|
gptkbp:instance_of
|
gptkb:software
|
|
gptkbp:api
|
for customization
|
|
gptkbp:can_be_used_with
|
gptkb:scikit-learn
|
|
gptkbp:community_support
|
gptkb:theorem
|
|
gptkbp:developed_by
|
Ray Labs
|
|
gptkbp:has
|
available in documentation
|
|
gptkbp:has_documentation
|
available online
|
|
gptkbp:has_feature
|
Bayesian optimization
grid search
random search
population-based training
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Ray Tune
|
|
gptkbp:integrates_with
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_available_on
|
gptkb:archive
|
|
gptkbp:is_compatible_with
|
gptkb:Keras
gptkb:Jupyter_notebooks
|
|
gptkbp:is_designed_for
|
scalable machine learning
|
|
gptkbp:is_effective_against
|
parameter search
|
|
gptkbp:is_influential_in
|
machine learning community
|
|
gptkbp:is_integrated_with
|
gptkb:Weights_&_Biases
M Lflow
|
|
gptkbp:is_maintained_by
|
gptkb:Community_Center
|
|
gptkbp:is_open_source
|
gptkb:theorem
|
|
gptkbp:is_optimized_for
|
cloud environments
machine learning models
deep learning models
|
|
gptkbp:is_part_of
|
gptkb:Ray_ecosystem
AI/ ML tools
Ray framework
|
|
gptkbp:is_recognized_by
|
tech industry
|
|
gptkbp:is_scalable
|
large datasets
|
|
gptkbp:is_supported_by
|
gptkb:Ray_community
|
|
gptkbp:is_used_by
|
data scientists
machine learning engineers
|
|
gptkbp:is_used_for
|
hyperparameter tuning
hyperparameter optimization
|
|
gptkbp:is_used_in
|
gptkb:academic_research
industry projects
|
|
gptkbp:offers
|
multi-agent optimization
|
|
gptkbp:passes_through
|
clusters
local machines
|
|
gptkbp:performance
|
available for evaluation
|
|
gptkbp:provides
|
automated search algorithms
|
|
gptkbp:released_in
|
gptkb:2019
|
|
gptkbp:suitable_for
|
research purposes
production environments
|
|
gptkbp:supports
|
distributed training
multi-node training
|
|
gptkbp:user_experience
|
gptkb:theorem
|
|
gptkbp:written_in
|
gptkb:Library
|
|
gptkbp:bfsParent
|
gptkb:Ray_project
|
|
gptkbp:bfsLayer
|
5
|