MXNet
E234123
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
All labels observed (6)
| Label | Occurrences |
|---|---|
| MXNet canonical | 8 |
| Apache MXNet | 1 |
| MXNet Engine | 1 |
| MXNet Executor | 1 |
| MXNet KVStore | 1 |
| MXNet Optimizer | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2111937 — 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: MXNet Context triple: [NVIDIA DGX, supports, MXNet]
-
A.
Theano
Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
-
B.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
C.
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
-
D.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
E.
Keras
Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
- 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: MXNet Target entity description: MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
A.
Theano
Theano is an open-source numerical computation library for Python that allows efficient definition, optimization, and evaluation of mathematical expressions, particularly those involving multi-dimensional arrays, and was widely used as a backend for deep learning frameworks.
-
B.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
C.
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework developed by Microsoft for building, training, and deploying neural networks at scale.
-
D.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
E.
Keras
Keras is a high-level neural networks API written in Python that simplifies building, training, and deploying deep learning models, often running on top of frameworks like TensorFlow.
- F. None of above. chosen
Statements (88)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning framework
ⓘ
machine learning library ⓘ neural network library ⓘ open-source software ⓘ |
| designedFor |
distributed systems
ⓘ
efficient training ⓘ inference ⓘ multi-GPU environments ⓘ scalable training ⓘ |
| developedBy | Apache Software Foundation ⓘ |
| hasAPI |
Gluon API
ⓘ
NDArray API ⓘ Symbol API ⓘ |
| hasComponent |
MXNet
self-linksurface differs
ⓘ
surface form:
MXNet Engine
MXNet self-linksurface differs ⓘ
surface form:
MXNet Executor
MXNet self-linksurface differs ⓘ
surface form:
MXNet KVStore
MXNet self-linksurface differs ⓘ
surface form:
MXNet Optimizer
|
| hostedOn | GitHub ⓘ |
| integratedInto |
AWS Deep Learning AMI
ⓘ
Amazon SageMaker ⓘ |
| optimizationGoal |
high performance
ⓘ
memory efficiency ⓘ scalability ⓘ |
| previouslyDevelopedBy | DMLC (Distributed Machine Learning Community) ⓘ |
| programmingLanguage |
C++
ⓘ
Go ⓘ JavaScript ⓘ Julia ⓘ Perl ⓘ Python ⓘ R ⓘ Scala ⓘ |
| repositoryURL | https://github.com/apache/mxnet ⓘ |
| softwareLicense | Apache License 2.0 ⓘ |
| supportsCheckpoint |
model parameters
ⓘ
optimizer states ⓘ |
| supportsDataType |
float16
ⓘ
float32 ⓘ int8 ⓘ |
| supportsDeployment |
cloud
ⓘ
edge devices ⓘ on-premises ⓘ |
| supportsFeature |
GPU acceleration
ⓘ
Gluon API ⓘ NDArray API ⓘ automatic differentiation ⓘ checkpointing ⓘ custom operators ⓘ data parallelism ⓘ distributed training ⓘ hybrid symbolic-imperative programming ⓘ imperative computation ⓘ model parallelism ⓘ model serialization ⓘ parameter server architecture ⓘ sparse tensors ⓘ symbolic computation ⓘ |
| supportsFormat | ONNX ⓘ |
| supportsHardware |
CPU
ⓘ
GPU ⓘ distributed cluster ⓘ multi-GPU ⓘ |
| supportsLanguageBinding |
C++
ⓘ
Go ⓘ JavaScript ⓘ Julia ⓘ Perl ⓘ Python ⓘ R ⓘ Scala ⓘ |
| supportsModelType |
LSTM networks
ⓘ
convolutional neural networks ⓘ feedforward neural networks ⓘ recurrent neural networks ⓘ reinforcement learning models ⓘ sequence-to-sequence models ⓘ |
| supportsMonitoring | training metrics ⓘ |
| supportsOptimization |
AdaGrad
ⓘ
Adam ⓘ RMSProp ⓘ SGD ⓘ |
| supportsQuantization | yes ⓘ |
| supportsUseCase |
computer vision
ⓘ
natural language processing ⓘ recommendation systems ⓘ speech recognition ⓘ |
| supportsVisualization | computation graphs ⓘ |
| usedBy | Amazon Web Services ⓘ |
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: MXNet Description of subject: MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
Referenced by (13)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
MXNet Engine
this entity surface form:
MXNet KVStore
this entity surface form:
MXNet Executor
this entity surface form:
MXNet Optimizer
this entity surface form:
Apache MXNet