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release_up
...
bozhlin-up
| Author | SHA1 | Date | |
|---|---|---|---|
|
2be4ca1dba |
@@ -103,7 +103,7 @@
|
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"source": [
|
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"import azureml.core\n",
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"\n",
|
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"print(\"This notebook was created using version 1.50.0 of the Azure ML SDK\")\n",
|
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"print(\"This notebook was created using version 1.49.0 of the Azure ML SDK\")\n",
|
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
|
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},
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|
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@@ -6,7 +6,7 @@ dependencies:
|
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- fairlearn>=0.6.2
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- joblib
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- liac-arff
|
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- raiwidgets~=0.26.0
|
||||
- raiwidgets~=0.24.0
|
||||
- itsdangerous==2.0.1
|
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- markupsafe<2.1.0
|
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- protobuf==3.20.0
|
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|
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@@ -6,7 +6,7 @@ dependencies:
|
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- fairlearn>=0.6.2
|
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- joblib
|
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- liac-arff
|
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- raiwidgets~=0.26.0
|
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- raiwidgets~=0.24.0
|
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- itsdangerous==2.0.1
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- markupsafe<2.1.0
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- protobuf==3.20.0
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|
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@@ -12,13 +12,12 @@ dependencies:
|
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- pandas==1.1.5
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- scipy==1.5.3
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- Cython==0.29.14
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- tqdm==4.64.1
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|
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.50.0
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||||
- azureml-defaults~=1.50.0
|
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.50.0/validated_win32_requirements.txt [--no-deps]
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- azureml-widgets~=1.49.0
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||||
- azureml-defaults~=1.49.0
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.49.0/validated_win32_requirements.txt [--no-deps]
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- matplotlib==3.6.2
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- xgboost==1.3.3
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- cmdstanpy==0.9.5
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@@ -23,10 +23,10 @@ dependencies:
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|
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.50.0
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- azureml-defaults~=1.50.0
|
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- azureml-widgets~=1.49.0
|
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- azureml-defaults~=1.49.0
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- pytorch-transformers==1.0.0
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- spacy==2.2.4
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- pystan==2.19.1.1
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.50.0/validated_linux_requirements.txt [--no-deps]
|
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.49.0/validated_linux_requirements.txt [--no-deps]
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|
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@@ -23,10 +23,10 @@ dependencies:
|
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|
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- pip:
|
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# Required packages for AzureML execution, history, and data preparation.
|
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- azureml-widgets~=1.50.0
|
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- azureml-defaults~=1.50.0
|
||||
- azureml-widgets~=1.49.0
|
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- azureml-defaults~=1.49.0
|
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- pytorch-transformers==1.0.0
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- spacy==2.2.4
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- pystan==2.19.1.1
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.50.0/validated_darwin_requirements.txt [--no-deps]
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.49.0/validated_darwin_requirements.txt [--no-deps]
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@@ -97,7 +97,7 @@
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
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"print(\"This notebook was created using version 1.50.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.49.0 of the Azure ML SDK\")\n",
|
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
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]
|
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},
|
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|
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@@ -97,7 +97,7 @@
|
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"metadata": {},
|
||||
"outputs": [],
|
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"source": [
|
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"print(\"This notebook was created using version 1.50.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.49.0 of the Azure ML SDK\")\n",
|
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
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]
|
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},
|
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|
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@@ -92,7 +92,7 @@
|
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"metadata": {},
|
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"outputs": [],
|
||||
"source": [
|
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"print(\"This notebook was created using version 1.50.0 of the Azure ML SDK\")\n",
|
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"print(\"This notebook was created using version 1.49.0 of the Azure ML SDK\")\n",
|
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
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]
|
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},
|
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|
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@@ -91,7 +91,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
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"print(\"This notebook was created using version 1.50.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.49.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
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},
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@@ -42,7 +42,7 @@
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"\n",
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"AutoML highlights here include built-in holiday featurization, accessing engineered feature names, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
|
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"\n",
|
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"Make sure you have executed the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) before running this notebook.\n",
|
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"Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
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"\n",
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"Notebook synopsis:\n",
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"1. Creating an Experiment in an existing Workspace\n",
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@@ -43,7 +43,7 @@
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"\n",
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"In this example we use the associated New York City energy demand dataset to showcase how you can use AutoML for a simple forecasting problem and explore the results. The goal is predict the energy demand for the next 48 hours based on historic time-series data.\n",
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"\n",
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"If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\n",
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"If you are using an Azure Machine Learning Compute Instance, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first, if you haven't already, to establish your connection to the AzureML Workspace.\n",
|
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"\n",
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||||
"In this notebook you will learn how to:\n",
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||||
"1. Creating an Experiment using an existing Workspace\n",
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||||
|
||||
@@ -52,7 +52,7 @@
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Please make sure you have followed the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) so that your ML workspace information is saved in the config file."
|
||||
"Please make sure you have followed the `configuration.ipynb` notebook so that your ML workspace information is saved in the config file."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -52,7 +52,7 @@
|
||||
"\n",
|
||||
"AutoML highlights here include using Deep Learning forecasts, Arima, Prophet, Remote Execution and Remote Inferencing, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) before running this notebook.\n",
|
||||
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"Notebook synopsis:\n",
|
||||
"\n",
|
||||
|
||||
@@ -40,7 +40,7 @@
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||||
"## Introduction<a id=\"introduction\"></a>\n",
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||||
"In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) before running this notebook.\n",
|
||||
"Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
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||||
]
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@@ -13,7 +13,7 @@
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||||
"source": [
|
||||
"## Introduction\n",
|
||||
"\n",
|
||||
"In this notebook, we demonstrate how to use piplines to train and inference on AutoML Forecasting model. Two pipelines will be created: one for training AutoML model, and the other is for inference on AutoML model. We'll also demonstrate how to schedule the inference pipeline so you can get inference results periodically (with refreshed test dataset). Make sure you have executed the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) before running this notebook. In this notebook you will learn how to:\n",
|
||||
"In this notebook, we demonstrate how to use piplines to train and inference on AutoML Forecasting model. Two pipelines will be created: one for training AutoML model, and the other is for inference on AutoML model. We'll also demonstrate how to schedule the inference pipeline so you can get inference results periodically (with refreshed test dataset). Make sure you have executed the configuration notebook before running this notebook. In this notebook you will learn how to:\n",
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||||
"\n",
|
||||
"- Configure AutoML using AutoMLConfig for forecasting tasks using pipeline AutoMLSteps.\n",
|
||||
"- Create and register an AutoML model using AzureML pipeline.\n",
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||||
|
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@@ -106,7 +106,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.50.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.49.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -10,7 +10,7 @@ dependencies:
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.26.0
|
||||
- raiwidgets~=0.24.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
|
||||
@@ -10,7 +10,7 @@ dependencies:
|
||||
- matplotlib
|
||||
- azureml-dataset-runtime
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.26.0
|
||||
- raiwidgets~=0.24.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
|
||||
@@ -9,7 +9,7 @@ dependencies:
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.26.0
|
||||
- raiwidgets~=0.24.0
|
||||
- packaging>=20.9
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
|
||||
@@ -9,7 +9,7 @@ dependencies:
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.26.0
|
||||
- raiwidgets~=0.24.0
|
||||
- packaging>=20.9
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
|
||||
@@ -11,7 +11,7 @@ dependencies:
|
||||
- azureml-dataset-runtime
|
||||
- azureml-core
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.26.0
|
||||
- raiwidgets~=0.24.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
|
||||
@@ -175,7 +175,7 @@
|
||||
"store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\") # ADLS account name\n",
|
||||
"tenant_id=os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
|
||||
"client_id=os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
|
||||
"client_st=os.getenv(\"ADL_CLIENT_SECRET_62\", \"<my-client-secret>\") # the secret of service principal\n",
|
||||
"client_secret=os.getenv(\"ADL_CLIENT_SECRET_62\", \"<my-client-secret>\") # the secret of service principal\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" adls_datastore = Datastore.get(ws, datastore_name)\n",
|
||||
@@ -189,7 +189,7 @@
|
||||
" store_name=store_name, # ADLS account name\n",
|
||||
" tenant_id=tenant_id, # tenant id of service principal\n",
|
||||
" client_id=client_id, # client id of service principal\n",
|
||||
" client_secret=client_st) # the secret of service principal\n",
|
||||
" client_secret=client_secret) # the secret of service principal\n",
|
||||
" print(\"Registered datastore with name: %s\" % datastore_name)\n",
|
||||
"\n",
|
||||
"adls_data_ref = DataReference(\n",
|
||||
|
||||
@@ -147,7 +147,7 @@
|
||||
"store_name = os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\") # ADLS account name\n",
|
||||
"tenant_id = os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
|
||||
"client_id = os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
|
||||
"client_st = os.getenv(\"ADL_CLIENT_62_SECRET\", \"<my-client-secret>\") # the secret of service principal\n",
|
||||
"client_secret = os.getenv(\"ADL_CLIENT_62_SECRET\", \"<my-client-secret>\") # the secret of service principal\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" adls_datastore = Datastore.get(ws, datastore_name)\n",
|
||||
@@ -161,7 +161,7 @@
|
||||
" store_name=store_name, # ADLS account name\n",
|
||||
" tenant_id=tenant_id, # tenant id of service principal\n",
|
||||
" client_id=client_id, # client id of service principal\n",
|
||||
" client_secret=client_st) # the secret of service principal\n",
|
||||
" client_secret=client_secret) # the secret of service principal\n",
|
||||
" print(\"registered datastore with name: %s\" % datastore_name)"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -8,11 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
rm -rf /var/lib/apt/lists/* && \
|
||||
rm -rf /usr/share/man/*
|
||||
|
||||
RUN conda install -y conda=23.1.0 python=3.8 && conda clean -ay
|
||||
|
||||
RUN conda install -y -c conda-forge ffmpeg=4.0.2
|
||||
# RUN conda install -c anaconda opencv
|
||||
|
||||
RUN conda install -y conda=4.13.0 python=3.7 && conda clean -ay
|
||||
RUN pip install ray-on-aml==0.2.1 & \
|
||||
pip install --no-cache-dir \
|
||||
azureml-defaults \
|
||||
@@ -22,19 +18,21 @@ RUN pip install ray-on-aml==0.2.1 & \
|
||||
scipy \
|
||||
pyglet==1.5.27 \
|
||||
cloudpickle==1.3.0 \
|
||||
tensorboard==2.7.0 \
|
||||
tensorflow==2.7.0 \
|
||||
tensorboardX \
|
||||
tensorflow==1.14.0 \
|
||||
tabulate \
|
||||
dm_tree \
|
||||
lz4 \
|
||||
psutil \
|
||||
setproctitle \
|
||||
pygame \
|
||||
gym[classic_control]==0.19.0
|
||||
gym[classic_control]==0.19.0 && \
|
||||
conda install -y -c conda-forge x264='1!152.20180717' ffmpeg=4.0.2 && \
|
||||
conda install -c anaconda opencv
|
||||
|
||||
RUN pip install protobuf==3.20.0
|
||||
|
||||
RUN pip install --upgrade ray==0.8.7 \
|
||||
ray[rllib,dashboard,tune]==0.8.7
|
||||
RUN pip install --upgrade ray==0.8.3 \
|
||||
ray[rllib,dashboard,tune]==0.8.3
|
||||
|
||||
RUN pip install 'msrest<0.7.0'
|
||||
RUN pip install 'msrest<0.7.0'
|
||||
@@ -8,7 +8,7 @@ dependencies:
|
||||
- matplotlib
|
||||
- azureml-dataset-runtime
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.26.0
|
||||
- raiwidgets~=0.24.0
|
||||
- liac-arff
|
||||
- packaging>=20.9
|
||||
- itsdangerous==2.0.1
|
||||
|
||||
@@ -101,7 +101,7 @@
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using SDK version 1.50.0, you are currently running version\", azureml.core.VERSION)"
|
||||
"print(\"This notebook was created using SDK version 1.49.0, you are currently running version\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
1
index.md
1
index.md
@@ -87,6 +87,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
||||
| [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
|
||||
| [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
|
||||
| :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None |
|
||||
| :star:[Deploy models to AKS using controlled roll out](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Kubernetes Service | Scikit-learn | None |
|
||||
| [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter |
|
||||
| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
|
||||
| :star:[Convert and deploy TinyYolo with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) | Object Detection | PASCAL VOC | local | Azure Container Instance | ONNX | ONNX Converter |
|
||||
|
||||
@@ -102,7 +102,7 @@
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using version 1.50.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.49.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -16,7 +16,6 @@ import tf_slim
|
||||
from azureml.core import Run
|
||||
from azureml.core.model import Model
|
||||
from azureml.core.dataset import Dataset
|
||||
from tf_slim import nets
|
||||
|
||||
slim = tf_slim
|
||||
|
||||
@@ -42,14 +41,15 @@ def init():
|
||||
parser.add_argument('--model_name', dest="model_name", required=True)
|
||||
parser.add_argument('--labels_dir', dest="labels_dir", required=True)
|
||||
args, _ = parser.parse_known_args()
|
||||
from nets import inception_v3, inception_utils
|
||||
label_dict = get_class_label_dict(args.labels_dir)
|
||||
classes_num = len(label_dict)
|
||||
tf.disable_v2_behavior()
|
||||
with slim.arg_scope(nets.inception.inception_v3_arg_scope()):
|
||||
with slim.arg_scope(inception_utils.inception_arg_scope()):
|
||||
input_images = tf.placeholder(tf.float32, [1, image_size, image_size, num_channel])
|
||||
logits, _ = nets.inception.inception_v3(input_images,
|
||||
num_classes=classes_num,
|
||||
is_training=False)
|
||||
logits, _ = inception_v3.inception_v3(input_images,
|
||||
num_classes=classes_num,
|
||||
is_training=False)
|
||||
probabilities = tf.argmax(logits, 1)
|
||||
|
||||
config = tf.ConfigProto()
|
||||
|
||||
@@ -307,10 +307,9 @@
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(python_version=\"3.8\",\n",
|
||||
" conda_packages=['pip==20.2.4'],\n",
|
||||
" pip_packages=[\"tensorflow-gpu==2.6.0\",\n",
|
||||
" pip_packages=[\"tensorflow-gpu==2.3.0\",\n",
|
||||
" \"tf_slim==1.1.0\", \"protobuf==3.20.1\",\n",
|
||||
" \"typing-extensions==4.3.0\",\n",
|
||||
" \"azureml-core\", \"mltable\"])\n",
|
||||
" \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
|
||||
"\n",
|
||||
"env = Environment(name=\"parallelenv\")\n",
|
||||
"env.python.conda_dependencies=cd\n",
|
||||
|
||||
Reference in New Issue
Block a user