Docker
A platform for containerising applications, essential for deploying AI models consistently across different environments.
In-Depth Explanation
Docker is a platform for developing, shipping, and running applications in containers. For AI/ML, Docker ensures models run consistently across development, testing, and production environments.
Key concepts:
- Container: Lightweight, isolated runtime environment
- Image: Template for creating containers
- Dockerfile: Instructions to build an image
- Registry: Storage for container images (Docker Hub, ECR, GCR)
- Compose: Define multi-container applications
AI/ML benefits:
- Reproducible environments (exact dependencies)
- Consistent local and production behaviour
- Simplified deployment and scaling
- GPU support via NVIDIA Container Toolkit
- Easy sharing of model environments
Common AI use cases:
- Package models with dependencies
- Deploy inference servers
- Run training jobs
- Create development environments
- CI/CD pipelines for ML
Business Context
Docker standardises AI deployment, eliminating "works on my machine" problems and enabling consistent production deployments.
How Clever Ops Uses This
We containerise all AI deployments for Australian businesses, ensuring consistent, reproducible systems that can be deployed anywhere.
Example Use Case
"Packaging a model server in Docker so it runs identically on developer laptops, testing servers, and production cloud."
Frequently Asked Questions
Related Terms
Related Resources
Kubernetes
An open-source container orchestration platform for automating deployment, scali...
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