Device
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Device has 5 facts recorded in Dontopedia across 3 references.
Mostly:is initialized with(1), uses conditional(1), is assigned from(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedOther facts (5)
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.
| Predicate | Value | Ref |
|---|---|---|
| Is Initialized With | Torch Device | [1] |
| Uses Conditional | Cuda Cpu Conditional | [1] |
| Is Assigned From | torch.cuda.is_available | [2] |
| Checks | Cuda Availability | [3] |
| Defaults to | Cpu | [3] |
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.
References (3)
ctx:claims/beam/b2084fb4-c6e7-4f68-a30b-1fed653d4d63- full textbeam-chunktext/plain1 KB
doc:beam/b2084fb4-c6e7-4f68-a30b-1fed653d4d63Show excerpt
# Define the resizing module class ResizingModule(nn.Module): def __init__(self): super(ResizingModule, self).__init__() self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): …
ctx:claims/beam/2027f3e5-3e69-4ec4-941c-609aa4f28ed3- full textbeam-chunktext/plain1 KB
doc:beam/2027f3e5-3e69-4ec4-941c-609aa4f28ed3Show excerpt
loss.backward() optimizer.step() optimizer.zero_grad() # Log the processing log_entry = { 'timestamp': logging.LogRecord.created, 'level': 'INFO', 'batch_size': le…
ctx:claims/beam/37089ae6-6ce4-42e5-87a2-1cfd71693a4d- full textbeam-chunktext/plain1 KB
doc:beam/37089ae6-6ce4-42e5-87a2-1cfd71693a4dShow excerpt
5. **Parallel Processing**: - Utilize multi-threading or multi-processing for data loading. Here's an optimized version of your code: ### Optimized Code ```python import torch import torch.nn as nn import torch.optim as optim from tor…
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