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update samples from Release-82 as a part of SDK release
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‎configuration.ipynb

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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/automl_env.yml

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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.20.0
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- azureml-widgets~=1.21.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.20.0/validated_win32_requirements.txt [--no-deps]
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_win32_requirements.txt [--no-deps]

‎how-to-use-azureml/automated-machine-learning/automl_env_linux.yml

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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.20.0
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- azureml-widgets~=1.21.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.20.0/validated_linux_requirements.txt [--no-deps]
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_linux_requirements.txt [--no-deps]
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‎how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.20.0
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- azureml-widgets~=1.21.0
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- pytorch-transformers==1.0.0
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- spacy==2.1.8
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.20.0/validated_darwin_requirements.txt [--no-deps]
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- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.21.0/validated_darwin_requirements.txt [--no-deps]

‎how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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"\n",
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"**Time series identifier columns** are identified by values of the columns listed `time_series_id_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
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"\n",
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"**Forecast frequency (freq)** This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information.\n",
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"\n",
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"This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
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]
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},

‎how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
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"|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
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"|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
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"|**drop_column_names**|Name(s) of columns to drop prior to modeling|"
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"|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
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"|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
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]
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},
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{

‎how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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"|Property|Description|\n",
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"|-|-|\n",
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"|**time_column_name**|The name of your time column.|\n",
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"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|"
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"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
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"|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
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]
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},
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{

‎how-to-use-azureml/automated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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"* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n",
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"* Set limitations on the length of experiment run to 15 minutes.\n",
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"* Finally, we set the task to be forecasting.\n",
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"* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones."
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"* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones.\n",
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"* [Optional] Forecast frequency parameter (freq) represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
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]
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},
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{

‎how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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"|-|-|\n",
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"|**time_column_name**|The name of your time column.|\n",
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"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
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"|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|"
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"|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|\n",
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"|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
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]
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},
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{

‎how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

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"\n",
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"# Check core SDK version number\n",
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"\n",
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"print(\"This notebook was created using SDK version 1.20.0, you are currently running version\", azureml.core.VERSION)"
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"print(\"This notebook was created using SDK version 1.21.0, you are currently running version\", azureml.core.VERSION)"
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]
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},
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{

‎setup-environment/configuration.ipynb

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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.20.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.21.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},

‎tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb

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},
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"outputs": [],
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"source": [
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"experiment_name = 'sklearn-mnist'\n",
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"experiment_name = 'Tutorial-sklearn-mnist'\n",
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"\n",
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"from azureml.core import Experiment\n",
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"exp = Experiment(workspace=ws, name=experiment_name)"

‎tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb

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"from azureml.pipeline.core import Pipeline\n",
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"\n",
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"pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n",
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"pipeline_run = Experiment(ws, \"batch_scoring\").submit(pipeline)"
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"pipeline_run = Experiment(ws, \"Tutorial-Batch-Scoring\").submit(pipeline)"
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]
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},
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{
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"rest_endpoint = published_pipeline.endpoint\n",
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"response = requests.post(rest_endpoint, \n",
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" headers=auth_header, \n",
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" json={\"ExperimentName\": \"batch_scoring\",\n",
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" json={\"ExperimentName\": \"Tutorial-Batch-Scoring\",\n",
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" \"ParameterAssignments\": {\"process_count_per_node\": 6}})"
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]
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},

‎tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb

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"outputs": [],
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"source": [
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"from azureml.core.experiment import Experiment\n",
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"experiment = Experiment(ws, \"taxi-experiment\")\n",
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"experiment = Experiment(ws, \"Tutorial-NYCTaxi\")\n",
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"local_run = experiment.submit(automl_config, show_output=True)"
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]
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},

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