|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:can_be_used_with
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:constructed_in
|
Ray core
|
|
gptkbp:developed_by
|
gptkb:Ray_Team
gptkb:Library
|
|
gptkbp:enables
|
Real-time inference
|
|
gptkbp:has_feature
|
Dynamic scaling
Health checks
Traffic splitting
Custom routing
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Ray Serve
|
|
gptkbp:integrates_with
|
gptkb:computer
|
|
gptkbp:is_available_on
|
gptkb:2020
gptkb:archive
|
|
gptkbp:is_compatible_with
|
gptkb:lake
gptkb:fortification
Various ML frameworks
|
|
gptkbp:is_designed_for
|
Machine learning applications
|
|
gptkbp:is_documented_in
|
Ray documentation
|
|
gptkbp:is_integrated_with
|
gptkb:g_RPC
RESTAP Is
|
|
gptkbp:is_known_for
|
gptkb:benchmark
Flexibility
Ease of use
|
|
gptkbp:is_open_source
|
gptkb:theorem
|
|
gptkbp:is_optimized_for
|
Low latency
Resource efficiency
High throughput
|
|
gptkbp:is_part_of
|
gptkb:Ray_project
gptkb:Ray_ecosystem
Machine learning workflows
AI model serving solutions
|
|
gptkbp:is_scalable
|
Cloud environments
On-premise environments
|
|
gptkbp:is_supported_by
|
gptkb:Ray_community
Community contributions
|
|
gptkbp:is_used_by
|
Data scientists
Machine learning engineers
|
|
gptkbp:is_used_for
|
Model monitoring
A/ B testing
Model serving at scale
|
|
gptkbp:is_used_in
|
Production environments
|
|
gptkbp:language
|
gptkb:Library
|
|
gptkbp:offers
|
Load balancing
Monitoring tools
|
|
gptkbp:provides
|
Model deployment
API for model serving
Versioning for models
|
|
gptkbp:purpose
|
Serving machine learning models
|
|
gptkbp:scales
|
Thousands of requests
|
|
gptkbp:suitable_for
|
Real-time applications
Batch inference
|
|
gptkbp:supports
|
Scalability
Batch processing
Multi-model serving
|
|
gptkbp:uses
|
gptkb:Actor
|
|
gptkbp:bfsParent
|
gptkb:Ray_project
|
|
gptkbp:bfsLayer
|
5
|