|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:bfsLayer
|
5
|
|
gptkbp:bfsParent
|
gptkb:Kubeflow_Pipelines
|
|
gptkbp:allows
|
Multi-objective optimization
|
|
gptkbp:can_be_extended_by
|
Custom algorithms
|
|
gptkbp:community_support
|
gptkb:battle
|
|
gptkbp:deployment
|
Cloud platforms
On-premises environments
|
|
gptkbp:developed_by
|
gptkb:Kubeflow_Community
|
|
gptkbp:has_documentation
|
Official documentation
|
|
gptkbp:has_feature
|
Visualization tools
Experiment tracking
Trial management
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Kubeflow Katib
|
|
gptkbp:integrates_with
|
gptkb:Prometheus
gptkb:opera
gptkb:Kubeflow_Pipelines
|
|
gptkbp:is_available_on
|
gptkb:archive
|
|
gptkbp:is_compatible_with
|
gptkb:Jupyter_notebooks
Kubernetes clusters
|
|
gptkbp:is_designed_for
|
Automated machine learning
|
|
gptkbp:is_integrated_with
|
CI/ CD pipelines
|
|
gptkbp:is_open_source
|
gptkb:theorem
|
|
gptkbp:is_optimized_for
|
Machine learning models
|
|
gptkbp:is_part_of
|
Kubeflow ecosystem
ML Ops tools
ML workflow
|
|
gptkbp:is_scalable
|
gptkb:battle
|
|
gptkbp:is_supported_by
|
Cloud providers
|
|
gptkbp:is_used_by
|
Data scientists
Machine learning engineers
|
|
gptkbp:is_used_for
|
Model selection
Parameter tuning
|
|
gptkbp:is_used_in
|
Research projects
Production environments
|
|
gptkbp:provides
|
RESTAPI
CLI tools
Logging capabilities
Custom metrics support
Automated hyperparameter optimization
|
|
gptkbp:purpose
|
Hyperparameter tuning
|
|
gptkbp:released
|
Regular updates
|
|
gptkbp:suitable_for
|
Large datasets
Distributed training
|
|
gptkbp:supports
|
gptkb:fortification
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
gptkb:Scikit-learn
XG Boost
Multi-node training
|
|
gptkbp:user_interface
|
Web UI
|
|
gptkbp:uses
|
Bayesian optimization
Grid search
Random search
|
|
gptkbp:written_in
|
gptkb:Go
|