CUDA if available else CPU
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
CUDA if available else CPU has 9 facts recorded in Dontopedia across 5 references, with 2 live disagreements.
Mostly:rdf:type(3), can be(2), is selected by(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
assignedValueAssigned Value(2)
- Device
ex:device - Device Variable
ex:device-variable
selectsDeviceSelects Device(1)
- Device Detection
ex:device-detection
toDeviceTo Device(1)
- Device Movement
ex:device-movement
usesConditionalStringUses Conditional String(1)
- Torch Device Object
ex:torch-device-object
Other facts (8)
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 |
|---|---|---|
| Rdf:type | Device Selection | [2] |
| Rdf:type | Conditional Expression | [3] |
| Rdf:type | Device Choice | [5] |
| Can Be | "cuda" | [4] |
| Can Be | "cpu" | [4] |
| Is Selected by | Device Detection | [1] |
| Preferred Device | Cuda | [5] |
| Fallback Device | Cpu | [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.
References (5)
ctx:claims/beam/1a80c04e-0cf2-40e8-819b-8a4ba1401f6c- full textbeam-chunktext/plain1 KB
doc:beam/1a80c04e-0cf2-40e8-819b-8a4ba1401f6cShow excerpt
Would you like to proceed with this implementation, or do you have any additional questions or concerns? [Turn 8190] User: How can I optimize the performance of my PyTorch model, specifically with version 2.1.2, to achieve 99.8% stability …
ctx:claims/beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333- full textbeam-chunktext/plain1 KB
doc:beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333Show 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…
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…
ctx:claims/beam/a88a027e-f783-4e36-b111-3fe65e988f1f- full textbeam-chunktext/plain1 KB
doc:beam/a88a027e-f783-4e36-b111-3fe65e988f1fShow excerpt
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ …
ctx:claims/beam/24776806-43b0-491e-806d-e4f4e8d75851
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.