NDArray API
E814032
The NDArray API is MXNet’s core multi-dimensional array interface for efficient numerical computation and deep learning operations.
All labels observed (1)
| Label | Occurrences |
|---|---|
| NDArray API canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T9674940 — 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: NDArray API Context triple: [MXNet, supportsFeature, NDArray API]
-
A.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
B.
tensor programs framework
The tensor programs framework is a theoretical approach developed by Greg Yang that rigorously analyzes and characterizes the behavior and scaling limits of large neural networks using tools from probability and random matrix theory.
-
C.
jax.experimental
jax.experimental is a submodule of the JAX library that provides access to experimental, unstable, or cutting-edge numerical and machine learning features not yet part of the stable API.
-
D.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
E.
ArrayComm
ArrayComm is a wireless communications technology company known for pioneering smart antenna and adaptive beamforming solutions to improve mobile network capacity and performance.
- 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: NDArray API Target entity description: The NDArray API is MXNet’s core multi-dimensional array interface for efficient numerical computation and deep learning operations.
-
A.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
B.
tensor programs framework
The tensor programs framework is a theoretical approach developed by Greg Yang that rigorously analyzes and characterizes the behavior and scaling limits of large neural networks using tools from probability and random matrix theory.
-
C.
jax.experimental
jax.experimental is a submodule of the JAX library that provides access to experimental, unstable, or cutting-edge numerical and machine learning features not yet part of the stable API.
-
D.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
E.
ArrayComm
ArrayComm is a wireless communications technology company known for pioneering smart antenna and adaptive beamforming solutions to improve mobile network capacity and performance.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
array programming interface
ⓘ
software library component ⓘ |
| belongsToProject | Apache Software Foundation ⓘ |
| designedFor |
efficient tensor computation
ⓘ
large-scale deep learning ⓘ scientific computing ⓘ |
| exposedIn |
Julia
ⓘ
Perl ⓘ Python ⓘ R ⓘ Scala ⓘ |
| hasType | multi-dimensional array interface ⓘ |
| implementedIn | C++ ⓘ |
| integratesWith |
MXNet KVStore for distributed training
ⓘ
MXNet autograd module ⓘ
linked to:
MXNet
MXNet data iterators ⓘ
linked to:
MXNet
|
| license | Apache License 2.0 ⓘ |
| partOf |
Apache MXNet
ⓘ
linked to:
MXNet
|
| provides |
asynchronous execution model
ⓘ
broadcasting semantics ⓘ element-wise operations ⓘ in-place operations ⓘ indexing and slicing ⓘ linear algebra operations ⓘ n-dimensional arrays ⓘ random number generation ⓘ reduction operations ⓘ |
| supports |
CPU execution
ⓘ
GPU acceleration ⓘ automatic differentiation ⓘ deep learning operations ⓘ imperative programming ⓘ numerical computation ⓘ symbolic programming integration ⓘ tensor operations ⓘ |
| supportsDataType |
float16
ⓘ
float32 ⓘ float64 ⓘ int32 ⓘ uint8 ⓘ |
| supportsFeature |
context-aware arrays
ⓘ
lazy evaluation ⓘ memory reuse ⓘ multi-GPU training ⓘ serialization ⓘ |
| usedBy |
MXNet Gluon API
ⓘ
linked to:
MXNet
MXNet symbolic graph API ⓘ
linked to:
MXNet
|
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: NDArray API Description of subject: The NDArray API is MXNet’s core multi-dimensional array interface for efficient numerical computation and deep learning operations.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.