Parallel Training
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Parallel Training has 2 facts recorded in Dontopedia across 1 reference.
2 facts·2 predicates·1 sources
Maturity scale
raw canonical shape-checked rule-derived certifiedOther facts (2)
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.
2 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Training Strategy | [1] |
| Uses | Process Pool Executor | [1] |
Timeline
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typebeam/9151b445-41b5-4d53-900d-4199adc168c1
ex:TrainingStrategy
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usesbeam/9151b445-41b5-4d53-900d-4199adc168c1
ex:process-pool-executor
References (1)
1 references
ctx:claims/beam/9151b445-41b5-4d53-900d-4199adc168c1- full textbeam-chunktext/plain1 KB
doc:beam/9151b445-41b5-4d53-900d-4199adc168c1Show excerpt
model = MyModel().to(device) optimizer = optim.Adam(model.parameters(), lr=0.001) # Define the update logic def update_model(model, optimizer, data_loader): model.train() for data, _ in data_loader: data = data.to(device) …
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