Dontopedia

cuda if available else cpu

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

cuda if available else cpu has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

5 facts·3 predicates·2 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

implementsImplements(1)

Other facts (4)

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.

4 facts
PredicateValueRef
Chooses BetweenCuda Device[2]
Chooses BetweenCpu Device[2]
Rdf:typeControl Flow Pattern[1]
EnablesHardware Fallback[1]

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.

typebeam/c470eab1-38ce-41c3-9d0a-f012e744b156
ex:ControlFlowPattern
labelbeam/c470eab1-38ce-41c3-9d0a-f012e744b156
cuda if available else cpu
enablesbeam/c470eab1-38ce-41c3-9d0a-f012e744b156
ex:hardware-fallback
choosesBetweenbeam/605023bc-3480-4af4-a3b2-03a662d04cfc
ex:cuda-device
choosesBetweenbeam/605023bc-3480-4af4-a3b2-03a662d04cfc
ex:cpu-device

References (2)

2 references
  1. ctx:claims/beam/c470eab1-38ce-41c3-9d0a-f012e744b156
    • full textbeam-chunk
      text/plain1 KBdoc:beam/c470eab1-38ce-41c3-9d0a-f012e744b156
      Show excerpt
      ```python def retrieve(queries): # Tokenize the queries inputs = tokenizer(queries, padding=True, truncation=True, return_tensors="pt") # Perform retrieval using the LLM outputs = model(**inputs
  2. ctx:claims/beam/605023bc-3480-4af4-a3b2-03a662d04cfc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/605023bc-3480-4af4-a3b2-03a662d04cfc
      Show excerpt
      def __init__(self, model, device='cpu'): self.model = model.to(device) self.device = device def preprocess(self, input_data): return torch.tensor(input_data, dtype=torch.float32).to(self.device) def sco

See also

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.