|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:can_be_used_with
|
Custom algorithms
|
|
gptkbp:developed_by
|
gptkb:server
|
|
gptkbp:enables
|
Faster model convergence
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Sage Maker Training Compiler
|
|
gptkbp:improves
|
Training performance
|
|
gptkbp:integrates_with
|
gptkb:temple
|
|
gptkbp:is_accessible_by
|
gptkb:AWS_Management_Console
Web interface
AWSCLI
AWSSD Ks
|
|
gptkbp:is_available_in
|
Multiple AWS regions
|
|
gptkbp:is_compatible_with
|
gptkb:Jupyter_notebooks
|
|
gptkbp:is_designed_for
|
Data scientists
Machine learning engineers
|
|
gptkbp:is_effective_against
|
Large scale training
|
|
gptkbp:is_integrated_with
|
gptkb:aircraft
gptkb:AWS_Step_Functions
|
|
gptkbp:is_optimized_for
|
Deep learning frameworks
NVIDIAGP Us
|
|
gptkbp:is_part_of
|
AWS cloud services
AWSAI services
Sage Maker ecosystem
|
|
gptkbp:is_scalable
|
Large datasets
|
|
gptkbp:is_used_by
|
gptkb:physicist
Enterprises
Startups
|
|
gptkbp:is_used_for
|
Transfer learning
Hyperparameter optimization
|
|
gptkbp:offers
|
gptkb:Community_support
Built-in algorithms
Distributed training capabilities
|
|
gptkbp:provides
|
gptkb:document
Profiling tools
Model monitoring tools
Model tuning capabilities
Automatic mixed precision training
|
|
gptkbp:purpose
|
Optimize training of deep learning models
|
|
gptkbp:reduces
|
Training costs
|
|
gptkbp:security_features
|
AWS security features
|
|
gptkbp:supports
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
Real-time inference
Model deployment
Batch training
Multi-instance training
Containerized training jobs
|
|
gptkbp:updates
|
AWS updates
|
|
gptkbp:user_experience
|
gptkb:battle
|
|
gptkbp:uses
|
Graph optimization techniques
|
|
gptkbp:bfsParent
|
gptkb:AWS_Sage_Maker
|
|
gptkbp:bfsLayer
|
4
|