DMLC (Distributed Machine Learning Community)
E814036
DMLC (Distributed Machine Learning Community) is an open-source collaborative group that develops scalable machine learning and deep learning systems and tools, including major projects like Apache MXNet and XGBoost.
All labels observed (1)
| Label | Occurrences |
|---|---|
| DMLC (Distributed Machine Learning Community) canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9674961 — 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: DMLC (Distributed Machine Learning Community) Context triple: [MXNet, previouslyDevelopedBy, DMLC (Distributed Machine Learning Community)]
-
A.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
B.
PaddlePaddle
PaddlePaddle is an open-source deep learning platform developed by Baidu, designed for large-scale distributed training and deployment of neural networks.
-
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.
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.
-
E.
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.
- 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: DMLC (Distributed Machine Learning Community) Target entity description: DMLC (Distributed Machine Learning Community) is an open-source collaborative group that develops scalable machine learning and deep learning systems and tools, including major projects like Apache MXNet and XGBoost.
-
A.
MXNet
MXNet is an open-source deep learning framework designed for efficient, scalable training and inference across multiple GPUs and distributed systems.
-
B.
PaddlePaddle
PaddlePaddle is an open-source deep learning platform developed by Baidu, designed for large-scale distributed training and deployment of neural networks.
-
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.
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.
-
E.
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.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
developer community
ⓘ
open-source software community ⓘ |
| abbreviation | DMLC NERFINISHED ⓘ |
| aimsTo |
build scalable machine learning systems
ⓘ
enable distributed training ⓘ support large-scale deep learning ⓘ |
| associatedWith | Apache MXNet (Apache Incubator / ASF) NERFINISHED ⓘ |
| codeHostedOn | GitHub NERFINISHED ⓘ |
| collaboratesWith | Apache Software Foundation NERFINISHED ⓘ |
| contributionsInclude |
GPU-accelerated machine learning
ⓘ
distributed training infrastructure ⓘ parameter server implementations ⓘ |
| develops |
Apache MXNet
NERFINISHED
ⓘ
CXXNet NERFINISHED ⓘ DGL NERFINISHED ⓘ MXNet NERFINISHED ⓘ MinPy NERFINISHED ⓘ NNVM NERFINISHED ⓘ Rabit NERFINISHED ⓘ Singa (early contributions) NERFINISHED ⓘ TVM NERFINISHED ⓘ Treelite NERFINISHED ⓘ XGBoost NERFINISHED ⓘ dmlc-core NERFINISHED ⓘ mshadow NERFINISHED ⓘ ps-lite NERFINISHED ⓘ |
| focusesOn |
deep learning systems
ⓘ
distributed machine learning ⓘ open-source software ⓘ scalable machine learning ⓘ |
| fullName | Distributed Machine Learning Community NERFINISHED ⓘ |
| GitHubOrganization | https://github.com/dmlc ⓘ |
| hasProjectType |
compiler stack for deep learning
ⓘ
deep learning framework ⓘ gradient boosting library ⓘ graph neural network library ⓘ machine learning framework ⓘ |
| hasWebsite | https://dmlc.github.io ⓘ |
| licenseModel | open source ⓘ |
| notableProject |
Apache MXNet
NERFINISHED
ⓘ
DGL ⓘ TVM NERFINISHED ⓘ XGBoost NERFINISHED ⓘ |
| supportsLanguage |
C++
NERFINISHED
ⓘ
Julia NERFINISHED ⓘ Python ⓘ R NERFINISHED ⓘ Scala NERFINISHED ⓘ |
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: DMLC (Distributed Machine Learning Community) Description of subject: DMLC (Distributed Machine Learning Community) is an open-source collaborative group that develops scalable machine learning and deep learning systems and tools, including major projects like Apache MXNet and XGBoost.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.