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")
# 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>>')
# 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)
# Get the train dataset from the workspace
test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
# Drop the lablled column to get the testing set.
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)
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)
