Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to GPU-Accelerated Containerization
- Understanding GPU usage in deep learning workflows
- How Docker supports GPU-based workloads
- Key performance considerations
Installing and Configuring NVIDIA Container Toolkit
- Setting up drivers and CUDA compatibility
- Validating GPU access inside containers
- Configuring the runtime environment
Building GPU-Enabled Docker Images
- Using CUDA base images
- Packaging AI frameworks in GPU-ready containers
- Managing dependencies for training and inference
Running GPU-Accelerated AI Workloads
- Executing training jobs using GPUs
- Managing multi-GPU workloads
- Monitoring GPU utilization
Optimizing Performance and Resource Allocation
- Limiting and isolating GPU resources
- Optimizing memory, batch sizes, and device placement
- Performance tuning and diagnostics
Containerized Inference and Model Serving
- Building 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
- Coordinating multi-container AI systems
Security and Reliability for GPU-Enabled Containers
- Ensuring safe GPU access in shared environments
- Hardening container images
- Managing updates, versions, and compatibility
Summary and Next Steps
Requirements
- An understanding of deep learning fundamentals
- Experience with Python and common AI frameworks
- Familiarity with basic containerization concepts
Audience
- Deep learning engineers
- Research and development teams
- AI model trainers
21 Hours
Testimonials (5)
Interactive approach to conducting training.
Krzysztof Kupisz - Kredyt Inkaso S.A. Centrum Operacyjne w Lublinie
Course - Managing Kubernetes with Rancher
Machine Translated
The entire focus of the training involves hands-on experience (through writing code, configurations) with the training topics.
Adam Dereszewski - ATOS PGS sp. z o.o.
Course - Building Microservices with Spring Cloud and Docker
Machine Translated
Very informative and to the point. Hands on pratice
Gil Matias - FINEOS
Course - Introduction to Docker
Most appealing to me were the examples that supplemented the theory presented.
Milosz Galazka - LPP S.A.
Course - OpenShift for Administrators
Machine Translated
Labs and technical discussions.
