|
gptkbp:instance_of
|
gptkb:Performance_Monitoring
|
|
gptkbp:aims_to
|
Standardize benchmarking
|
|
gptkbp:collaborated_with
|
Research institutions
|
|
gptkbp:developed_by
|
gptkb:MLPerf
|
|
gptkbp:encourages
|
Collaboration among vendors
|
|
gptkbp:focuses_on
|
gptkb:machine_learning
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
MLPerf Cloud
|
|
gptkbp:includes
|
Cloud-based workloads
|
|
gptkbp:introduced_in
|
gptkb:2019
|
|
gptkbp:is_compared_to
|
Different cloud services
|
|
gptkbp:is_documented_in
|
MLPerf reports
|
|
gptkbp:is_evaluated_by
|
Performance metrics
Scalability
Cost efficiency
Performance benchmarks
Resource utilization
AI performance metrics
Cloud performance metrics
MLPerf rules
MLPerf standards
|
|
gptkbp:is_influenced_by
|
Community feedback
|
|
gptkbp:is_part_of
|
gptkb:MLPerf_suite
gptkb:MLPerf
|
|
gptkbp:is_promoted_by
|
Industry leaders
|
|
gptkbp:is_related_to
|
gptkb:Artificial_Intelligence
gptkb:cloud_computing
gptkb:Deep_Learning
gptkb:AI_technology
Data science
Machine Learning frameworks
Benchmarking standards
|
|
gptkbp:is_supported_by
|
gptkb:Microsoft_Azure
gptkb:Amazon_Web_Services
gptkb:Google_Cloud
gptkb:Open_AI
Major cloud providers
|
|
gptkbp:is_updated_by
|
Regularly
|
|
gptkbp:is_used_by
|
gptkb:researchers
Industry professionals
|
|
gptkbp:is_utilized_in
|
Academic research
Commercial applications
|
|
gptkbp:measures
|
Training performance
Inference performance
|
|
gptkbp:performance
|
Cloud AI workloads
Cloud ML workloads
Cloud inference workloads
Cloud training workloads
|
|
gptkbp:provides
|
Performance metrics
|
|
gptkbp:provides_access_to
|
Open-source tools
|
|
gptkbp:bfsParent
|
gptkb:MLPerf
|
|
gptkbp:bfsLayer
|
6
|