Scaler Update
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Scaler Update has 8 facts recorded in Dontopedia across 4 references, with 1 live disagreement.
Mostly:rdf:type(3), is called in(1), executed 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.
adjustedByAdjusted by(1)
- Scaler
ex:scaler
containsContains(1)
- Weight Update Logic
ex:weight-update-logic
dependsOnDepends on(1)
- Loss Tracking
ex:loss-tracking
executesExecutes(1)
- Weight Update
ex:weight-update
step6Step6(1)
- Training Sequence
ex:training-sequence
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 | Scaler Update | [2] |
| Rdf:type | Scaler Operation | [3] |
| Rdf:type | Scaler Adjustment | [4] |
| Is Called in | Training Loop | [1] |
| Executed by | Grad Scaler | [2] |
| Requires | Grad Scaler | [2] |
| Depends on | Optimizer Step | [2] |
| Calls | Update | [3] |
Timeline
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References (4)
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/d722ad53-d442-458e-b561-cab7e12fcbbf- full textbeam-chunktext/plain1 KB
doc:beam/d722ad53-d442-458e-b561-cab7e12fcbbfShow excerpt
optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True) scaler = GradScaler() try: for epoch in range(100): running…
ctx:claims/beam/80e4b051-0931-49af-8359-38149d7a6361- full textbeam-chunktext/plain1 KB
doc:beam/80e4b051-0931-49af-8359-38149d7a6361Show excerpt
with profiler.profile(record_shapes=True, use_cuda=True) as prof: with profiler.record_function("model_training"): for i, (batch_inputs, batch_targets) in enumerate(dataloader): with autocast(): # Us…
ctx:claims/beam/2bacfc08-73f1-4c21-88e8-d07ff734da09- full textbeam-chunktext/plain914 B
doc:beam/2bacfc08-73f1-4c21-88e8-d07ff734da09Show excerpt
# Backward pass scaler.scale(loss).backward() # Update weights if (i + 1) % accumulation_steps == 0: scaler.step(optimizer) …
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