Metadata-Version: 2.1
Name: servz
Version: 0.0.0.38
Summary: DO NOT USE - This is a sample program
Home-page: https://github.com/hashmapinc/servz
Author: Hashmap, and NTT DATA Services Company
Author-email: accelerators@hashmapinc.com
License: UNKNOWN
Description: <!---
        # Modifications © 2020 Hashmap, Inc
        #
        # Licensed under the Apache License, Version 2.0 (the "License");
        # you may not use this file except in compliance with the License.
        # You may obtain a copy of the License at
        #
        #     http://www.apache.org/licenses/LICENSE-2.0
        #
        # Unless required by applicable law or agreed to in writing, software
        # distributed under the License is distributed on an "AS IS" BASIS,
        # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        # See the License for the specific language governing permissions and
        # limitations under the License.
        -->
        
        # 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)
        
        ```buildoutcfg
        "pipelines": self._pipelines
        
        return [self.__build_flow(pipeline) for pipeline in flows['pipelines']]
        
        self._pipelines.append(pipeline)
        ```
        
        ### Artifact Builder
        
        ```buildoutcfg
        "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
        
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.6
Description-Content-Type: text/markdown
