kubeflow.fairing.ml_tasks package¶
Submodules¶
kubeflow.fairing.ml_tasks.tasks module¶
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class
kubeflow.fairing.ml_tasks.tasks.
BaseTask
(entry_point, base_docker_image=None, docker_registry=None, input_files=None, backend=None, pod_spec_mutators=None)¶ Bases:
object
Base class for handling high level ML tasks.
Parameters: - entry_point – An object or reference to the source code that has to be deployed.
- base_docker_image – Name of the base docker image that should be used as a base image when building a new docker image as part of an ML task deployment.
- docker_registry – Docker registry to store output docker images.
- input_files – list of files that needs to be packaged along with the entry point. E.g. local python modules, trained model weigths, etc.
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class
kubeflow.fairing.ml_tasks.tasks.
PredictionEndpoint
(model_class, base_docker_image=None, docker_registry=None, input_files=None, backend=None, service_type='ClusterIP', pod_spec_mutators=None)¶ Bases:
kubeflow.fairing.ml_tasks.tasks.BaseTask
Create a prediction endpoint.
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create
()¶ Create prediction endpoint.
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delete
()¶ Delete prediction endpoint.
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predict_nparray
(data, feature_names=None)¶ Return the prediction result.
Parameters: - data – Data to be predicted.
- feature_names – Feature extracted from data (Default value = None)
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class
kubeflow.fairing.ml_tasks.tasks.
TrainJob
(entry_point, base_docker_image=None, docker_registry=None, input_files=None, backend=None, pod_spec_mutators=None)¶ Bases:
kubeflow.fairing.ml_tasks.tasks.BaseTask
Create a train job.
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submit
()¶ Submit a train job.
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kubeflow.fairing.ml_tasks.utils module¶
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kubeflow.fairing.ml_tasks.utils.
guess_preprocessor
(entry_point, input_files, output_map)¶ Preprocessor to use to modify inputs before sending them to docker build
Parameters: - entry_point – entry_point which to use
- input_files – input files
- output_map – output
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kubeflow.fairing.ml_tasks.utils.
is_docker_daemon_exists
()¶ To check if docker daemon exists or not.