def get_child_runs(run_history_name, parent_run_id):
ws = Workspace.from_config()
run = Run(ws, run_history_name, parent_run_id)
print('parent:', run.id)
print('children:')
for c in run.get_children():
print(c.id)
def cancel_runs_in_experiment(ws, experiment):
failed_experiment = Experiment(ws, experiment)
all_runs = failed_experiment.get_runs()
for idx, run in enumerate(all_runs):
try:
if run.status == 'Running':
run = Run(failed_experiment, run.id)
print('Canceling run: ', run)
run.cancel()
except Exception as e:
print('Canceling run failed due to ', e)
def run_multiple_inferences(summary_df, train_experiment, test_experiment,
compute_target, script_folder, test_dataset,
lookback_dataset, max_horizon, target_column_name,
time_column_name, freq):
for run_name, run_summary in summary_df.iterrows():
print(run_name)
print(run_summary)
run_id = run_summary.run_id
train_run = Run(train_experiment, run_id)
test_run = run_inference(test_experiment, compute_target,
script_folder, train_run, test_dataset,
lookback_dataset, max_horizon,
target_column_name, time_column_name, freq)
print(test_run)
summary_df.loc[summary_df.run_id == run_id,
'test_run_id'] = test_run.id
return summary_df
def download_model(workspace, experiment_name, run_id, input_location,
output_location):
"""Download the pretrained model
Args:
ws: workspace to access the experiment
experiment_name: Name of the experiment in which model is saved
run_id: Run Id of the experiment in which model is pre-trained
input_location: Input location in a RUN Id
output_location: Location for saving the model
"""
experiment = Experiment(workspace=workspace, name=experiment_name)
# Download the model on which evaluation need to be done
run = Run(experiment, run_id=run_id)
if input_location.endswith(".h5"):
run.download_file(input_location, output_location)
elif input_location.endswith(".ckpt"):
run.download_files(prefix=input_location,
output_directory=output_location)
else:
raise NameError(f"{input_location}'s path extension not supported")
logger.info("Successfully downloaded model")
automl_setup_model_explanations, automl_check_model_if_explainable
from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
from azureml.explain.model.mimic_wrapper import MimicWrapper
from automl.client.core.common.constants import MODEL_PATH
from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save
OUTPUT_DIR = './outputs/'
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Get workspace from the run context
run = Run.get_context()
ws = run.experiment.workspace
# Get the AutoML run object from the experiment name and the workspace
experiment = Experiment(ws, '<<experiment_name>>')
automl_run = Run(experiment=experiment, run_id='<<run_id>>')
# Check if this AutoML model is explainable
if not automl_check_model_if_explainable(automl_run):
raise Exception("Model explanations is currently not supported for " +
automl_run.get_properties().get('run_algorithm'))
# Download the best model from the artifact store
automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
# Load the AutoML model into memory
fitted_model = joblib.load('model.pkl')
# Get the train dataset from the workspace
train_dataset = Dataset.get_by_name(workspace=ws,
name='<<train_dataset_name>>')
def download_pretrained_model(workspace: Workspace, output_model_fpath: str):
print(f"Downloading pretrained model from {CONFIG.PRETRAINED_RUN}")
previous_experiment = Experiment(workspace=workspace, name=CONFIG.PRETRAINED_EXPERIMENT)
previous_run = Run(previous_experiment, CONFIG.PRETRAINED_RUN)
previous_run.download_file(f"outputs/{MODEL_H5_FILENAME}", output_model_fpath)
nargs='+',
default=None,
help='runids to create')
return parser.parse_args()
args = parse_args()
print(args)
if args.runids:
# get workspace
ws = Workspace.from_config()
# set the expiriment
experiment_name = 'test'
exp = Experiment(workspace=ws, name=experiment_name)
runs = []
for idx in args.runids:
run = Run(exp, idx)
runs.append(run)
tb = Tensorboard(runs)
tb.start()
## Wait for input to stop tensorboard.
print('Enter to stop tensorboard')
input()
tb.stop()
def download_pretrained_model(output_model_fpath):
print(f"Downloading pretrained model from {CONFIG.PRETRAINED_RUN}")
previous_experiment = Experiment(workspace=workspace, name=CONFIG.PRETRAINED_EXPERIMENT)
previous_run = Run(previous_experiment, CONFIG.PRETRAINED_RUN)
previous_run.download_file("outputs/best_model.h5", output_model_fpath)
from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer
from azureml.train.automl.runtime.automl_explain_utilities import (
automl_setup_model_explanations,
automl_check_model_if_explainable,
)
OUTPUT_DIR = "./outputs/"
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Get workspace from the run context
run = Run.get_context()
ws = run.experiment.workspace
# Get the AutoML run object from the experiment name and the workspace
experiment = Experiment(ws, "<<experiment_name>>")
automl_run = Run(experiment=experiment, run_id="<<run_id>>")
# Check if this AutoML model is explainable
if not automl_check_model_if_explainable(automl_run):
raise Exception("Model explanations are currently not supported for " +
automl_run.get_properties().get("run_algorithm"))
# Download the best model from the artifact store
automl_run.download_file(name=MODEL_PATH, output_file_path="model.pkl")
# Load the AutoML model into memory
fitted_model = joblib.load("model.pkl")
# Get the train dataset from the workspace
train_dataset = Dataset.get_by_name(workspace=ws,
name="<<train_dataset_name>>")
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
Cancel pipeline run
"""
from azureml.core.run import Run
from azureml.core import Workspace, Experiment
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--run_id", type=str, help="run id value", required=True)
parser.add_argument("--step_id", type=str, help="step id value", required=True)
args = parser.parse_args()
ws = Workspace.from_config()
experiment = Experiment(workspace=ws,
name="DEV-train-pipeline",
_id=args.run_id)
fetched_run = Run(experiment=experiment, run_id=args.step_id)
fetched_run.cancel()
def get_run(run_id):
ws = Workspace.from_config()
print(ws.name, ws.resource_group)
from azureml.core.run import Run
run = Run(workspace=ws, run_history_name='e2e-07', run_id = run_id)
return run
automl_setup_model_explanations
from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
from azureml.explain.model.mimic_wrapper import MimicWrapper
from automl.client.core.common.constants import MODEL_PATH
from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save
OUTPUT_DIR = './outputs/'
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Get workspace from the run context
run = Run.get_context()
ws = run.experiment.workspace
# Get the AutoML run object from the experiment name and the workspace
experiment = Experiment(ws, '<<experimnet_name>>')
automl_run = Run(experiment=experiment,
run_id='AutoML_176c6b1a-fbf8-4f52-a4c9-36ea019addc1_18')
# Download the best model from the artifact store
automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
# Load the AutoML model into memory
fitted_model = joblib.load('model.pkl')
# Get the train dataset from the workspace
train_dataset = Dataset.get_by_name(workspace=ws,
name='machineData_train_dataset')
# Drop the lablled column to get the training set.
X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'],
validate=True)
