Concurrent Batch Processing
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Concurrent Batch Processing has 3 facts recorded in Dontopedia across 2 references.
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enablesEnables(1)
- Thread Pool Executor
ex:thread-pool-executor
Other facts (3)
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| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Computational Pattern | [1] |
| Enables | Throughput Improvement | [1] |
| Is Enabled by | Thread Pool Executor | [2] |
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References (2)
ctx:claims/beam/9135d402-fc47-4283-b912-3de3bce312e4- full textbeam-chunktext/plain1 KB
doc:beam/9135d402-fc47-4283-b912-3de3bce312e4Show excerpt
futures.append(executor.submit(pipeline.evaluate, batch)) # Collect results results = [future.result() for future in futures] # Flatten the results scores = np.concatenate(results) print(scores) ```…
ctx:claims/beam/7fff30a2-d53b-47d9-a9b2-885c870e8128- full textbeam-chunktext/plain1 KB
doc:beam/7fff30a2-d53b-47d9-a9b2-885c870e8128Show excerpt
3. **Redis Configuration**: Ensure Redis is properly configured and accessible from your application. ### Next Steps 1. **Implement Batch Processing**: Modify the `reformulate` and `batch_reformulate` methods to handle batches. 2. **Use `…
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