""" Copyright (C) Microsoft Corporation. All rights reserved.​ ​ Microsoft Corporation (“Microsoft”) grants you a nonexclusive, perpetual, royalty-free right to use, copy, and modify the software code provided by us ("Software Code"). You may not sublicense the Software Code or any use of it (except to your affiliates and to vendors to perform work on your behalf) through distribution, network access, service agreement, lease, rental, or otherwise. This license does not purport to express any claim of ownership over data you may have shared with Microsoft in the creation of the Software Code. Unless applicable law gives you more rights, Microsoft reserves all other rights not expressly granted herein, whether by implication, estoppel or otherwise. ​ ​ THE SOFTWARE CODE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL MICROSOFT OR ITS LICENSORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THE SOFTWARE CODE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import pickle from azureml.core import Workspace from azureml.core.run import Run import os import argparse from sklearn.datasets import load_diabetes from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from sklearn.externals import joblib import numpy as np import json import subprocess from typing import Tuple, List parser = argparse.ArgumentParser("train") parser.add_argument( "--config_suffix", type=str, help="Datetime suffix for json config files" ) parser.add_argument( "--json_config", type=str, help="Directory to write all the intermediate json configs", ) parser.add_argument( "--model_name", type=str, help="Name of the Model", default="sklearn_regression_model.pkl", ) args = parser.parse_args() print("Argument 1: %s" % args.config_suffix) print("Argument 2: %s" % args.json_config) model_name = args.model_name if not (args.json_config is None): os.makedirs(args.json_config, exist_ok=True) print("%s created" % args.json_config) run = Run.get_context() exp = run.experiment ws = run.experiment.workspace X, y = load_diabetes(return_X_y=True) columns = ["age", "gender", "bmi", "bp", "s1", "s2", "s3", "s4", "s5", "s6"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) data = {"train": {"X": X_train, "y": y_train}, "test": {"X": X_test, "y": y_test}} print("Running train.py") # Randomly pic alpha alphas = np.arange(0.0, 1.0, 0.05) alpha = alphas[np.random.choice(alphas.shape[0], 1, replace=False)][0] print(alpha) run.log("alpha", alpha) reg = Ridge(alpha=alpha) reg.fit(data["train"]["X"], data["train"]["y"]) preds = reg.predict(data["test"]["X"]) run.log("mse", mean_squared_error(preds, data["test"]["y"])) # Save model as part of the run history # model_name = "." with open(model_name, "wb") as file: joblib.dump(value=reg, filename=model_name) # upload the model file explicitly into artifacts run.upload_file(name="./outputs/" + model_name, path_or_stream=model_name) print("Uploaded the model {} to experiment {}".format(model_name, run.experiment.name)) dirpath = os.getcwd() print(dirpath) print("Following files are uploaded ") print(run.get_file_names()) # register the model # run.log_model(file_name = model_name) # print('Registered the model {} to run history {}'.format(model_name, run.history.name)) run_id = {} run_id["run_id"] = run.id run_id["experiment_name"] = run.experiment.name filename = "run_id_{}.json".format(args.config_suffix) output_path = os.path.join(args.json_config, filename) with open(output_path, "w") as outfile: json.dump(run_id, outfile) run.complete()