Dontopedia

device assignment

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

device assignment has 13 facts recorded in Dontopedia across 6 references, with 2 live disagreements.

13 facts·8 predicates·6 sources·2 in dispute

Mostly:rdf:type(4), assigns(2), uses ternary operator(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (3)

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consists-ofConsists of(1)

followsFollows(1)

precedesPrecedes(1)

Other facts (12)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

12 facts
PredicateValueRef
Rdf:typeOperation[2]
Rdf:typeVariable Assignment[4]
Rdf:typeConditional Assignment[5]
Rdf:typeConditional Assignment[6]
AssignsDevice Variable[2]
Assignsdevice[4]
Uses Ternary Operatortrue[1]
FollowsAvailability Check[3]
Valuetorch.device('cuda' if torch.cuda.is_available() else 'cpu')[4]
ConditionCuda Available Check[5]
True ValueCuda Device[5]
False ValueCpu Device[5]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

usesTernaryOperatorbeam/bd88fada-39be-4f23-92a8-bcf3186013bd
true
typebeam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
ex:Operation
labelbeam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
device assignment
assignsbeam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
ex:device-variable
followsbeam/1dd18c5a-82f0-4898-9740-49697f0d9016
ex:availability-check
typebeam/343cede3-dc11-4e37-89af-916034a8c42b
ex:Variable-Assignment
assignsbeam/343cede3-dc11-4e37-89af-916034a8c42b
device
valuebeam/343cede3-dc11-4e37-89af-916034a8c42b
torch.device('cuda' if torch.cuda.is_available() else 'cpu')
typebeam/4d47005b-a1e7-4757-82f3-77722798dfec
ex:ConditionalAssignment
conditionbeam/4d47005b-a1e7-4757-82f3-77722798dfec
ex:cuda-available-check
trueValuebeam/4d47005b-a1e7-4757-82f3-77722798dfec
ex:cuda-device
falseValuebeam/4d47005b-a1e7-4757-82f3-77722798dfec
ex:cpu-device
typebeam/24776806-43b0-491e-806d-e4f4e8d75851
ex:ConditionalAssignment

References (6)

6 references
  1. ctx:claims/beam/bd88fada-39be-4f23-92a8-bcf3186013bd
    • full textbeam-chunk
      text/plain1 KBdoc:beam/bd88fada-39be-4f23-92a8-bcf3186013bd
      Show excerpt
      [Turn 8818] User: I'm trying to optimize the memory usage for my reranking model, and I've capped it at 1.9GB to reduce spikes by 20% for 11,000 queries. However, I'm not sure if this is the best approach. Can you review my code and suggest
  2. ctx:claims/beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
      Show excerpt
      - Use tools like `torch.utils.benchmark` to measure and compare the performance of different configurations. ### Example with Error Handling Here's an example with error handling: ```python import torch import torch.nn as nn class Sc
  3. ctx:claims/beam/1dd18c5a-82f0-4898-9740-49697f0d9016
  4. ctx:claims/beam/343cede3-dc11-4e37-89af-916034a8c42b
  5. ctx:claims/beam/4d47005b-a1e7-4757-82f3-77722798dfec
  6. ctx:claims/beam/24776806-43b0-491e-806d-e4f4e8d75851

See also

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