GradScaler
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
GradScaler has 10 facts recorded in Dontopedia across 3 references, with 4 live disagreements.
Mostly:is used for(2), rdf:type(2), enables(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
isMethodCallIs Method Call(1)
- Scaler.update
ex:scaler.update
rdf:typeRdf:type(1)
- Grad Scaler
ex:GradScaler
usesComponentUses Component(1)
- Training Loop
ex:training-loop
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 |
|---|---|---|
| Is Used for | Automatic Mixed Precision Training | [1] |
| Is Used for | Mixed Precision | [2] |
| Rdf:type | Grad Scaler | [2] |
| Rdf:type | Mixed Precision Tool | [3] |
| Enables | Mixed Precision | [2] |
| Enables | Mixed Precision Training | [3] |
| Is Used in | Training Loop | [2] |
| Purpose | mixed-precision-training | [3] |
Timeline
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References (3)
ctx:claims/beam/51a366c4-36ad-4c73-a8a6-a8071a33c62a- full textbeam-chunktext/plain1 KB
doc:beam/51a366c4-36ad-4c73-a8a6-a8071a33c62aShow excerpt
scaler.update() optimizer.zero_grad() # Example usage: train_model_with_amp(model, optimizer, dataloader, device, gradient_accumulation_steps=4) ``` 4. **Data Loading Efficiency:** - Use effici…
ctx:claims/beam/b37d3f65-b489-4a88-aa05-62e2c014851e- full textbeam-chunktext/plain1 KB
doc:beam/b37d3f65-b489-4a88-aa05-62e2c014851eShow excerpt
import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from torch.cuda.amp import GradScaler, autocast # Initialize PyTorch model model = nn.Sequential( nn.Linear(128, 128)…
ctx:claims/beam/a38a0bc2-6ed2-4089-b908-741e1595c678- full textbeam-chunktext/plain1 KB
doc:beam/a38a0bc2-6ed2-4089-b908-741e1595c678Show excerpt
### 6. Use `torch.cuda.empty_cache()` Periodically calling `torch.cuda.empty_cache()` can help free up unused memory on the GPU. ### 7. Use `torch.autograd.profiler` Profiling your code can help identify bottlenecks and areas where memory …
See also
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