TensorFlow

E17662
deep learning framework machine learning framework open-source software

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.

Try in SPARQL Jump to: Surface forms Statements Referenced by

All labels observed (10)

Label Occurrences
TensorFlow canonical 53
TensorFlow team 3
TensorFlow 2 1

Statements (60)

Predicate Object
instanceOf deep learning framework ⓘ
machine learning framework ⓘ
open-source software ⓘ
backedBy Google ⓘ
developer Google ⓘ
Google Brain ⓘ
surface form: Google Brain team
feature automatic differentiation ⓘ
distributed training ⓘ
model serving APIs ⓘ
saved model format ⓘ
visualization with TensorBoard ⓘ
hasComponent Keras ⓘ
TF-Agents ⓘ
TensorBoard ⓘ
TensorFlow self-linksurface differs ⓘ
surface form: TensorFlow Core

TensorFlow Extended ⓘ
TensorFlow Hub ⓘ
TensorFlow self-linksurface differs ⓘ
surface form: TensorFlow Lite

TensorFlow.js ⓘ
initialReleaseDate 2015-11-09 ⓘ
license Apache License 2.0 ⓘ
maintainer TensorFlow self-linksurface differs ⓘ
surface form: TensorFlow team
predecessor DistBelief ⓘ
programmingLanguage C++ ⓘ
NVIDIA CUDA ⓘ
surface form: CUDA

JavaScript ⓘ
Python ⓘ
repository https://github.com/tensorflow/tensorflow ⓘ
supportsDeployment cloud platforms ⓘ
edge devices ⓘ
mobile devices ⓘ
web browsers ⓘ
supportsHardware CPU ⓘ
GPU ⓘ
TPU ⓘ
supportsLanguage C++ ⓘ
Go ⓘ
Java ⓘ
JavaScript ⓘ
Python ⓘ
supportsModelType autoencoders ⓘ
convolutional neural networks ⓘ
generative adversarial networks ⓘ
recurrent neural networks ⓘ
transformer models ⓘ
supportsParadigm dataflow graphs ⓘ
eager execution ⓘ
supportsPlatform Android ⓘ
Linux ⓘ
Windows ⓘ
iOS ⓘ
macOS ⓘ
useCase computer vision ⓘ
deep learning research ⓘ
natural language processing ⓘ
neural network training ⓘ
production model deployment ⓘ
reinforcement learning ⓘ
time series forecasting ⓘ
website https://www.tensorflow.org ⓘ

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: TensorFlow
Description of subject: 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.

Referenced by (64)

Full triples — surface form annotated when it differs from this entity's canonical label.

Python → machineLearningLibrary → TensorFlow ⓘ
Google Brain → productDeveloped → TensorFlow ⓘ
OpenAI Gym → compatibleWith → TensorFlow ⓘ
NumPy → influenced → TensorFlow ⓘ
TensorFlow → hasComponent → TensorFlow self-linksurface differs ⓘ
this entity surface form: TensorFlow Core
TensorFlow → hasComponent → TensorFlow self-linksurface differs ⓘ
this entity surface form: TensorFlow Lite
TensorFlow → maintainer → TensorFlow self-linksurface differs ⓘ
this entity surface form: TensorFlow team
OpenAI Baselines → dependsOn → TensorFlow ⓘ
GPT-2 → framework → TensorFlow ⓘ
Keras → supportsBackend → TensorFlow ⓘ
Keras → primaryBackend → TensorFlow ⓘ
Keras → integratedInto → TensorFlow ⓘ
Keras → partOf → TensorFlow ⓘ
this entity surface form: TensorFlow 2.x core API
NVIDIA DGX → supports → TensorFlow ⓘ
Jeff Dean → notableWork → TensorFlow ⓘ
Rajat Monga → developed → TensorFlow ⓘ
Rajat Monga → coCreatorOf → TensorFlow ⓘ
Jeffrey Dean → knownFor → TensorFlow ⓘ
Jeffrey Dean → workedOn → TensorFlow ⓘ
this entity surface form: TensorFlow machine learning framework
Jeffrey Dean → coAuthorOf → TensorFlow ⓘ
this entity surface form: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
VGG → implementedIn → TensorFlow ⓘ
ResNet → implementationAvailableIn → TensorFlow ⓘ
Swift for TensorFlow → developedBy → TensorFlow ⓘ
this entity surface form: TensorFlow team
Swift for TensorFlow → integratesWith → TensorFlow ⓘ
Swift for TensorFlow → relatedTo → TensorFlow ⓘ
TPU → supportsFramework → TensorFlow ⓘ
TensorFlow.js → basedOn → TensorFlow ⓘ
TensorFlow.js → partOf → TensorFlow ⓘ
this entity surface form: TensorFlow ecosystem
TensorBoard → developedFor → TensorFlow ⓘ
TF-Agents → basedOn → TensorFlow ⓘ
TF-Agents → supports → TensorFlow ⓘ
this entity surface form: TensorFlow 2
TensorFlow Hub → compatibleWith → TensorFlow ⓘ
TensorFlow Hub → supportsFramework → TensorFlow ⓘ
this entity surface form: TensorFlow 2.x
TensorFlow Hub → maintainer → TensorFlow ⓘ
this entity surface form: TensorFlow team
DistBelief → inspired → TensorFlow ⓘ
DistBelief → predecessorOf → TensorFlow ⓘ
DistBelief → successor → TensorFlow ⓘ
Vertex AI → supports → TensorFlow ⓘ
Hugging Face Transformers → compatibleWith → TensorFlow ⓘ
Theano → influenced → TensorFlow ⓘ
RLlib → supportsFramework → TensorFlow ⓘ
Gymnasium → compatibleWith → TensorFlow ⓘ
Python scientific stack → hasComponent → TensorFlow ⓘ
Transformer → implementedIn → TensorFlow ⓘ
TensorFlow Extended → basedOn → TensorFlow ⓘ
RMSProp → implementedIn → TensorFlow ⓘ
Proximal Policy Optimization → oftenImplementedIn → TensorFlow ⓘ
Gaudi → compatibleWith → TensorFlow ⓘ
Goya inference processor → compatibleWith → TensorFlow ⓘ
Adam optimizer → implementedIn → TensorFlow ⓘ