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update samples from Release-72 as a part of SDK release

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amlrelsa-ms
2020-10-28 21:24:40 +00:00
parent 58e584e7eb
commit 7871e37ec0
2 changed files with 13 additions and 4 deletions
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2
README.md
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@@ -1,7 +1,5 @@
# Azure Machine Learning service example notebooks # Azure Machine Learning service example notebooks
> A community-driven repository of training and scoring examples can be found at https://github.com/Azure/azureml-examples
This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure. The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud. This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure. The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png) ![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)

15
how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py
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@@ -10,6 +10,8 @@ from sklearn.metrics import mean_absolute_error, mean_squared_error
from azureml.automl.runtime.shared.score import scoring, constants from azureml.automl.runtime.shared.score import scoring, constants
from azureml.core import Run from azureml.core import Run
import torch
def align_outputs(y_predicted, X_trans, X_test, y_test, def align_outputs(y_predicted, X_trans, X_test, y_test,
predicted_column_name='predicted', predicted_column_name='predicted',
@@ -221,6 +223,10 @@ def MAPE(actual, pred):
return np.mean(APE(actual_safe, pred_safe)) return np.mean(APE(actual_safe, pred_safe))
def map_location_cuda(storage, loc):
return storage.cuda()
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument( parser.add_argument(
'--max_horizon', type=int, dest='max_horizon', '--max_horizon', type=int, dest='max_horizon',
@@ -274,8 +280,13 @@ X_lookback_df = lookback_dataset.drop_columns(columns=[target_column_name])
y_lookback_df = lookback_dataset.with_timestamp_columns( y_lookback_df = lookback_dataset.with_timestamp_columns(
None).keep_columns(columns=[target_column_name]) None).keep_columns(columns=[target_column_name])
fitted_model = joblib.load(model_path) # Load the trained model with torch.
if torch.cuda.is_available():
map_location = map_location_cuda
else:
map_location = 'cpu'
with open(model_path, 'rb') as fh:
fitted_model = torch.load(fh, map_location=map_location)
if hasattr(fitted_model, 'get_lookback'): if hasattr(fitted_model, 'get_lookback'):
lookback = fitted_model.get_lookback() lookback = fitted_model.get_lookback()