lib.sedna.core.joint_inference
¶
Submodules¶
Package Contents¶
Classes¶
Sedna provide a framework make sure under the condition of limited |
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Large model services implemented |
- class lib.sedna.core.joint_inference.JointInference(estimator=None, hard_example_mining: dict = None)[source]¶
Bases:
sedna.core.base.JobBase
Sedna provide a framework make sure under the condition of limited resources on the edge, difficult inference tasks are offloaded to the cloud to improve the overall performance, keeping the throughput.
- Parameters:
estimator (Instance) – An instance with the high-level API that greatly simplifies machine learning programming. Estimators encapsulate training, evaluation, prediction, and exporting for your model.
hard_example_mining (Dict) – HEM algorithms with parameters which has registered to ClassFactory, see sedna.algorithms.hard_example_mining for more detail.
Examples
>>> Estimator = keras.models.Sequential() >>> ji_service = JointInference( estimator=Estimator, hard_example_mining={ "method": "IBT", "param": { "threshold_img": 0.9 } } )
Notes
Sedna provide an interface call get_hem_algorithm_from_config to build the hard_example_mining parameter from CRD definition.
- classmethod get_hem_algorithm_from_config(**param)[source]¶
get the algorithm name and param of hard_example_mining from crd
- Parameters:
param (Dict) – update value in parameters of hard_example_mining
- Returns:
e.g.: {“method”: “IBT”, “param”: {“threshold_img”: 0.5}}
- Return type:
dict
Examples
>>> JointInference.get_hem_algorithm_from_config( threshold_img=0.9 ) {"method": "IBT", "param": {"threshold_img": 0.9}}
- inference(data=None, post_process=None, **kwargs)[source]¶
Inference task with JointInference
- Parameters:
data (BaseDataSource) – datasource use for inference, see sedna.datasources.BaseDataSource for more detail.
post_process (function or a registered method) – effected after estimator inference.
kwargs (Dict) – parameters for estimator inference, Like: ntree_limit in Xgboost.XGBClassifier
- Returns:
if is hard sample (bool)
inference result (object)
result from little-model (object)
result from big-model (object)
- class lib.sedna.core.joint_inference.BigModelService(estimator=None)[source]¶
Bases:
sedna.core.base.JobBase
Large model services implemented Provides RESTful interfaces for large-model inference.
- Parameters:
estimator (Instance, big model) – An instance with the high-level API that greatly simplifies machine learning programming. Estimators encapsulate training, evaluation, prediction, and exporting for your model.
Examples
>>> Estimator = xgboost.XGBClassifier() >>> BigModelService(estimator=Estimator).start()
- inference(data=None, post_process=None, **kwargs)[source]¶
Inference task for JointInference
- Parameters:
data (BaseDataSource) – datasource use for inference, see sedna.datasources.BaseDataSource for more detail.
post_process (function or a registered method) – effected after estimator inference.
kwargs (Dict) – parameters for estimator inference, Like: ntree_limit in Xgboost.XGBClassifier
- Return type:
inference result