torch.cuda.is_available()
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
torch.cuda.is_available() has 13 facts recorded in Dontopedia across 6 references, with 1 live disagreement.
Mostly:rdf:type(5), used in(1), function(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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containsDeviceCheckContains Device Check(1)
- Improved Code
ex:improved-code
detectionLogicDetection Logic(1)
- Device
ex:device
selectedBySelected by(1)
- Device
ex:device
usesUses(1)
- Example Usage Section
ex:example-usage-section
Other facts (12)
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| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Condition | [1] |
| Rdf:type | Py Torch Function | [3] |
| Rdf:type | Conditional Check | [4] |
| Rdf:type | Device Check | [5] |
| Rdf:type | Function Call | [6] |
| Used in | Device Variable | [1] |
| Function | torch.cuda.is_available | [2] |
| Fallback Device | cpu | [5] |
| Uses Function | torch.cuda.is_available | [5] |
| Enables | GPU acceleration | [5] |
| Conditional Logic | cuda if available else cpu | [5] |
| Invokes | Torch Cuda Is Available | [6] |
Timeline
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References (6)
ctx:claims/beam/c470eab1-38ce-41c3-9d0a-f012e744b156- full textbeam-chunktext/plain1 KB
doc:beam/c470eab1-38ce-41c3-9d0a-f012e744b156Show 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…
ctx:claims/beam/9c95419a-99e1-4237-800b-9b4747989acb- full textbeam-chunktext/plain1 KB
doc:beam/9c95419a-99e1-4237-800b-9b4747989acbShow excerpt
3. **Device Management**: Explicitly manage the device (CPU/GPU) to ensure the model and data are on the same device. 4. **Gradient Management**: Since you are using the model for scoring, ensure that gradients are disabled to improve perf…
ctx:claims/beam/605023bc-3480-4af4-a3b2-03a662d04cfc- full textbeam-chunktext/plain1 KB
doc:beam/605023bc-3480-4af4-a3b2-03a662d04cfcShow 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…
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/73205099-d256-4a1b-9568-78e1f64184b0- full textbeam-chunktext/plain1 KB
doc:beam/73205099-d256-4a1b-9568-78e1f64184b0Show excerpt
[Turn 9469] Assistant: Certainly! Let's go through your code and identify potential issues and improvements to ensure stability and proper functioning. ### Potential Issues and Improvements 1. **DataLoader Usage**: - Your code does not…
ctx:claims/beam/24776806-43b0-491e-806d-e4f4e8d75851
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