cuML
E890458
cuML is a GPU-accelerated machine learning library in the NVIDIA RAPIDS ecosystem that provides scalable, high-performance implementations of common ML algorithms.
All labels observed (2)
| Label | Occurrences |
|---|---|
| cuML canonical | 4 |
| RAPIDS cuML | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T10882137 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: cuML Context triple: [NVIDIA RAPIDS, component, cuML]
-
A.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
B.
cuSOLVER
cuSOLVER is an NVIDIA GPU-accelerated linear algebra library that provides high-performance routines for solving dense and sparse systems of equations, eigenvalue problems, and related numerical tasks.
-
C.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
D.
cuSPARSE
cuSPARSE is NVIDIA’s GPU-accelerated library providing high-performance sparse linear algebra routines for CUDA applications.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: cuML Target entity description: cuML is a GPU-accelerated machine learning library in the NVIDIA RAPIDS ecosystem that provides scalable, high-performance implementations of common ML algorithms.
-
A.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
B.
cuSOLVER
cuSOLVER is an NVIDIA GPU-accelerated linear algebra library that provides high-performance routines for solving dense and sparse systems of equations, eigenvalue problems, and related numerical tasks.
-
C.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
D.
cuSPARSE
cuSPARSE is NVIDIA’s GPU-accelerated library providing high-performance sparse linear algebra routines for CUDA applications.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- F. None of above. chosen
Statements (52)
| Predicate | Object |
|---|---|
| instanceOf |
GPU-accelerated library
ⓘ
machine learning library ⓘ open-source software ⓘ |
| aimsTo | provide scalable high-performance ML on GPUs ⓘ |
| belongsTo | data science and analytics domain ⓘ |
| compatibleWith | scikit-learn ecosystem ⓘ |
| developer |
NVIDIA
ⓘ
linked to:
NVIDIA Corporation
|
| documentation | https://docs.rapids.ai/api/cuml/stable/ ⓘ |
| feature |
GPU-accelerated estimators
ⓘ
distributed training ⓘ multi-GPU support via Dask ⓘ scikit-learn-like API ⓘ |
| implements | machine learning algorithms ⓘ |
| integratesWith |
Dask
ⓘ
RAPIDS cuGraph ⓘ
linked to:
cuGraph
cuDF ⓘ |
| license | Apache License 2.0 ⓘ |
| optimizedFor | GPU ⓘ |
| partOf |
NVIDIA RAPIDS ecosystem
ⓘ
linked to:
NVIDIA RAPIDS
RAPIDS ⓘ
linked to:
NVIDIA RAPIDS
|
| programmingLanguage |
C++
ⓘ
CUDA ⓘ
linked to:
NVIDIA CUDA
Python ⓘ |
| repository | https://github.com/rapidsai/cuml ⓘ |
| requires | NVIDIA CUDA-capable GPU ⓘ |
| supportsAlgorithm |
ARIMA
ⓘ
DBSCAN ⓘ
linked to:
DBSCAN algorithm
Naive Bayes ⓘ
linked to:
Naive Bayes classifier
PCA ⓘ SVM-like methods (experimental in some releases) ⓘ UMAP ⓘ k-means clustering ⓘ k-nearest neighbors ⓘ
linked to:
KNN
linear regression ⓘ logistic regression ⓘ random forest ⓘ t-SNE ⓘ |
| supportsDataFormat |
NumPy arrays (via host-device transfer)
ⓘ
cuDF DataFrame ⓘ
linked to:
cuDF
|
| supportsLanguageBinding |
C++
ⓘ
Python ⓘ |
| supportsPlatform | Linux ⓘ |
| useCase |
classification
ⓘ
clustering ⓘ dimensionality reduction ⓘ regression ⓘ tabular data machine learning ⓘ |
| usesFramework |
CUDA
ⓘ
linked to:
NVIDIA CUDA
RAFT ⓘ cuBLAS ⓘ cuSolver ⓘ
linked to:
cuSOLVER
|
| usesHardwareAcceleration |
NVIDIA GPU
ⓘ
linked to:
NVIDIA GPU hardware
|
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: cuML Description of subject: cuML is a GPU-accelerated machine learning library in the NVIDIA RAPIDS ecosystem that provides scalable, high-performance implementations of common ML algorithms.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: cuML
linked to: cuML
linked to: cuML