep learning engineers
- Research and development teams
- AI model trainers
21 Hours
Testimonials (3)
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
Bogdan Olaru
Course - Introduction to Docker
The knowledge and exchanges with Augustin