Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Konteenarada Docker ee ML Model Kedis

Ku tababbarto lakabinta sawirka Docker, dhismooyin dhowr ah, maaraynta farshaxanimada iyo doorashooyinka amniga runtime ee codsiyada-barashada mashiinka.

Wadooyinka haga ee la xidhiidhaWeelasha Docker ee Moodooyinka ML
Su'aal 1 ee 8

Waa maxay sababta koobiyaynta faylka ku-tiirsanaanta ka hor inta aan si joogta ah loo bedelin isha codsiga?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay sababta koobiyaynta faylka ku-tiirsanaanta ka hor inta aan si joogta ah loo bedelin isha codsiga?
    • Docker can reuse the dependency-install layer when only source code changes
    • It guarantees packages never need security updates
    • It makes model data part of the base image
    • It runs dependency installation on the GPU
  2. Dhibaato noocee ah ayuu Docker-ka-marxaladda badan dhisaa ciwaanka?
    • Run every stage at the same time in production
    • Use build tools in one stage and copy needed runtime artifacts to another
    • Avoid declaring any base image
    • Store secrets in an intermediate layer
  3. Waa maxay sababta aad ugu dhejin lahayd xog-ururin gaar ah halkii aad ku koobiyayn lahayd sawirka?
    • It removes the need for data access controls
    • It ensures the model is deterministic
    • It avoids embedding data in an image that is distributed
    • It converts the dataset into a model layer automatically
  4. Waa maxay pinning dheefshiidka siinayaa sawirka salka?
    • A smaller image size regardless of its layers
    • Automatic security updates without rebuilding
    • A record of the Python package versions only
    • A more precise identity for the referenced image content
  5. Halkee ayay tahay in sirta runtime guud ahaan la keeno?
    • In source files copied into the image
    • Through a secure runtime mechanism rather than embedded image layers
    • In a public base-image tag
    • Inside the model's class labels
  6. What does a running container add beyond an image?
    • The physical GPU driver inside the container
    • A new compiled base image automatically
    • Runtime configuration such as mounts, environment and network
    • A guarantee of bitwise-deterministic training
  7. Why run an application as a non-root user when practical?
    • It makes the image digest shorter
    • It prevents every dependency vulnerability
    • It guarantees the model meets accuracy targets
    • It limits privileges if the process is compromised
  8. What does a container image alone not guarantee?
    • Identical numerical results across different hardware
    • A packaged filesystem and metadata
    • A specified application command
    • Layered build inputs