Đề cương khóa học

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

Yêu cầu

  • 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 Giờ học

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