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41
SECURITY.md
Normal file
41
SECURITY.md
Normal file
@@ -0,0 +1,41 @@
|
||||
<!-- BEGIN MICROSOFT SECURITY.MD V0.0.7 BLOCK -->
|
||||
|
||||
## Security
|
||||
|
||||
Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
|
||||
|
||||
If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below.
|
||||
|
||||
## Reporting Security Issues
|
||||
|
||||
**Please do not report security vulnerabilities through public GitHub issues.**
|
||||
|
||||
Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report).
|
||||
|
||||
If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey).
|
||||
|
||||
You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc).
|
||||
|
||||
Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
|
||||
|
||||
* Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
|
||||
* Full paths of source file(s) related to the manifestation of the issue
|
||||
* The location of the affected source code (tag/branch/commit or direct URL)
|
||||
* Any special configuration required to reproduce the issue
|
||||
* Step-by-step instructions to reproduce the issue
|
||||
* Proof-of-concept or exploit code (if possible)
|
||||
* Impact of the issue, including how an attacker might exploit the issue
|
||||
|
||||
This information will help us triage your report more quickly.
|
||||
|
||||
If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs.
|
||||
|
||||
## Preferred Languages
|
||||
|
||||
We prefer all communications to be in English.
|
||||
|
||||
## Policy
|
||||
|
||||
Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd).
|
||||
|
||||
<!-- END MICROSOFT SECURITY.MD BLOCK -->
|
||||
@@ -103,7 +103,7 @@
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -367,9 +367,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -525,9 +525,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -599,9 +599,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -6,7 +6,7 @@ dependencies:
|
||||
- fairlearn>=0.6.2
|
||||
- joblib
|
||||
- liac-arff
|
||||
- raiwidgets~=0.19.0
|
||||
- raiwidgets~=0.22.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- protobuf==3.20.0
|
||||
|
||||
@@ -523,9 +523,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -6,7 +6,7 @@ dependencies:
|
||||
- fairlearn>=0.6.2
|
||||
- joblib
|
||||
- liac-arff
|
||||
- raiwidgets~=0.19.0
|
||||
- raiwidgets~=0.22.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- protobuf==3.20.0
|
||||
|
||||
@@ -18,16 +18,19 @@ dependencies:
|
||||
- pywin32==227
|
||||
- PySocks==1.7.1
|
||||
- conda-forge::pyqt==5.12.3
|
||||
- jsonschema==4.9.1
|
||||
- Pygments==2.12.0
|
||||
- jinja2<=2.11.2
|
||||
- markupsafe<2.1.0
|
||||
- tqdm==4.64.1
|
||||
- jsonschema==4.16.0
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.44.0
|
||||
- azureml-widgets~=1.46.0
|
||||
- azureml-defaults~=1.46.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.2.4
|
||||
- pystan==2.19.1.1
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.44.0/validated_win32_requirements.txt [--no-deps]
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.46.0/validated_win32_requirements.txt [--no-deps]
|
||||
- arch==4.14
|
||||
- wasabi==0.9.1
|
||||
|
||||
@@ -21,13 +21,17 @@ dependencies:
|
||||
- conda-forge::fbprophet==0.7.1
|
||||
- pytorch::pytorch=1.4.0
|
||||
- cudatoolkit=10.1.243
|
||||
- jinja2<=2.11.2
|
||||
- markupsafe<2.1.0
|
||||
- jsonschema==4.15.0
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.44.0
|
||||
- azureml-widgets~=1.46.0
|
||||
- azureml-defaults~=1.46.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.2.4
|
||||
- pystan==2.19.1.1
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.44.0/validated_linux_requirements.txt [--no-deps]
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.46.0/validated_linux_requirements.txt [--no-deps]
|
||||
- arch==4.14
|
||||
|
||||
@@ -22,13 +22,17 @@ dependencies:
|
||||
- conda-forge::fbprophet==0.7.1
|
||||
- pytorch::pytorch=1.4.0
|
||||
- cudatoolkit=9.0
|
||||
- jinja2<=2.11.2
|
||||
- markupsafe<2.1.0
|
||||
- jsonschema==4.15.0
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.44.0
|
||||
- azureml-widgets~=1.46.0
|
||||
- azureml-defaults~=1.46.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.2.4
|
||||
- pystan==2.19.1.1
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.44.0/validated_darwin_requirements.txt [--no-deps]
|
||||
- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.46.0/validated_darwin_requirements.txt [--no-deps]
|
||||
- arch==4.14
|
||||
|
||||
@@ -1060,9 +1060,9 @@
|
||||
"name": "python3-azureml"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -456,9 +456,9 @@
|
||||
"friendly_name": "Classification of credit card fraudulent transactions using Automated ML",
|
||||
"index_order": 5,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -567,9 +567,9 @@
|
||||
"friendly_name": "DNN Text Featurization",
|
||||
"index_order": 2,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -564,9 +564,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -324,9 +324,9 @@
|
||||
"hash": "adb464b67752e4577e3dc163235ced27038d19b7d88def00d75d1975bde5d9ab"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -713,9 +713,9 @@
|
||||
"hash": "adb464b67752e4577e3dc163235ced27038d19b7d88def00d75d1975bde5d9ab"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -3,11 +3,11 @@ dependencies:
|
||||
# The python interpreter version.
|
||||
# Currently Azure ML only supports 3.6.0 and later.
|
||||
- pip<=20.2.4
|
||||
- python>=3.6.0,<3.9
|
||||
- python>=3.6.0,<3.10
|
||||
- cython==0.29.14
|
||||
- urllib3==1.26.7
|
||||
- PyJWT < 2.0.0
|
||||
- numpy==1.21.6
|
||||
- numpy==1.22.3
|
||||
- pywin32==227
|
||||
- cryptography<37.0.0
|
||||
|
||||
@@ -18,4 +18,7 @@ dependencies:
|
||||
- azureml-defaults
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- azureml-mlflow
|
||||
- pandas
|
||||
- mlflow
|
||||
- docker<6.0.0
|
||||
|
||||
@@ -7,7 +7,7 @@ dependencies:
|
||||
# Currently Azure ML only supports 3.6.0 and later.
|
||||
- pip<=20.2.4
|
||||
- nomkl
|
||||
- python>=3.6.0,<3.9
|
||||
- python>=3.6.0,<3.10
|
||||
- urllib3==1.26.7
|
||||
- PyJWT < 2.0.0
|
||||
- numpy>=1.21.6,<=1.22.3
|
||||
@@ -20,4 +20,6 @@ dependencies:
|
||||
- azureml-defaults
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- azureml-mlflow
|
||||
- pandas
|
||||
- mlflow
|
||||
|
||||
@@ -92,7 +92,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -354,7 +354,7 @@
|
||||
"This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
|
||||
"The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u0192\u00c2\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net and the page of the DefeatFraud project\n",
|
||||
"Please cite the following works: \n",
|
||||
"\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
|
||||
"\u00c3\u00a2\u00e2\u201a\u00ac\u00c2\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
|
||||
@@ -389,9 +389,9 @@
|
||||
"friendly_name": "Classification of credit card fraudulent transactions using Automated ML",
|
||||
"index_order": 5,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -91,7 +91,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -448,9 +448,9 @@
|
||||
"automated-machine-learning"
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -324,7 +324,8 @@
|
||||
"| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
|
||||
"| **label_column_name** | The name of the label column. |\n",
|
||||
"| **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). Periods are inferred from your data. |\n",
|
||||
"| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
|
||||
"| **n_cross_validations** | Number of cross validation splits. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"| **time_column_name** | The name of your time column. |\n",
|
||||
"| **time_series_id_column_names** | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |\n",
|
||||
"| **track_child_runs** | Flag to disable tracking of child runs. Only best run is tracked if the flag is set to False (this includes the model and metrics of the run). |\n",
|
||||
@@ -353,7 +354,8 @@
|
||||
" \"iterations\": 15,\n",
|
||||
" \"experiment_timeout_hours\": 0.25, # This also needs to be changed based on the dataset. For larger data set this number needs to be bigger.\n",
|
||||
" \"label_column_name\": TARGET_COLNAME,\n",
|
||||
" \"n_cross_validations\": 3,\n",
|
||||
" \"n_cross_validations\": \"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" \"cv_step_size\": \"auto\",\n",
|
||||
" \"time_column_name\": TIME_COLNAME,\n",
|
||||
" \"forecast_horizon\": 6,\n",
|
||||
" \"time_series_id_column_names\": partition_column_names,\n",
|
||||
@@ -704,9 +706,9 @@
|
||||
"automated-machine-learning"
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -718,7 +720,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -283,7 +283,8 @@
|
||||
"| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
|
||||
"| **label_column_name** | The name of the label column. |\n",
|
||||
"| **max_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). Periods are inferred from your data. |\n",
|
||||
"| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
|
||||
"| **n_cross_validations** | Number of cross validation splits. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"| **time_column_name** | The name of your time column. |\n",
|
||||
"| **grain_column_names** | The column names used to uniquely identify timeseries in data that has multiple rows with the same timestamp. |"
|
||||
]
|
||||
@@ -301,7 +302,8 @@
|
||||
" \"iterations\": 15,\n",
|
||||
" \"experiment_timeout_hours\": 1, # This also needs to be changed based on the dataset. For larger data set this number needs to be bigger.\n",
|
||||
" \"label_column_name\": LABEL_COLUMN_NAME,\n",
|
||||
" \"n_cross_validations\": 3,\n",
|
||||
" \"n_cross_validations\": \"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" \"cv_step_size\": \"auto\",\n",
|
||||
" \"time_column_name\": TIME_COLUMN_NAME,\n",
|
||||
" \"max_horizon\": FORECAST_HORIZON,\n",
|
||||
" \"track_child_runs\": False,\n",
|
||||
@@ -698,9 +700,9 @@
|
||||
"Azure ML AutoML"
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -712,7 +714,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -265,7 +265,8 @@
|
||||
"|**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",
|
||||
"|**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",
|
||||
"|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
|
||||
"|**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."
|
||||
"|**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.\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -285,7 +286,7 @@
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
"|**compute_target**|The remote compute for training.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"|**enable_early_stopping**|If early stopping is on, training will stop when the primary metric is no longer improving.|\n",
|
||||
"|**forecasting_parameters**|A class that holds all the forecasting related parameters.|\n",
|
||||
"\n",
|
||||
@@ -350,6 +351,7 @@
|
||||
" country_or_region_for_holidays=\"US\", # set country_or_region will trigger holiday featurizer\n",
|
||||
" target_lags=\"auto\", # use heuristic based lag setting\n",
|
||||
" freq=\"D\", # Set the forecast frequency to be daily\n",
|
||||
" cv_step_size=\"auto\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(\n",
|
||||
@@ -362,7 +364,7 @@
|
||||
" label_column_name=target_column_name,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" enable_early_stopping=True,\n",
|
||||
" n_cross_validations=3,\n",
|
||||
" n_cross_validations=\"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" max_concurrent_iterations=4,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
@@ -695,9 +697,9 @@
|
||||
"friendly_name": "Forecasting BikeShare Demand",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -709,7 +711,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
@@ -719,7 +721,12 @@
|
||||
"Forecasting"
|
||||
],
|
||||
"task": "Forecasting",
|
||||
"version": 3
|
||||
"version": 3,
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
|
||||
@@ -308,7 +308,8 @@
|
||||
"|-|-|\n",
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**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",
|
||||
"|**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."
|
||||
"|**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.\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -328,7 +329,7 @@
|
||||
"|**training_data**|The training data to be used within the experiment.|\n",
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
"|**compute_target**|The remote compute for training.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
|
||||
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\n",
|
||||
"|**forecasting_parameters**|A class holds all the forecasting related parameters.|\n"
|
||||
]
|
||||
@@ -352,6 +353,7 @@
|
||||
" time_column_name=time_column_name,\n",
|
||||
" forecast_horizon=forecast_horizon,\n",
|
||||
" freq=\"H\", # Set the forecast frequency to be hourly\n",
|
||||
" cv_step_size=\"auto\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(\n",
|
||||
@@ -363,7 +365,7 @@
|
||||
" label_column_name=target_column_name,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" enable_early_stopping=True,\n",
|
||||
" n_cross_validations=3,\n",
|
||||
" n_cross_validations=\"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" forecasting_parameters=forecasting_parameters,\n",
|
||||
")"
|
||||
@@ -609,6 +611,7 @@
|
||||
" forecast_horizon=forecast_horizon,\n",
|
||||
" target_lags=12,\n",
|
||||
" target_rolling_window_size=4,\n",
|
||||
" cv_step_size=\"auto\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(\n",
|
||||
@@ -628,7 +631,7 @@
|
||||
" label_column_name=target_column_name,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" enable_early_stopping=True,\n",
|
||||
" n_cross_validations=3,\n",
|
||||
" n_cross_validations=\"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" forecasting_parameters=advanced_forecasting_parameters,\n",
|
||||
")"
|
||||
@@ -764,9 +767,9 @@
|
||||
"automated-machine-learning"
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -778,7 +781,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -335,7 +335,8 @@
|
||||
" forecast_horizon=forecast_horizon,\n",
|
||||
" time_series_id_column_names=[TIME_SERIES_ID_COLUMN_NAME],\n",
|
||||
" target_lags=lags,\n",
|
||||
" freq=\"H\", # Set the forecast frequency to be hourly\n",
|
||||
" freq=\"H\", # Set the forecast frequency to be hourly,\n",
|
||||
" cv_step_size=\"auto\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
@@ -365,7 +366,7 @@
|
||||
" enable_early_stopping=True,\n",
|
||||
" training_data=train_data,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" n_cross_validations=3,\n",
|
||||
" n_cross_validations=\"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" max_concurrent_iterations=4,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
@@ -865,9 +866,9 @@
|
||||
"friendly_name": "Forecasting away from training data",
|
||||
"index_order": 3,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -879,13 +880,18 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"tags": [
|
||||
"Forecasting",
|
||||
"Confidence Intervals"
|
||||
],
|
||||
"task": "Forecasting"
|
||||
"task": "Forecasting",
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
|
||||
@@ -681,9 +681,9 @@
|
||||
],
|
||||
"hide_code_all_hidden": false,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -263,7 +263,8 @@
|
||||
"| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
|
||||
"| **label_column_name** | The name of the label column. |\n",
|
||||
"| **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). Periods are inferred from your data. |\n",
|
||||
"| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
|
||||
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"| **enable_early_stopping** | Flag to enable early termination if the score is not improving in the short term. |\n",
|
||||
"| **time_column_name** | The name of your time column. |\n",
|
||||
"| **hierarchy_column_names** | The names of columns that define the hierarchical structure of the data from highest level to most granular. |\n",
|
||||
@@ -311,10 +312,11 @@
|
||||
" \"track_child_runs\": False,\n",
|
||||
" \"pipeline_fetch_max_batch_size\": 15,\n",
|
||||
" \"model_explainability\": model_explainability,\n",
|
||||
" \"n_cross_validations\": \"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" \"cv_step_size\": \"auto\",\n",
|
||||
" # The following settings are specific to this sample and should be adjusted according to your own needs.\n",
|
||||
" \"iteration_timeout_minutes\": 10,\n",
|
||||
" \"iterations\": 10,\n",
|
||||
" \"n_cross_validations\": 2,\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"hts_parameters = HTSTrainParameters(\n",
|
||||
@@ -618,9 +620,9 @@
|
||||
"automated-machine-learning"
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -391,7 +391,8 @@
|
||||
"| **experiment_timeout_hours** | Maximum amount of time in hours that the experiment can take before it terminates. This is optional but provides customers with greater control on exit criteria. |\n",
|
||||
"| **label_column_name** | The name of the label column. |\n",
|
||||
"| **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). Periods are inferred from your data. |\n",
|
||||
"| **n_cross_validations** | Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
|
||||
"| **n_cross_validations** | Number of cross validation splits. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value. Rolling Origin Validation is used to split time-series in a temporally consistent way. |\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"| **enable_early_stopping** | Flag to enable early termination if the score is not improving in the short term. |\n",
|
||||
"| **time_column_name** | The name of your time column. |\n",
|
||||
"| **enable_engineered_explanations** | Engineered feature explanations will be downloaded if enable_engineered_explanations flag is set to True. By default it is set to False to save storage space. |\n",
|
||||
@@ -423,7 +424,8 @@
|
||||
" \"iterations\": 15,\n",
|
||||
" \"experiment_timeout_hours\": 0.25,\n",
|
||||
" \"label_column_name\": \"Quantity\",\n",
|
||||
" \"n_cross_validations\": 3,\n",
|
||||
" \"n_cross_validations\": \"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" \"cv_step_size\": \"auto\",\n",
|
||||
" \"time_column_name\": \"WeekStarting\",\n",
|
||||
" \"drop_column_names\": \"Revenue\",\n",
|
||||
" \"forecast_horizon\": 6,\n",
|
||||
@@ -835,9 +837,9 @@
|
||||
"automated-machine-learning"
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -849,7 +851,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
|
Before Width: | Height: | Size: 2.6 MiB After Width: | Height: | Size: 2.6 MiB |
@@ -368,7 +368,8 @@
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**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",
|
||||
"|**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",
|
||||
"|**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."
|
||||
"|**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.\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -390,7 +391,7 @@
|
||||
"In the first case, AutoML loops over all time-series in your dataset and trains one model (e.g. AutoArima or Prophet, as the case may be) for each series. This can result in long runtimes to train these models if there are a lot of series in the data. One way to mitigate this problem is to fit models for different series in parallel if you have multiple compute cores available. To enable this behavior, set the `max_cores_per_iteration` parameter in your AutoMLConfig as shown in the example in the next cell. \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n",
|
||||
"Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you could specify the desired number of CV folds and the number of periods between two consecutive folds in the AutoMLConfig object, or AutoMl could set them automatically if you don't specify them. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n",
|
||||
"\n",
|
||||
"Here is a summary of AutoMLConfig parameters used for training the OJ model:\n",
|
||||
"\n",
|
||||
@@ -403,7 +404,7 @@
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|\n",
|
||||
"|**compute_target**|The remote compute for training.|\n",
|
||||
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection|\n",
|
||||
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or users could specify an integer value.\n",
|
||||
"|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models|\n",
|
||||
"|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models|\n",
|
||||
"|**debug_log**|Log file path for writing debugging information|\n",
|
||||
@@ -424,6 +425,7 @@
|
||||
" forecast_horizon=n_test_periods,\n",
|
||||
" time_series_id_column_names=time_series_id_column_names,\n",
|
||||
" freq=\"W-THU\", # Set the forecast frequency to be weekly (start on each Thursday)\n",
|
||||
" cv_step_size=\"auto\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(\n",
|
||||
@@ -436,7 +438,7 @@
|
||||
" compute_target=compute_target,\n",
|
||||
" enable_early_stopping=True,\n",
|
||||
" featurization=featurization_config,\n",
|
||||
" n_cross_validations=3,\n",
|
||||
" n_cross_validations=\"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" forecasting_parameters=forecasting_parameters,\n",
|
||||
@@ -819,9 +821,9 @@
|
||||
"friendly_name": "Forecasting orange juice sales with deployment",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -833,12 +835,17 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Forecasting"
|
||||
"task": "Forecasting",
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
|
||||
@@ -292,7 +292,8 @@
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**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",
|
||||
"|**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",
|
||||
"|**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."
|
||||
"|**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.\n",
|
||||
"|**cv_step_size**|Number of periods between two consecutive cross-validation folds. The default value is \"auto\", in which case AutoMl determines the cross-validation step size automatically, if a validation set is not provided. Or users could specify an integer value."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -307,7 +308,8 @@
|
||||
" time_column_name=time_column_name,\n",
|
||||
" forecast_horizon=n_test_periods,\n",
|
||||
" time_series_id_column_names=time_series_id_column_names,\n",
|
||||
" freq=\"W-THU\", # Set the forecast frequency to be weekly (start on each Thursday)\n",
|
||||
" freq=\"W-THU\", # Set the forecast frequency to be weekly (start on each Thursday),\n",
|
||||
" cv_step_size=\"auto\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(\n",
|
||||
@@ -319,7 +321,7 @@
|
||||
" label_column_name=target_column_name,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" enable_early_stopping=True,\n",
|
||||
" n_cross_validations=5,\n",
|
||||
" n_cross_validations=\"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" forecasting_parameters=forecasting_parameters,\n",
|
||||
@@ -797,9 +799,9 @@
|
||||
"friendly_name": "Forecasting orange juice sales with deployment",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -811,12 +813,17 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Forecasting"
|
||||
"task": "Forecasting",
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
|
||||
@@ -472,9 +472,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -358,7 +358,8 @@
|
||||
" enable_early_stopping=True,\n",
|
||||
" training_data=train_dataset,\n",
|
||||
" label_column_name=TARGET_COLNAME,\n",
|
||||
" n_cross_validations=5,\n",
|
||||
" n_cross_validations=\"auto\", # Feel free to set to a small integer (>=2) if runtime is an issue.\n",
|
||||
" cv_step_size=\"auto\",\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" max_cores_per_iteration=-1,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
@@ -571,9 +572,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
@@ -585,7 +586,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "6bd77c88278e012ef31757c15997a7bea8c943977c43d6909403c00ae11d43ca"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -870,9 +870,9 @@
|
||||
"friendly_name": "Classification of credit card fraudulent transactions using Automated ML",
|
||||
"index_order": 5,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -895,9 +895,9 @@
|
||||
"friendly_name": "Automated ML run with featurization and model explainability.",
|
||||
"index_order": 5,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -449,9 +449,9 @@
|
||||
"automated-machine-learning"
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -429,9 +429,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -557,9 +557,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -161,9 +161,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -215,9 +215,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -482,9 +482,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -302,9 +302,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -86,7 +86,7 @@
|
||||
"source": [
|
||||
"In this example, we will be using and registering two models. \n",
|
||||
"\n",
|
||||
"First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
|
||||
"First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -373,9 +373,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -541,7 +541,7 @@
|
||||
" - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n",
|
||||
" - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).\n",
|
||||
" - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n",
|
||||
" - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)."
|
||||
" - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where#choose-a-compute-target)."
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -568,9 +568,9 @@
|
||||
"friendly_name": "Register model and deploy as webservice",
|
||||
"index_order": 3,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -473,9 +473,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -529,9 +529,9 @@
|
||||
"friendly_name": "Register a model and deploy locally",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -344,9 +344,9 @@
|
||||
"friendly_name": "Deploy models to AKS using controlled roll out",
|
||||
"index_order": 3,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -476,9 +476,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -405,9 +405,9 @@
|
||||
"friendly_name": "Convert and deploy TinyYolo with ONNX Runtime",
|
||||
"index_order": 5,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -773,9 +773,9 @@
|
||||
"friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime",
|
||||
"index_order": 2,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -750,9 +750,9 @@
|
||||
"friendly_name": "Deploy MNIST digit recognition with ONNX Runtime",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -206,9 +206,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -389,9 +389,9 @@
|
||||
"friendly_name": "Deploy ResNet50 with ONNX Runtime",
|
||||
"index_order": 4,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -564,9 +564,9 @@
|
||||
"friendly_name": "Train MNIST in PyTorch, convert, and deploy with ONNX Runtime",
|
||||
"index_order": 3,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -329,9 +329,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -213,7 +213,7 @@
|
||||
"\n",
|
||||
"> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
|
||||
"\n",
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/azure/machine-learning/v1/how-to-secure-web-service) for more details"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -334,9 +334,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -366,7 +366,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create AKS Cluster in an existing virtual network (optional)\n",
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details."
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview) for more details."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -397,7 +397,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Enable SSL on the AKS Cluster (optional)\n",
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
|
||||
"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview#secure-the-inferencing-environment-v1) for more details"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -603,9 +603,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -327,9 +327,9 @@
|
||||
],
|
||||
"friendly_name": "Register Spark model and deploy as webservice",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -106,7 +106,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.44.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.46.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -237,12 +237,18 @@
|
||||
"import pkg_resources\n",
|
||||
"available_packages = pkg_resources.working_set\n",
|
||||
"pandas_ver = None\n",
|
||||
"numpy_ver = None\n",
|
||||
"for dist in list(available_packages):\n",
|
||||
" if dist.key == 'pandas':\n",
|
||||
" pandas_ver = dist.version\n",
|
||||
" if dist.key == 'numpy':\n",
|
||||
" numpy_ver = dist.version\n",
|
||||
"pandas_dep = 'pandas'\n",
|
||||
"numpy_dep = 'numpy'\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"if numpy_ver:\n",
|
||||
" numpy_dep = 'numpy=={}'.format(numpy_ver)\n",
|
||||
"\n",
|
||||
"# Note: we build shap at commit 690245 for Tesla K80 GPUs\n",
|
||||
"env.docker.base_dockerfile = f\"\"\"\n",
|
||||
@@ -282,7 +288,7 @@
|
||||
"pip uninstall -y xgboost && \\\n",
|
||||
"conda install py-xgboost==1.3.3 && \\\n",
|
||||
"pip uninstall -y numpy && \\\n",
|
||||
"conda install numpy==1.20.3 \\\n",
|
||||
"pip install {numpy_dep} \\\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"env.python.user_managed_dependencies = True\n",
|
||||
@@ -477,9 +483,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -7,12 +7,12 @@ dependencies:
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- jinja2
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.19.0
|
||||
- raiwidgets~=0.22.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
|
||||
@@ -269,23 +269,29 @@
|
||||
"available_packages = pkg_resources.working_set\n",
|
||||
"sklearn_ver = None\n",
|
||||
"pandas_ver = None\n",
|
||||
"joblib_ver = None\n",
|
||||
"for dist in list(available_packages):\n",
|
||||
" if dist.key == 'scikit-learn':\n",
|
||||
" sklearn_ver = dist.version\n",
|
||||
" elif dist.key == 'pandas':\n",
|
||||
" pandas_ver = dist.version\n",
|
||||
" elif dist.key == 'joblib':\n",
|
||||
" joblib_ver = dist.version\n",
|
||||
"sklearn_dep = 'scikit-learn'\n",
|
||||
"pandas_dep = 'pandas'\n",
|
||||
"joblib_dep = 'joblib'\n",
|
||||
"if sklearn_ver:\n",
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"if joblib_ver:\n",
|
||||
" joblib_dep = 'joblib=={}'.format(joblib_ver)\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"azureml_pip_packages.extend([sklearn_dep, pandas_dep])\n",
|
||||
"azureml_pip_packages.extend([sklearn_dep, pandas_dep, joblib_dep])\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(pip_packages=azureml_pip_packages, python_version=python_version)\n",
|
||||
"\n",
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
@@ -490,9 +496,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -6,13 +6,13 @@ dependencies:
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- jinja2
|
||||
- ipython
|
||||
- matplotlib
|
||||
- azureml-dataset-runtime
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.19.0
|
||||
- raiwidgets~=0.22.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
|
||||
@@ -595,9 +595,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -6,13 +6,13 @@ dependencies:
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- jinja2
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.19.0
|
||||
- raiwidgets~=0.22.0
|
||||
- packaging>=20.9
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
|
||||
@@ -516,9 +516,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -6,13 +6,13 @@ dependencies:
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- jinja2
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.19.0
|
||||
- raiwidgets~=0.22.0
|
||||
- packaging>=20.9
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
|
||||
@@ -277,23 +277,29 @@
|
||||
"available_packages = pkg_resources.working_set\n",
|
||||
"sklearn_ver = None\n",
|
||||
"pandas_ver = None\n",
|
||||
"joblib_ver = None\n",
|
||||
"for dist in available_packages:\n",
|
||||
" if dist.key == 'scikit-learn':\n",
|
||||
" sklearn_ver = dist.version\n",
|
||||
" elif dist.key == 'pandas':\n",
|
||||
" pandas_ver = dist.version\n",
|
||||
" elif dist.key == 'joblib':\n",
|
||||
" joblib_ver = dist.version\n",
|
||||
"sklearn_dep = 'scikit-learn'\n",
|
||||
"pandas_dep = 'pandas'\n",
|
||||
"joblib_dep = 'joblib'\n",
|
||||
"if sklearn_ver:\n",
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"if joblib_ver:\n",
|
||||
" joblib_dep = 'joblib=={}'.format(joblib_ver)\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
|
||||
"azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep, joblib_dep])\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
|
||||
" python_version=python_version,\n",
|
||||
" pip_packages=azureml_pip_packages)\n",
|
||||
@@ -440,23 +446,29 @@
|
||||
"available_packages = pkg_resources.working_set\n",
|
||||
"sklearn_ver = None\n",
|
||||
"pandas_ver = None\n",
|
||||
"joblib_ver = None\n",
|
||||
"for dist in available_packages:\n",
|
||||
" if dist.key == 'scikit-learn':\n",
|
||||
" sklearn_ver = dist.version\n",
|
||||
" elif dist.key == 'pandas':\n",
|
||||
" pandas_ver = dist.version\n",
|
||||
" elif dist.key == 'joblib':\n",
|
||||
" joblib_ver = dist.version\n",
|
||||
"sklearn_dep = 'scikit-learn'\n",
|
||||
"pandas_dep = 'pandas'\n",
|
||||
"joblib_dep = 'joblib'\n",
|
||||
"if sklearn_ver:\n",
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"if joblib_ver:\n",
|
||||
" joblib_dep = 'joblib=={}'.format(joblib_ver)\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep])\n",
|
||||
"azureml_pip_packages.extend(['pyyaml', sklearn_dep, pandas_dep, joblib_dep])\n",
|
||||
"myenv = CondaDependencies.create(python_version=python_version, pip_packages=azureml_pip_packages)\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
@@ -564,9 +576,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -6,14 +6,14 @@ dependencies:
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- jinja2
|
||||
- ipython
|
||||
- matplotlib
|
||||
- azureml-dataset-runtime
|
||||
- azureml-core
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.19.0
|
||||
- raiwidgets~=0.22.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
|
||||
@@ -579,9 +579,9 @@
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipeline with DataTranferStep",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -632,9 +632,9 @@
|
||||
],
|
||||
"friendly_name": "Getting Started with Azure Machine Learning Pipelines",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -384,9 +384,9 @@
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipeline with AzureBatchStep",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -470,9 +470,9 @@
|
||||
],
|
||||
"friendly_name": "How to use ModuleStep with AML Pipelines",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -261,9 +261,9 @@
|
||||
],
|
||||
"friendly_name": "How to use Pipeline Drafts to create a Published Pipeline",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -292,7 +292,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.0-GPU')"
|
||||
"tf_env = Environment.get(ws, name='AzureML-tensorflow-2.6-ubuntu20.04-py38-cuda11-gpu')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -595,9 +595,9 @@
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipeline with HyperDriveStep",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -443,9 +443,9 @@
|
||||
],
|
||||
"friendly_name": "How to Publish a Pipeline and Invoke the REST endpoint",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -432,7 +432,7 @@
|
||||
"This schedule will run when additions or modifications are made to Blobs in the Datastore.\n",
|
||||
"By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Note: the path_on_datastore will be under the container for the datastore, so the actual path monitored will be container/path_on_datastore. Changes made to subfolders in the container/path will not trigger the schedule.\n",
|
||||
"Note: Only Blob Datastores are supported.\n",
|
||||
"Note: Not supported for CMK workspaces. Please review these [instructions](https://docs.microsoft.com/azure/machine-learning/how-to-trigger-published-pipeline) in order to setup a blob trigger submission schedule with CMK enabled. Also see those instructions to bring your own LogicApp to avoid the schedule triggers per month limit."
|
||||
"Note: Not supported for CMK workspaces. Please review these [instructions](https://docs.microsoft.com/azure/machine-learning/v1/how-to-trigger-published-pipeline) in order to setup a blob trigger submission schedule with CMK enabled. Also see those instructions to bring your own LogicApp to avoid the schedule triggers per month limit."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -637,9 +637,9 @@
|
||||
],
|
||||
"friendly_name": "How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -581,9 +581,9 @@
|
||||
],
|
||||
"friendly_name": "How to setup a versioned Pipeline Endpoint",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -500,9 +500,9 @@
|
||||
],
|
||||
"friendly_name": "How to use DataPath as a PipelineParameter",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -496,9 +496,9 @@
|
||||
],
|
||||
"friendly_name": "How to use Dataset as a PipelineParameter",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -377,9 +377,9 @@
|
||||
],
|
||||
"friendly_name": "How to use AdlaStep with AML Pipelines",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -20,7 +20,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
|
||||
"To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
|
||||
"To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
|
||||
"\n",
|
||||
"The notebook will show:\n",
|
||||
"1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
|
||||
@@ -180,10 +180,9 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data Connections with Inputs and Outputs\n",
|
||||
"The DatabricksStep supports DBFS, Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
|
||||
"The DatabricksStep supports DBFS, Azure Blob and ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.microsoft.com/azure/databricks/security/access-control/secret-acl) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
|
||||
"\n",
|
||||
"- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n",
|
||||
"- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n",
|
||||
"- Databricks documentation on [Azure Storage](https://docs.microsoft.com/azure/databricks/data/data-sources/azure/azure-storage)\n",
|
||||
"\n",
|
||||
"### Type of Data Access\n",
|
||||
"Databricks allows to interact with Azure Blob and ADLS in two ways.\n",
|
||||
@@ -415,7 +414,7 @@
|
||||
"### 1. Running the demo notebook already added to the Databricks workspace\n",
|
||||
"Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:\n",
|
||||
"\n",
|
||||
"your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.azuredatabricks.net/user-guide/workspace.html) to learn about the folder structure.\n",
|
||||
"your notebook's path in Azure Databricks UI by hovering over to notebook's title. A typical path of notebook looks like this `/Users/example@databricks.com/example`. See [Databricks Workspace](https://docs.microsoft.com/azure/databricks/workspace) to learn about the folder structure.\n",
|
||||
"\n",
|
||||
"Note: DataPath `PipelineParameter` should be provided in list of inputs. Such parameters can be accessed by the datapath `name`."
|
||||
]
|
||||
@@ -487,7 +486,7 @@
|
||||
"### 2. Running a Python script from DBFS\n",
|
||||
"This shows how to run a Python script in DBFS. \n",
|
||||
"\n",
|
||||
"To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html). The CLI command is given below:\n",
|
||||
"To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.microsoft.com/azure/databricks/dbfs). The CLI command is given below:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n",
|
||||
@@ -630,7 +629,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 4. Running a JAR job that is alreay added in DBFS\n",
|
||||
"To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
|
||||
"To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.microsoft.com/azure/databricks/dbfs).\n",
|
||||
"\n",
|
||||
"The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n",
|
||||
"\n",
|
||||
@@ -704,7 +703,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 5. Running demo notebook already added to the Databricks workspace using existing cluster\n",
|
||||
"First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
|
||||
"First you need register DBFS datastore and make sure path_on_datastore does exist in databricks file system, you can browser the files by refering [this](https://docs.microsoft.com/azure/databricks/dbfs).\n",
|
||||
"\n",
|
||||
"Find existing_cluster_id by opeing Azure Databricks UI with Clusters page and in url you will find a string connected with '-' right after \"clusters/\"."
|
||||
]
|
||||
@@ -941,9 +940,9 @@
|
||||
],
|
||||
"friendly_name": "How to use DatabricksStep with AML Pipelines",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -244,9 +244,9 @@
|
||||
],
|
||||
"friendly_name": "How to use KustoStep with AML Pipelines",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -498,9 +498,9 @@
|
||||
],
|
||||
"friendly_name": "How to use AutoMLStep with AML Pipelines",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -315,9 +315,9 @@
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipeline with CommandStep for R",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -278,9 +278,9 @@
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipeline with CommandStep",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -545,9 +545,9 @@
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipelines with Data Dependency",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -409,9 +409,9 @@
|
||||
],
|
||||
"friendly_name": "How to use run a notebook as a step in AML Pipelines",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -84,9 +84,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -1046,9 +1046,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
"In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"\n",
|
||||
"In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch. \n",
|
||||
"\n",
|
||||
@@ -277,7 +277,7 @@
|
||||
"### Register the model with Workspace\n",
|
||||
"A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.\n",
|
||||
"\n",
|
||||
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-and-where#registermodel) "
|
||||
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/azure/machine-learning/v1/how-to-deploy-and-where#registermodel) "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -581,16 +581,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "joringer"
|
||||
},
|
||||
{
|
||||
"name": "asraniwa"
|
||||
},
|
||||
{
|
||||
"name": "pansav"
|
||||
},
|
||||
{
|
||||
"name": "tracych"
|
||||
"name": "prsbjdev"
|
||||
}
|
||||
],
|
||||
"category": "Other notebooks",
|
||||
@@ -610,9 +601,9 @@
|
||||
"friendly_name": "MNIST data inferencing using ParallelRunStep",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
"In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"\n",
|
||||
"This example will create a sample dataset with nested folder structure, where the folder name corresponds to the attribute of the files inside it. The Batch Inference job would split the files inside the dataset according to their attributes, so that all files with identical value on the specified attribute will form up a single mini-batch to be processed.\n",
|
||||
"\n",
|
||||
@@ -356,13 +356,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "pansav"
|
||||
},
|
||||
{
|
||||
"name": "tracych"
|
||||
},
|
||||
{
|
||||
"name": "migu"
|
||||
"name": "prsbjdev"
|
||||
}
|
||||
],
|
||||
"category": "Other notebooks",
|
||||
@@ -382,9 +376,9 @@
|
||||
"friendly_name": "Batch inferencing file data partitioned by folder using ParallelRunStep",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
"In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"\n",
|
||||
"In this example we will take use a machine learning model already trained to predict different types of iris flowers and run that trained model on some of the data in a CSV file which has characteristics of different iris flowers. However, the same example can be extended to manipulating data to any embarrassingly-parallel processing through a python script.\n",
|
||||
"\n",
|
||||
@@ -487,16 +487,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "joringer"
|
||||
},
|
||||
{
|
||||
"name": "asraniwa"
|
||||
},
|
||||
{
|
||||
"name": "pansav"
|
||||
},
|
||||
{
|
||||
"name": "tracych"
|
||||
"name": "prsbjdev"
|
||||
}
|
||||
],
|
||||
"category": "Other notebooks",
|
||||
@@ -516,9 +507,9 @@
|
||||
"friendly_name": "IRIS data inferencing using ParallelRunStep",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
"In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction.\n",
|
||||
"\n",
|
||||
"This example will create a partitioned tabular dataset by splitting the rows in a large csv file by its value on specified column. Each partition will form up a mini-batch in the parallel processing procedure.\n",
|
||||
"\n",
|
||||
@@ -379,13 +379,7 @@
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "pansav"
|
||||
},
|
||||
{
|
||||
"name": "tracych"
|
||||
},
|
||||
{
|
||||
"name": "migu"
|
||||
"name": "prsbjdev"
|
||||
}
|
||||
],
|
||||
"category": "Other notebooks",
|
||||
@@ -405,9 +399,9 @@
|
||||
"friendly_name": "Batch inferencing OJ Sales Data partitioned by column using ParallelRunStep",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -27,7 +27,7 @@
|
||||
"3. Stitch the image back into a video.\n",
|
||||
"\n",
|
||||
"> **Tip**\n",
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction."
|
||||
"If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/v1/how-to-consume-web-service) instead of batch prediction."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -726,9 +726,9 @@
|
||||
"friendly_name": "Style transfer using ParallelRunStep",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -521,9 +521,9 @@
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -332,9 +332,9 @@
|
||||
"friendly_name": "Distributed Training with Chainer",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -4,7 +4,7 @@ import os
|
||||
|
||||
import numpy as np
|
||||
|
||||
from utils import download_mnist
|
||||
from datautils import download_mnist
|
||||
|
||||
import chainer
|
||||
from chainer import backend
|
||||
|
||||
@@ -2,7 +2,7 @@ import numpy as np
|
||||
import os
|
||||
import json
|
||||
|
||||
from utils import download_mnist
|
||||
from datautils import download_mnist
|
||||
|
||||
from chainer import serializers, using_config, Variable, datasets
|
||||
import chainer.functions as F
|
||||
|
||||
@@ -210,7 +210,7 @@
|
||||
"\n",
|
||||
"shutil.copy('chainer_mnist.py', project_folder)\n",
|
||||
"shutil.copy('chainer_score.py', project_folder)\n",
|
||||
"shutil.copy('utils.py', project_folder)"
|
||||
"shutil.copy('datautils.py', project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -783,9 +783,9 @@
|
||||
"friendly_name": "Train a model with hyperparameter tuning",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user