PlaidML
E99362
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
All labels observed (1)
| Label | Occurrences |
|---|---|
| PlaidML canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T849726 — 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: PlaidML Context triple: [Keras, supportsBackend, PlaidML]
-
A.
Swift for TensorFlow
Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
-
B.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
C.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
D.
TensorFlow.js
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
E.
TPUs (via XLA integrations)
TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
- 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: PlaidML Target entity description: PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
-
A.
Swift for TensorFlow
Swift for TensorFlow is an experimental machine learning platform that integrates TensorFlow directly into the Swift programming language to enable differentiable programming and high-performance model development.
-
B.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
C.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
D.
TensorFlow.js
TensorFlow.js is a JavaScript library that enables training and running machine learning models directly in the browser and in Node.js using TensorFlow.
-
E.
TPUs (via XLA integrations)
TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning engine
ⓘ
machine learning library ⓘ open-source software ⓘ tensor compiler ⓘ |
| acquiredBy |
Intel
ⓘ
linked to:
Intel Corporation
|
| canBeIntegratedWith | existing deep learning workflows ⓘ |
| category |
AI framework
ⓘ
GPU computing library ⓘ |
| designedFor |
deep learning acceleration
ⓘ
neural network computation ⓘ |
| developedBy |
Vertex.AI
ⓘ
linked to:
Vertex AI
|
| feature |
automatic kernel generation
ⓘ
backend-agnostic computation ⓘ cross-platform support ⓘ graph optimization ⓘ tensor algebra compiler ⓘ |
| goal |
avoid vendor lock-in
ⓘ
provide high-performance deep learning on diverse hardware ⓘ |
| hasComponent |
Tile IR
ⓘ
backend code generators ⓘ |
| implements | tile language ⓘ |
| isHardwareAgnostic | true ⓘ |
| maintainer |
Intel
ⓘ
linked to:
Intel Corporation
|
| optimizationTarget |
performance
ⓘ
portability ⓘ |
| programmingLanguage |
C++
ⓘ
Python ⓘ |
| repositoryPlatform | GitHub ⓘ |
| softwareLicense | Apache License 2.0 ⓘ |
| supportsBackend |
LLVM
ⓘ
Metal ⓘ OpenCL ⓘ |
| supportsFramework |
Keras
ⓘ
ONNX ⓘ |
| supportsHardwareType |
AMD GPU
ⓘ
linked to:
AMD Radeon GCN-based GPU
CPU ⓘ GPU ⓘ Intel GPU ⓘ
linked to:
Intel Arc
Metal devices ⓘ NVIDIA GPU ⓘ
linked to:
GPU
OpenCL devices ⓘ |
| supportsLanguage |
C++
ⓘ
Python ⓘ |
| supportsOS |
Linux
ⓘ
Windows ⓘ macOS ⓘ |
| useCase |
inference of neural networks
ⓘ
running Keras models on non-CUDA GPUs ⓘ training neural networks ⓘ |
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: PlaidML Description of subject: PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.