os.cpu_count
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
os.cpu_count has 18 facts recorded in Dontopedia across 6 references, with 3 live disagreements.
18 facts·8 predicates·6 sources·3 in dispute
Mostly:rdf:type(6), returns(3), programming language(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (9)
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.
countCount(2)
- Worker Processes
ex:worker-processes - Worker Processes
ex:worker-processes
computedFromComputed From(1)
- Cpu Cores Doubled
ex:cpu-cores-doubled
count-determined-byCount Determined by(1)
- Worker Threads
ex:worker-threads
createdWithCreated With(1)
- Pool Instance
ex:pool-instance
determinationMethodDetermination Method(1)
- Number of Cpu Cores
ex:number-of-cpu-cores
processesCountProcesses Count(1)
- Pool Object
ex:pool-object
providesFunctionProvides Function(1)
- Os Module
ex:os-module
sourceSource(1)
- Process Count
ex:process-count
Other facts (15)
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.
15 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Function Call | [1] |
| Rdf:type | Function Call | [2] |
| Rdf:type | System Function | [3] |
| Rdf:type | Programming Function | [4] |
| Rdf:type | System Function | [5] |
| Rdf:type | Function Call | [6] |
| Returns | Number of Cpu Cores | [2] |
| Returns | Number of Cpu Cores | [4] |
| Returns | Core Count | [5] |
| Programming Language | Python | [4] |
| Purpose | determines-number-of-worker-threads | [6] |
| Based on | number-of-cpu-cores | [6] |
| Import Source | os | [6] |
| Module | os | [6] |
| Return Value | number-of-cpu-cores | [6] |
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.
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typebeam/033a8e69-4536-4bb5-95fa-8622b141c188
ex:FunctionCall
—
typebeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
ex:FunctionCall
—
labelbeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
os.cpu_count
—
returnsbeam/c74e97dd-23f2-45e9-9ec1-958b9896a948
ex:number-of-cpu-cores
—
typebeam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a
ex:SystemFunction
—
typebeam/00c6dc14-7ce1-4383-847a-fbf9f0479a94
ex:ProgrammingFunction
—
labelbeam/00c6dc14-7ce1-4383-847a-fbf9f0479a94
os.cpu_count()
—
programmingLanguagebeam/00c6dc14-7ce1-4383-847a-fbf9f0479a94
ex:Python
—
returnsbeam/00c6dc14-7ce1-4383-847a-fbf9f0479a94
ex:number-of-cpu-cores
—
typebeam/47d57751-a78d-4497-9d85-c0f9cc7c20ad
ex:SystemFunction
—
returnsbeam/47d57751-a78d-4497-9d85-c0f9cc7c20ad
ex:core-count
—
typebeam/4d4fddbd-bca6-4dbf-b313-6a75761246df
ex:Function-Call
—
labelbeam/4d4fddbd-bca6-4dbf-b313-6a75761246df
os.cpu_count()
—
purposebeam/4d4fddbd-bca6-4dbf-b313-6a75761246df
determines-number-of-worker-threads
—
based-onbeam/4d4fddbd-bca6-4dbf-b313-6a75761246df
number-of-cpu-cores
—
import-sourcebeam/4d4fddbd-bca6-4dbf-b313-6a75761246df
os
—
modulebeam/4d4fddbd-bca6-4dbf-b313-6a75761246df
os
—
return-valuebeam/4d4fddbd-bca6-4dbf-b313-6a75761246df
number-of-cpu-cores
References (6)
6 references
ctx:claims/beam/033a8e69-4536-4bb5-95fa-8622b141c188- full textbeam-chunktext/plain1 KB
doc:beam/033a8e69-4536-4bb5-95fa-8622b141c188Show excerpt
for i in range(0, len(documents), batch_size): batch = documents[i:i + batch_size] with Pool(processes=os.cpu_count()) as pool: pool.map(ingest_document, batch) def main(): documents = [f"document_{i}" f…
ctx:claims/beam/c74e97dd-23f2-45e9-9ec1-958b9896a948- full textbeam-chunktext/plain1 KB
doc:beam/c74e97dd-23f2-45e9-9ec1-958b9896a948Show excerpt
4. **Monitoring and Logging**: Implement monitoring and logging to ensure high uptime and diagnose issues quickly. ### Example Implementation Let's modify your code to use multiprocessing to handle the ingestion of documents concurrently.…
ctx:claims/beam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084a- full textbeam-chunktext/plain1 KB
doc:beam/7da9ea7b-c0ac-49fd-b423-5ee8dee6084aShow excerpt
documents = [f"document_{i}" for i in range(18000)] start_time = datetime.now() ingest_documents(documents) end_time = datetime.now() total_time = end_time - start_time print(f"Total ingestion time: {total_time}") …
ctx:claims/beam/00c6dc14-7ce1-4383-847a-fbf9f0479a94- full textbeam-chunktext/plain1 KB
doc:beam/00c6dc14-7ce1-4383-847a-fbf9f0479a94Show excerpt
[Turn 9489] Assistant: Yes, if the number of users scales up to thousands, you may need to adjust the thread pool configuration to ensure optimal performance and resource utilization. Here are some considerations and adjustments you can mak…
ctx:claims/beam/47d57751-a78d-4497-9d85-c0f9cc7c20ad- full textbeam-chunktext/plain1 KB
doc:beam/47d57751-a78d-4497-9d85-c0f9cc7c20adShow excerpt
Here's an example implementation that dynamically adjusts the number of workers based on the number of users: ```python import time import os from concurrent.futures import ThreadPoolExecutor, as_completed from cryptography.hazmat.primitiv…
ctx:claims/beam/4d4fddbd-bca6-4dbf-b313-6a75761246df
See also
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