|
gptkbp:instanceOf
|
gptkb:software
distributed computing framework
|
|
gptkbp:category
|
gptkb:software
cloud computing
distributed systems
machine learning infrastructure
|
|
gptkbp:citation
|
Moritz, P., Nishihara, R., et al. (2018). Ray: A Distributed Framework for Emerging AI Applications. OSDI 2018.
|
|
gptkbp:component
|
gptkb:Ray_Core
gptkb:Ray_Data
gptkb:Ray_RLlib
gptkb:Ray_Serve
gptkb:Ray_Train
gptkb:Ray_Tune
Ray AIR
|
|
gptkbp:contributedTo
|
community contributors
|
|
gptkbp:developedBy
|
gptkb:Anyscale
|
|
gptkbp:documentation
|
https://docs.ray.io/en/latest/
|
|
gptkbp:feature
|
resource management
scalability
fault tolerance
task parallelism
integration with cloud platforms
actor-based programming model
|
|
gptkbp:firstReleased
|
2017
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Ray Project
|
|
gptkbp:integration
|
gptkb:AWS
gptkb:LightGBM
gptkb:TensorFlow
gptkb:XGBoost
gptkb:Azure
gptkb:GCP
gptkb:Kubernetes
gptkb:PyTorch
gptkb:scikit-learn
|
|
gptkbp:license
|
gptkb:Apache_License_2.0
|
|
gptkbp:logo
|
https://raw.githubusercontent.com/ray-project/ray/master/doc/source/images/ray_logo.png
|
|
gptkbp:maintainedBy
|
gptkb:Anyscale
|
|
gptkbp:programmingLanguage
|
gptkb:Java
gptkb:Python
|
|
gptkbp:repository
|
https://github.com/ray-project/ray
|
|
gptkbp:supports
|
gptkb:machine_learning
gptkb:reinforcement_learning
hyperparameter tuning
distributed training
|
|
gptkbp:usedFor
|
scalable Python and machine learning applications
|
|
gptkbp:website
|
https://www.ray.io/
|
|
gptkbp:bfsParent
|
gptkb:RLlib
gptkb:KubeRay
|
|
gptkbp:bfsLayer
|
7
|