servz 0.0.0.27

DO NOT USE - This is a sample program

Servz

Machine Learning Model Serving

This library is a pre-alpha right now

The intent of this package is to provide a machine learning deplpoyment layer for model predictions.

Features

• pipeline driven
• deployment via task runner
• deployment via flask or other endpoint
• MLFlow based deployment
• Seldon deployment (in progress)

Pipeline formats

Pipeline Composer

Appends all the pipelines from the yaml file(s)

"pipelines": self._pipelines

return [self.__build_flow(pipeline) for pipeline in flows['pipelines']]

self._pipelines.append(pipeline)

Artifact Builder

"artifact": self._workflows

_res = self._build(pipelines=kwargs.get('packager'))

results = [self._compile_workflow(pipe) for pipe in pipelines]
        self._workflows = pipelines

for task in pipe['workflow']:
    do stuff
success = self.__build_prefect_flow()
return {
            'success': success,
        }

Endpoint Appender

Packager

Package Publisher

structure

/core - core files for servz
/orchestration_artifact_builder - artifact packager
/orchestration_artifact_deployer - deployment runner
/packager - manifest packager
/pipeline - loading and validation of serving pipeline
/server_templates - artifacts for artifact builder to construct endpoints
/tests - unit tests and e2e tests

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