|
gptkbp:instance_of
|
gptkb:project
|
|
gptkbp:application
|
hyperparameter tuning
reinforcement learning
data parallelism
distributed training
model serving
|
|
gptkbp:architectural_style
|
multi-node
cluster-based
single-node
|
|
gptkbp:community
|
open source
|
|
gptkbp:developed_by
|
gptkb:UC_Berkeley
|
|
gptkbp:focus
|
gptkb:software_framework
data processing
distributed computing
parallel processing
|
|
gptkbp:goal
|
enhance productivity
support diverse workloads
enable scalable applications
provide easy-to-use AP Is
simplify distributed computing
|
|
gptkbp:has_documentation
|
available online
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Ray project
|
|
gptkbp:integrates_with
|
gptkb:lake
gptkb:fortification
gptkb:skincare_product
gptkb:park
M Lflow
|
|
gptkbp:language
|
gptkb:Java
gptkb:C++
gptkb:Library
|
|
gptkbp:latest_version
|
2.5.0
|
|
gptkbp:license
|
Apache License 2.0
|
|
gptkbp:performance
|
low latency
high throughput
|
|
gptkbp:provides
|
gptkb:Ray_Data
gptkb:Ray_Serve
gptkb:Ray_Tune
Ray R Lib
|
|
gptkbp:release_date
|
gptkb:2017
|
|
gptkbp:repository
|
gptkb:archive
|
|
gptkbp:supports
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
Python libraries
|
|
gptkbp:tutorials
|
available online
|
|
gptkbp:user_base
|
gptkb:physicist
gptkb:software
data scientists
data engineers
machine learning engineers
|
|
gptkbp:uses
|
actor model
|
|
gptkbp:bfsParent
|
gptkb:Ray_community
|
|
gptkbp:bfsLayer
|
4
|