ml/registry.py (34 lines of code) (raw):

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. # """ ML registry Registry object that will keep information about available algorithms and corresponding endpoints. """ from ml.classifiers import Classifier from typing import Dict from api.models import Algorithm, Dataset def has_empty_values(data: dict): for key, value in data.items(): if value == None: raise ValueError(f'{key} cannot be null') return False class MLRegistry: def __init__(self): self.classifiers: Dict[int, Classifier] = {} def add_algorithms(self, attrs=[{ "classifier": None, "description": None, "status": None, "version": None, "dataset": None, "region": None, "created_by": None }]): for attr in attrs: if not has_empty_values(attr): #get dataset dataset, _ = Dataset.objects.get_or_create( name=attr['dataset'], region=attr['region']) # get algorithm algorithm, _ = Algorithm.objects.get_or_create( classifier=attr['classifier'].__class__.__name__, description=attr['description'], version=attr['version'], status=attr['status'], dataset=dataset, created_by=attr['created_by']) self.classifiers[algorithm.id] = attr['classifier'] return self.classifiers