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Course Outline
Foundations of GPU-Accelerated Containerization
- Exploring the role of GPUs in deep learning workflows
- Understanding how Docker facilitates GPU-based workloads
- Identifying key performance factors
Setup and Configuration of the NVIDIA Container Toolkit
- Establishing driver and CUDA compatibility
- Verifying GPU accessibility within containers
- Tuning the runtime environment
Creating GPU-Ready Docker Images
- Leveraging CUDA base images
- Encapsulating AI frameworks into GPU-compatible containers
- Handling dependencies for training and inference processes
Executing GPU-Accelerated AI Tasks
- Running training jobs utilizing GPU resources
- Managing workloads across multiple GPUs
- Tracking and monitoring GPU usage
Enhancing Performance and Resource Management
- Controlling and isolating GPU resources
- Refining memory usage, batch sizes, and device placement
- Conducting performance tuning and diagnostics
In-Container Inference and Model Serving
- Developing containers optimized for inference
- Handling high-throughput workloads on GPUs
- Integrating model runners and API interfaces
Scaling GPU Operations with Docker
- Implementing strategies for distributed GPU training
- Scaling inference microservices
- Orchestrating multi-container AI systems
Security and Resilience for GPU-Enabled Containers
- Securing GPU access in shared environments
- Strengthening container image security
- Oversight of updates, versions, and compatibility
Conclusions and Future Directions
Requirements
- A solid grasp of deep learning fundamentals
- Proficiency in Python and common AI frameworks
- Knowledge of basic containerization principles
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
