multi-threaded data loading
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
multi-threaded data loading has 8 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:rdf:type(2), enabled by(1), affects(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
enablesEnables(2)
- Data Loader
ex:DataLoader - Parallel Processing
ex:parallel-processing
describesFeatureDescribes Feature(1)
- Data Loading Preprocessing
ex:data-loading-preprocessing
purposePurpose(1)
- Num Workers
ex:num-workers
Other facts (6)
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 | Process | [1] |
| Rdf:type | Technique | [3] |
| Enabled by | num_workers parameter | [2] |
| Affects | Data Preprocessing | [3] |
| Increases | Throughput | [3] |
| Part of | Data Loading | [3] |
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 (3)
ctx:claims/beam/bb661926-a23e-4f89-b0a0-8fd1c07034c4- full textbeam-chunktext/plain1 KB
doc:beam/bb661926-a23e-4f89-b0a0-8fd1c07034c4Show excerpt
1. **Data Loading and Preprocessing**: - Use `DataLoader` with `num_workers` to enable multi-threaded data loading. - Ensure data is moved to the GPU using `.to(device)`. 2. **Model and Optimizer Initialization**: - Move the model…
ctx:claims/beam/d9a80d69-c4c9-47c5-8393-2eaf674f6563- full textbeam-chunktext/plain1 KB
doc:beam/d9a80d69-c4c9-47c5-8393-2eaf674f6563Show excerpt
inputs = torch.tensor(decrypted_batch['query'], dtype=torch.float32).to(device) labels = torch.tensor(decrypted_batch['label'], dtype=torch.long).to(device) # Forward pass outputs = model(inputs) los…
ctx:claims/beam/2d5078e9-d244-454c-b9a1-551fc675b359
See also
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