Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Koonteenarada GPU oo wata NVIDIA Qalabka Konteenarka

Kala saar darawallada GPU-ga martida loo yahay weelka CUDA maktabadaha oo raadi tillaabooyinka runtime loo baahan yahay si loo soo bandhigo oo loo ansixiyo qalabka NVIDIA.

Wadooyinka haga ee la xidhiidhaQalabka Kontaynarrada GPU Nvidia Qalabka Kontaynarrada
Su'aal 1 ee 8

Which component normally manages the physical NVIDIA GPU at the kernel level?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Which component normally manages the physical NVIDIA GPU at the kernel level?
    • The host driver
    • The CUDA container image alone
    • The model checkpoint
    • The Python package lockfile
  2. What role does NVIDIA Container Toolkit provide?
    • A replacement GPU kernel driver stored in each image
    • Runtime integration that exposes devices and driver interfaces to containers
    • A framework-neutral model accuracy benchmark
    • A compiler that converts all Python into CUDA
  3. Why can a container with CUDA libraries still fail to initialize the GPU?
    • CUDA libraries always replace the host driver
    • The container necessarily needs its own physical GPU
    • The host driver or runtime integration may be incompatible or missing
    • The model file does not contain a device node
  4. What does seeing a GPU in a container prove?
    • That memory allocation can never fail
    • That the model's accuracy is acceptable
    • That CUDA and driver versions are identical
    • Device visibility, but not that every application kernel will run correctly
  5. Why is updating CUDA packages inside an image different from updating the host driver?
    • Both update the same kernel module automatically
    • They are different software layers with different responsibilities
    • The container cannot contain any runtime libraries
    • Driver versions are determined by the model weights
  6. Which test is stronger than checking device visibility alone?
    • Inspect the model filename
    • Read the image tag
    • Run a small representative framework operation on the GPU
    • Confirm the container starts without a GPU request
  7. What should an incident record include for reproducibility?
    • Only the framework version and model architecture
    • Only the host driver and GPU model
    • Image tag and Python version without runtime configuration
    • Image digest, host driver, toolkit and runtime configuration
  8. What does GPU containerization not provide by itself?
    • A fully independent hardware driver environment
    • Packaged user-space dependencies
    • A way to request devices at launch
    • An image filesystem