Dontopedia

torch.device

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torch.device has 4 facts recorded in Dontopedia across 2 references, with 2 live disagreements.

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

Inbound mentions (2)

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hasTypeHas Type(2)

Other facts (2)

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2 facts
PredicateValueRef
Rdf:typeData Type[1]
Rdf:typePy Torch Class[2]

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/16c146b3-4e30-40ba-bda6-27d68d4d4231
ex:DataType
labelbeam/16c146b3-4e30-40ba-bda6-27d68d4d4231
torch.device
typebeam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
ex:PyTorchClass
labelbeam/2b55433d-f10b-4ba8-ac07-7b8a156dc333
torch.device class

References (2)

2 references
  1. ctx:claims/beam/16c146b3-4e30-40ba-bda6-27d68d4d4231
    • full textbeam-chunk
      text/plain1 KBdoc:beam/16c146b3-4e30-40ba-bda6-27d68d4d4231
      Show excerpt
      device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = RerankingModel().to(device) dataset = ... # Your dataset loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True) optimizer
  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

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