mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-19 17:17:04 -05:00
update samples from Release-209 as a part of 1.56.0 SDK stable release
This commit is contained in:
amlrelsa-ms
@@ -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 1.55.0 of the Azure ML SDK\")\n",
|
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"print(\"This notebook was created using version 1.56.0 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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@@ -9,7 +9,6 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
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* [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
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* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
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* [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
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* [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
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* [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
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Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
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@@ -7,19 +7,21 @@ dependencies:
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# The python interpreter version.
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# Azure ML only supports 3.8 and later.
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- pip==22.3.1
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- python>=3.9,<3.10
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- python>=3.10,<3.11
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- holidays==0.29
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- scipy==1.10.1
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- tqdm==4.66.1
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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~=1.55.0
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- azureml-defaults~=1.55.0
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.55.0/validated_win32_requirements.txt [--no-deps]
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- azureml-widgets~=1.56.0
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- azureml-defaults~=1.56.0
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.56.0/validated_win32_requirements.txt [--no-deps]
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- matplotlib==3.7.1
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- xgboost==1.3.3
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- xgboost==1.5.2
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- prophet==1.1.4
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- pandas==1.3.5
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- cmdstanpy==1.1.0
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- setuptools-git==1.2
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- spacy==3.4.4
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- https://aka.ms/automl-resources/packages/en_core_web_sm-3.4.1.tar.gz
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@@ -7,13 +7,12 @@ dependencies:
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# The python interpreter version.
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# Azure ML only supports 3.7 and later.
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- pip==22.3.1
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- python>=3.9,<3.10
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- python>=3.10,<3.11
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- matplotlib==3.7.1
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- numpy>=1.21.6,<=1.23.5
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- urllib3==1.26.7
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- scipy==1.10.1
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- scikit-learn=1.1.3
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- py-xgboost<=1.3.3
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- scikit-learn==1.1.3
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- holidays==0.29
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- pytorch::pytorch=1.11.0
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- cudatoolkit=10.1.243
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@@ -21,10 +20,11 @@ dependencies:
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- pip:
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# Required packages for AzureML execution, history, and data preparation.
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- azureml-widgets~=1.55.0
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- azureml-defaults~=1.55.0
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- azureml-widgets~=1.56.0
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- azureml-defaults~=1.56.0
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- pytorch-transformers==1.0.0
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- spacy==2.3.9
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- spacy==3.4.4
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- xgboost==1.5.2
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- prophet==1.1.4
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.3.1.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.55.0/validated_linux_requirements.txt [--no-deps]
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- https://aka.ms/automl-resources/packages/en_core_web_sm-3.4.1.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.56.0/validated_linux_requirements.txt [--no-deps]
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@@ -7,7 +7,7 @@ dependencies:
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# The python interpreter version.
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# Currently Azure ML only supports 3.7 and later.
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- pip==22.3.1
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- python>=3.9,<3.10
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- python>=3.10,<3.11
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- numpy>=1.21.6,<=1.23.5
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- scipy==1.10.1
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- scikit-learn==1.1.3
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@@ -15,12 +15,12 @@ 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~=1.55.0
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- azureml-defaults~=1.55.0
|
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- azureml-widgets~=1.56.0
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- azureml-defaults~=1.56.0
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- pytorch-transformers==1.0.0
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- prophet==1.1.4
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- xgboost==1.3.3
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- spacy==2.3.9
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- xgboost==1.5.2
|
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- spacy==3.4.4
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- matplotlib==3.7.1
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- https://aka.ms/automl-resources/packages/en_core_web_sm-2.3.1.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.55.0/validated_darwin_requirements.txt [--no-deps]
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- https://aka.ms/automl-resources/packages/en_core_web_sm-3.4.1.tar.gz
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- -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.56.0/validated_darwin_requirements.txt [--no-deps]
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@@ -1,4 +0,0 @@
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name: auto-ml-classification-bank-marketing-all-features
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-classification-credit-card-fraud
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-continuous-retraining
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dependencies:
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- pip:
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- azureml-sdk
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@@ -9,7 +9,7 @@ To run these notebook on your own notebook server, use these installation instru
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The instructions below will install everything you need and then start a Jupyter notebook.
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If you would like to use a lighter-weight version of the client that does not install all of the machine learning libraries locally, you can leverage the [experimental notebooks.](experimental/README.md)
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### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
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### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.8 or higher.
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- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V). If you have a previous version installed, you can update it using the command: conda update conda.
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There's no need to install mini-conda specifically.
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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 1.55.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.56.0 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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@@ -148,7 +148,7 @@
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"from azureml.core.compute_target import ComputeTargetException\n",
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"\n",
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"# Choose a name for your CPU cluster\n",
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"cpu_cluster_name = \"cpu-cluster\"\n",
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"cpu_cluster_name = \"cpu-codegen\"\n",
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"\n",
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"# Verify that cluster does not exist already\n",
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"try:\n",
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@@ -1,4 +0,0 @@
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name: codegen-for-autofeaturization
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dependencies:
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- pip:
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- azureml-sdk
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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 1.55.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.56.0 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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@@ -1,4 +0,0 @@
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name: custom-model-training-from-autofeaturization-run
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dependencies:
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- pip:
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- azureml-sdk
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@@ -92,7 +92,7 @@
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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 1.55.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.56.0 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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},
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|
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@@ -1,4 +0,0 @@
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name: auto-ml-classification-credit-card-fraud-local-managed
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dependencies:
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- pip:
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- azureml-sdk
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@@ -91,7 +91,7 @@
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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 1.55.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.56.0 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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},
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@@ -1,4 +0,0 @@
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name: auto-ml-regression-model-proxy
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-backtest-many-models
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-backtest-single-model
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-bike-share
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-energy-demand
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-function
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-github-dau
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dependencies:
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-hierarchical-timeseries
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dependencies:
|
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- pip:
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- azureml-sdk
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-many-models
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dependencies:
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- pip:
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- azureml-sdk
|
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-orange-juice-sales
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dependencies:
|
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- pip:
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- azureml-sdk
|
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-pipelines
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dependencies:
|
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- pip:
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- azureml-sdk
|
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-univariate-recipe-experiment-settings
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dependencies:
|
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- pip:
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- azureml-sdk
|
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@@ -1,4 +0,0 @@
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name: auto-ml-forecasting-univariate-recipe-run-experiment
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dependencies:
|
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- pip:
|
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- azureml-sdk
|
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@@ -570,273 +570,6 @@
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"automl_run.upload_file(\"outputs/scoring_explainer.pkl\", scoring_explainer_file_name)"
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]
|
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},
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{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploying the scoring and explainer models to a web service to Azure Kubernetes Service (AKS)\n",
|
||||
"\n",
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||||
"We use the TreeScoringExplainer from azureml.interpret package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. In the cell below, we register the AutoML model and the scoring explainer with the Model Management Service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Register trained automl model present in the 'outputs' folder in the artifacts\n",
|
||||
"original_model = automl_run.register_model(\n",
|
||||
" model_name=\"automl_model\", model_path=\"outputs/model.pkl\"\n",
|
||||
")\n",
|
||||
"scoring_explainer_model = automl_run.register_model(\n",
|
||||
" model_name=\"scoring_explainer\", model_path=\"outputs/scoring_explainer.pkl\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create the conda dependencies for setting up the service\n",
|
||||
"\n",
|
||||
"We need to download the conda dependencies using the automl_run object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.automl.core.shared import constants\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"\n",
|
||||
"automl_run.download_file(constants.CONDA_ENV_FILE_PATH, \"myenv.yml\")\n",
|
||||
"myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
|
||||
"myenv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Write the Entry Script\n",
|
||||
"Write the script that will be used to predict on your model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import joblib\n",
|
||||
"import pandas as pd\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"from azureml.train.automl.runtime.automl_explain_utilities import (\n",
|
||||
" automl_setup_model_explanations,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global automl_model\n",
|
||||
" global scoring_explainer\n",
|
||||
"\n",
|
||||
" # Retrieve the path to the model file using the model name\n",
|
||||
" # Assume original model is named original_prediction_model\n",
|
||||
" automl_model_path = Model.get_model_path(\"automl_model\")\n",
|
||||
" scoring_explainer_path = Model.get_model_path(\"scoring_explainer\")\n",
|
||||
"\n",
|
||||
" automl_model = joblib.load(automl_model_path)\n",
|
||||
" scoring_explainer = joblib.load(scoring_explainer_path)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def run(raw_data):\n",
|
||||
" data = pd.read_json(raw_data, orient=\"records\")\n",
|
||||
" # Make prediction\n",
|
||||
" predictions = automl_model.predict(data)\n",
|
||||
" # Setup for inferencing explanations\n",
|
||||
" automl_explainer_setup_obj = automl_setup_model_explanations(\n",
|
||||
" automl_model, X_test=data, task=\"classification\"\n",
|
||||
" )\n",
|
||||
" # Retrieve model explanations for engineered explanations\n",
|
||||
" engineered_local_importance_values = scoring_explainer.explain(\n",
|
||||
" automl_explainer_setup_obj.X_test_transform\n",
|
||||
" )\n",
|
||||
" # Retrieve model explanations for raw explanations\n",
|
||||
" raw_local_importance_values = scoring_explainer.explain(\n",
|
||||
" automl_explainer_setup_obj.X_test_transform, get_raw=True\n",
|
||||
" )\n",
|
||||
" # You can return any data type as long as it is JSON-serializable\n",
|
||||
" return {\n",
|
||||
" \"predictions\": predictions.tolist(),\n",
|
||||
" \"engineered_local_importance_values\": engineered_local_importance_values,\n",
|
||||
" \"raw_local_importance_values\": raw_local_importance_values,\n",
|
||||
" }"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create the InferenceConfig \n",
|
||||
"Create the inference config that will be used when deploying the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inf_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Provision the AKS Cluster\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AksCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your cluster.\n",
|
||||
"aks_name = \"scoring-explain\"\n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
|
||||
" print(\"Found existing cluster, use it.\")\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" prov_config = AksCompute.provisioning_configuration(vm_size=\"STANDARD_D3_V2\")\n",
|
||||
" aks_target = ComputeTarget.create(\n",
|
||||
" workspace=ws, name=aks_name, provisioning_configuration=prov_config\n",
|
||||
" )\n",
|
||||
"aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Deploy web service to AKS"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set the web service configuration (using default here)\n",
|
||||
"from azureml.core.webservice import AksWebservice\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service_name = \"model-scoring-local-aks\"\n",
|
||||
"\n",
|
||||
"aks_service = Model.deploy(\n",
|
||||
" workspace=ws,\n",
|
||||
" name=aks_service_name,\n",
|
||||
" models=[scoring_explainer_model, original_model],\n",
|
||||
" inference_config=inf_config,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=aks_target,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output=True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View the service logs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service.get_logs()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Consume the web service using run method to do the scoring and explanation of scoring.\n",
|
||||
"We test the web sevice by passing data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Serialize the first row of the test data into json\n",
|
||||
"X_test_json = X_test_df[:1].to_json(orient=\"records\")\n",
|
||||
"print(X_test_json)\n",
|
||||
"\n",
|
||||
"# Call the service to get the predictions and the engineered and raw explanations\n",
|
||||
"output = aks_service.run(X_test_json)\n",
|
||||
"\n",
|
||||
"# Print the predicted value\n",
|
||||
"print(\"predictions:\\n{}\\n\".format(output[\"predictions\"]))\n",
|
||||
"# Print the engineered feature importances for the predicted value\n",
|
||||
"print(\n",
|
||||
" \"engineered_local_importance_values:\\n{}\\n\".format(\n",
|
||||
" output[\"engineered_local_importance_values\"]\n",
|
||||
" )\n",
|
||||
")\n",
|
||||
"# Print the raw feature importances for the predicted value\n",
|
||||
"print(\n",
|
||||
" \"raw_local_importance_values:\\n{}\\n\".format(output[\"raw_local_importance_values\"])\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Clean up\n",
|
||||
"Delete the service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
||||
@@ -1,4 +0,0 @@
|
||||
name: auto-ml-classification-credit-card-fraud-local
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -1,4 +0,0 @@
|
||||
name: auto-ml-regression-explanation-featurization
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -1,4 +0,0 @@
|
||||
name: auto-ml-regression
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -1,217 +0,0 @@
|
||||
|
||||
NOTICES AND INFORMATION
|
||||
Do Not Translate or Localize
|
||||
|
||||
This Azure Machine Learning service example notebooks repository includes material from the projects listed below.
|
||||
|
||||
|
||||
1. SSD-Tensorflow (https://github.com/balancap/ssd-tensorflow)
|
||||
|
||||
|
||||
%% SSD-Tensorflow NOTICES AND INFORMATION BEGIN HERE
|
||||
=========================================
|
||||
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
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|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
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END OF TERMS AND CONDITIONS
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APPENDIX: How to apply the Apache License to your work.
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To apply the Apache License to your work, attach the following
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|
||||
=========================================
|
||||
END OF SSD-Tensorflow NOTICES AND INFORMATION
|
||||
@@ -1,104 +0,0 @@
|
||||
|
||||
# Notebooks for Microsoft Azure Machine Learning Hardware Accelerated Models SDK
|
||||
|
||||
Easily create and train a model using various deep neural networks (DNNs) as a featurizer for deployment to Azure or a Data Box Edge device for ultra-low latency inferencing using FPGA's. These models are currently available:
|
||||
|
||||
* ResNet 50
|
||||
* ResNet 152
|
||||
* DenseNet-121
|
||||
* VGG-16
|
||||
* SSD-VGG
|
||||
|
||||
To learn more about the azureml-accel-model classes, see the section [Model Classes](#model-classes) below or the [Azure ML Accel Models SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py).
|
||||
|
||||
### Step 1: Create an Azure ML workspace
|
||||
Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
|
||||
|
||||
### Step 2: Check your FPGA quota
|
||||
Use the Azure CLI to check whether you have quota.
|
||||
|
||||
```shell
|
||||
az vm list-usage --location "eastus" -o table
|
||||
```
|
||||
|
||||
The other locations are ``southeastasia``, ``westeurope``, and ``westus2``.
|
||||
|
||||
Under the "Name" column, look for "Standard PBS Family vCPUs" and ensure you have at least 6 vCPUs under "CurrentValue."
|
||||
|
||||
If you do not have quota, then submit a request form [here](https://aka.ms/accelerateAI).
|
||||
|
||||
### Step 3: Install the Azure ML Accelerated Models SDK
|
||||
Once you have set up your environment, install the Azure ML Accel Models SDK. This package requires tensorflow >= 1.6,<2.0 to be installed.
|
||||
|
||||
If you already have tensorflow >= 1.6,<2.0 installed in your development environment, you can install the SDK package using:
|
||||
|
||||
```
|
||||
pip install azureml-accel-models
|
||||
```
|
||||
|
||||
If you do not have tensorflow >= 1.6,<2.0 and are using a CPU-only development environment, our SDK with tensorflow can be installed using:
|
||||
|
||||
```
|
||||
pip install azureml-accel-models[cpu]
|
||||
```
|
||||
|
||||
If your machine supports GPU (for example, on an [Azure DSVM](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview)), then you can leverage the tensorflow-gpu functionality using:
|
||||
|
||||
```
|
||||
pip install azureml-accel-models[gpu]
|
||||
```
|
||||
|
||||
### Step 4: Follow our notebooks
|
||||
|
||||
We provide notebooks to walk through the following scenarios, linked below:
|
||||
* [Quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
|
||||
* [Object Detection](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
|
||||
* [Training models](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
|
||||
|
||||
**Note**: the above notebooks work only for tensorflow >= 1.6,<2.0.
|
||||
|
||||
<a name="model-classes"></a>
|
||||
## Model Classes
|
||||
As stated above, we support 5 Accelerated Models. Here's more information on their input and output tensors.
|
||||
|
||||
**Available models and output tensors**
|
||||
|
||||
The available models and the corresponding default classifier output tensors are below. This is the value that you would use during inferencing if you used the default classifier.
|
||||
* Resnet50, QuantizedResnet50
|
||||
``
|
||||
output_tensors = "classifier_1/resnet_v1_50/predictions/Softmax:0"
|
||||
``
|
||||
* Resnet152, QuantizedResnet152
|
||||
``
|
||||
output_tensors = "classifier/resnet_v1_152/predictions/Softmax:0"
|
||||
``
|
||||
* Densenet121, QuantizedDensenet121
|
||||
``
|
||||
output_tensors = "classifier/densenet121/predictions/Softmax:0"
|
||||
``
|
||||
* Vgg16, QuantizedVgg16
|
||||
``
|
||||
output_tensors = "classifier/vgg_16/fc8/squeezed:0"
|
||||
``
|
||||
* SsdVgg, QuantizedSsdVgg
|
||||
``
|
||||
output_tensors = ['ssd_300_vgg/block4_box/Reshape_1:0', 'ssd_300_vgg/block7_box/Reshape_1:0', 'ssd_300_vgg/block8_box/Reshape_1:0', 'ssd_300_vgg/block9_box/Reshape_1:0', 'ssd_300_vgg/block10_box/Reshape_1:0', 'ssd_300_vgg/block11_box/Reshape_1:0', 'ssd_300_vgg/block4_box/Reshape:0', 'ssd_300_vgg/block7_box/Reshape:0', 'ssd_300_vgg/block8_box/Reshape:0', 'ssd_300_vgg/block9_box/Reshape:0', 'ssd_300_vgg/block10_box/Reshape:0', 'ssd_300_vgg/block11_box/Reshape:0']
|
||||
``
|
||||
|
||||
For more information, please reference the azureml.accel.models package in the [Azure ML Python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.models?view=azure-ml-py).
|
||||
|
||||
**Input tensors**
|
||||
|
||||
The input_tensors value defaults to "Placeholder:0" and is created in the [Image Preprocessing](#construct-model) step in the line:
|
||||
``
|
||||
in_images = tf.placeholder(tf.string)
|
||||
``
|
||||
|
||||
You can change the input_tensors name by doing this:
|
||||
``
|
||||
in_images = tf.placeholder(tf.string, name="images")
|
||||
``
|
||||
|
||||
|
||||
## Resources
|
||||
* [Read more about FPGAs](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas)
|
||||
@@ -1,14 +0,0 @@
|
||||
# Model Deployment with Azure ML service
|
||||
You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization).
|
||||
For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
|
||||
|
||||
## Get Started
|
||||
To begin, you will need an ML workspace.
|
||||
For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
|
||||
|
||||
## Deploy to the cloud
|
||||
You can deploy to the cloud using the Azure ML CLI or the Azure ML SDK.
|
||||
- CLI example: https://aka.ms/azmlcli
|
||||
- Notebook example: [model-register-and-deploy](./model-register-and-deploy.ipynb).
|
||||
|
||||
|
||||
@@ -1,395 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploy Multiple Models as Webservice\n",
|
||||
"\n",
|
||||
"This example shows how to deploy a Webservice with multiple models in step-by-step fashion:\n",
|
||||
"\n",
|
||||
" 1. Register Models\n",
|
||||
" 2. Deploy Models as Webservice"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize Workspace\n",
|
||||
"\n",
|
||||
"Initialize a workspace object from persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"create workspace"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register Models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"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/toy_dataset.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"import sklearn\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import BayesianRidge, Ridge\n",
|
||||
"\n",
|
||||
"x, y = load_diabetes(return_X_y=True)\n",
|
||||
"\n",
|
||||
"first_model = Ridge().fit(x, y)\n",
|
||||
"second_model = BayesianRidge().fit(x, y)\n",
|
||||
"\n",
|
||||
"joblib.dump(first_model, \"first_model.pkl\")\n",
|
||||
"joblib.dump(second_model, \"second_model.pkl\")\n",
|
||||
"\n",
|
||||
"print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"register model from file"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"my_model_1 = Model.register(model_path=\"first_model.pkl\",\n",
|
||||
" model_name=\"my_first_model\",\n",
|
||||
" workspace=ws)\n",
|
||||
"\n",
|
||||
"my_model_2 = Model.register(model_path=\"second_model.pkl\",\n",
|
||||
" model_name=\"my_second_model\",\n",
|
||||
" workspace=ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Write the Entry Script\n",
|
||||
"Write the script that will be used to predict on your models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Model.get_model_path()\n",
|
||||
"\n",
|
||||
"To get the paths of your models, use `Model.get_model_path(model_name, version=None, _workspace=None)` method. This method will find the path to a model using the name of the model registered under the workspace.\n",
|
||||
"\n",
|
||||
"In this example, we do not use the optional arguments `version` and `_workspace`.\n",
|
||||
"\n",
|
||||
"#### Using environment variable AZUREML_MODEL_DIR\n",
|
||||
"\n",
|
||||
"In other [examples](../deploy-to-cloud/score.py) with a single model deployment, we use the environment variable `AZUREML_MODEL_DIR` and model file name to get the model path. \n",
|
||||
"\n",
|
||||
"For single model deployments, this environment variable is the path to the model folder (`./azureml-models/$MODEL_NAME/$VERSION`). When we deploy multiple models, the environment variable is set to the folder containing all models (./azureml-models).\n",
|
||||
"\n",
|
||||
"If you're using multiple models and you know the versions of the models you deploy, you can use this method to get the model path:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# Construct the model path using the registered model name, version, and model file name\n",
|
||||
"model_1_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'my_first_model', '1', 'first_model.pkl')\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model_1, model_2\n",
|
||||
" # Here \"my_first_model\" is the name of the model registered under the workspace.\n",
|
||||
" # This call will return the path to the .pkl file on the local disk.\n",
|
||||
" model_1_path = Model.get_model_path(model_name='my_first_model')\n",
|
||||
" model_2_path = Model.get_model_path(model_name='my_second_model')\n",
|
||||
" \n",
|
||||
" # Deserialize the model files back into scikit-learn models.\n",
|
||||
" model_1 = joblib.load(model_1_path)\n",
|
||||
" model_2 = joblib.load(model_2_path)\n",
|
||||
"\n",
|
||||
"# Note you can pass in multiple rows for scoring.\n",
|
||||
"def run(raw_data):\n",
|
||||
" try:\n",
|
||||
" data = json.loads(raw_data)['data']\n",
|
||||
" data = np.array(data)\n",
|
||||
" \n",
|
||||
" # Call predict() on each model\n",
|
||||
" result_1 = model_1.predict(data)\n",
|
||||
" result_2 = model_2.predict(data)\n",
|
||||
"\n",
|
||||
" # You can return any JSON-serializable value.\n",
|
||||
" return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n",
|
||||
" except Exception as e:\n",
|
||||
" result = str(e)\n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create Environment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Please note that your environment must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service.\n",
|
||||
"\n",
|
||||
"More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"env = Environment(\"deploytocloudenv\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"joblib\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"numpy==1.23\")\n",
|
||||
"env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create Inference Configuration\n",
|
||||
"\n",
|
||||
"There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n",
|
||||
"Note: in that case, environments's entry_script and file_path are relative paths to the source_directory path; myenv.docker.base_dockerfile is a string containing extra docker steps or contents of the docker file.\n",
|
||||
"\n",
|
||||
"Sample code for using a source directory:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n",
|
||||
"\n",
|
||||
"# explicitly set base_image to None when setting base_dockerfile\n",
|
||||
"myenv.docker.base_image = None\n",
|
||||
"# add extra docker commends to execute\n",
|
||||
"myenv.docker.base_dockerfile = \"FROM ubuntu\\n RUN echo \\\"hello\\\"\"\n",
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
|
||||
" entry_script=\"x/y/score.py\",\n",
|
||||
" environment=myenv)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
" - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
|
||||
" - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n",
|
||||
" - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
|
||||
" - entry_script: contains logic specific to initializing your model and running predictions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"create image"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploy Model as Webservice on Azure Container Instance\n",
|
||||
"\n",
|
||||
"Note that the service creation can take few minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"azuremlexception-remarks-sample"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"\n",
|
||||
"aci_service_name = \"aciservice-multimodel\"\n",
|
||||
"\n",
|
||||
"deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
|
||||
"\n",
|
||||
"service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n",
|
||||
"service.wait_for_deployment(True)\n",
|
||||
"\n",
|
||||
"print(service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Test web service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"test_sample = json.dumps({'data': x[0:2].tolist()})\n",
|
||||
"\n",
|
||||
"prediction = service.run(test_sample)\n",
|
||||
"\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Delete ACI to clean up"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"service.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "jenns"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
name: multi-model-register-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- scikit-learn
|
||||
@@ -1,12 +0,0 @@
|
||||
# Model Deployment with Azure ML service
|
||||
You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization).
|
||||
For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
|
||||
|
||||
## Get Started
|
||||
To begin, you will need an ML workspace.
|
||||
For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
|
||||
|
||||
## Deploy locally
|
||||
You can deploy a model locally for testing & debugging using the Azure ML CLI or the Azure ML SDK.
|
||||
- CLI example: https://aka.ms/azmlcli
|
||||
- Notebook example: [register-model-deploy-local](./register-model-deploy-local.ipynb).
|
||||
@@ -1 +0,0 @@
|
||||
RUN echo "this is test"
|
||||
@@ -1,495 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register model and deploy locally with advanced usages\n",
|
||||
"\n",
|
||||
"This example shows how to deploy a web service in step-by-step fashion:\n",
|
||||
"\n",
|
||||
" 1. Register model\n",
|
||||
" 2. Deploy the image as a web service in a local Docker container.\n",
|
||||
" 3. Quickly test changes to your entry script by reloading the local service.\n",
|
||||
" 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize Workspace\n",
|
||||
"\n",
|
||||
"Initialize a workspace object from persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"create workspace"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create trained model\n",
|
||||
"\n",
|
||||
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"\n",
|
||||
"dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
|
||||
"\n",
|
||||
"sk_model = Ridge().fit(dataset_x, dataset_y)\n",
|
||||
"\n",
|
||||
"joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\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, framework, category, target customer etc. Note that tags must be alphanumeric."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"register model from file",
|
||||
"sample-model-register"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
|
||||
" model_name=\"sklearn_regression_model\",\n",
|
||||
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
|
||||
" description=\"Ridge regression model to predict diabetes\",\n",
|
||||
" workspace=ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Manage your dependencies in a folder"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"source_directory = \"source_directory\"\n",
|
||||
"\n",
|
||||
"os.makedirs(source_directory, exist_ok=True)\n",
|
||||
"os.makedirs(os.path.join(source_directory, \"x/y\"), exist_ok=True)\n",
|
||||
"os.makedirs(os.path.join(source_directory, \"env\"), exist_ok=True)\n",
|
||||
"os.makedirs(os.path.join(source_directory, \"dockerstep\"), exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Show `score.py`. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile source_directory/x/y/score.py\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment. Join this path with the filename of the model file.\n",
|
||||
" # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
" global name\n",
|
||||
" # Note here, the entire source directory from inference config gets added into image.\n",
|
||||
" # Below is an example of how you can use any extra files in image.\n",
|
||||
" with open('./source_directory/extradata.json') as json_file:\n",
|
||||
" data = json.load(json_file)\n",
|
||||
" name = data[\"people\"][0][\"name\"]\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
"@output_schema(NumpyParameterType(output_sample))\n",
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return \"Hello \" + name + \" here is your result = \" + str(result)\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile source_directory/extradata.json\n",
|
||||
"{\n",
|
||||
" \"people\": [\n",
|
||||
" {\n",
|
||||
" \"website\": \"microsoft.com\", \n",
|
||||
" \"from\": \"Seattle\", \n",
|
||||
" \"name\": \"Mrudula\"\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create Inference Configuration\n",
|
||||
"\n",
|
||||
" - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
|
||||
" - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n",
|
||||
" - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
|
||||
" - entry_script: contains logic specific to initializing your model and running predictions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sklearn\n",
|
||||
"\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"myenv = Environment('myenv')\n",
|
||||
"myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
|
||||
"myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n",
|
||||
"myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n",
|
||||
"\n",
|
||||
"# explicitly set base_image to None when setting base_dockerfile\n",
|
||||
"myenv.docker.base_image = None\n",
|
||||
"myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\\nRUN echo \\\"this is test\\\"\"\n",
|
||||
"myenv.inferencing_stack_version = \"latest\"\n",
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(source_directory=source_directory,\n",
|
||||
" entry_script=\"x/y/score.py\",\n",
|
||||
" environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy Model as a Local Docker Web Service\n",
|
||||
"\n",
|
||||
"*Make sure you have Docker installed and running.*\n",
|
||||
"\n",
|
||||
"Note that the service creation can take few minutes.\n",
|
||||
"\n",
|
||||
"NOTE:\n",
|
||||
"\n",
|
||||
"The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
|
||||
"\n",
|
||||
" # PowerShell command to switch to Linux engine\n",
|
||||
" & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"deploy service",
|
||||
"aci"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import LocalWebservice\n",
|
||||
"\n",
|
||||
"# This is optional, if not provided Docker will choose a random unused port.\n",
|
||||
"deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
|
||||
"\n",
|
||||
"local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
|
||||
"\n",
|
||||
"local_service.wait_for_deployment()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print('Local service port: {}'.format(local_service.port))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Check Status and Get Container Logs\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(local_service.get_logs())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test Web Service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the web service with some input data to get a prediction."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"sample_input = json.dumps({\n",
|
||||
" 'data': dataset_x[0:2].tolist()\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
"print(local_service.run(sample_input))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Reload Service\n",
|
||||
"\n",
|
||||
"You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile source_directory/x/y/score.py\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
" global name, from_location\n",
|
||||
" # Note here, the entire source directory from inference config gets added into image.\n",
|
||||
" # Below is an example of how you can use any extra files in image.\n",
|
||||
" with open('source_directory/extradata.json') as json_file: \n",
|
||||
" data = json.load(json_file)\n",
|
||||
" name = data[\"people\"][0][\"name\"]\n",
|
||||
" from_location = data[\"people\"][0][\"from\"]\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
"@output_schema(NumpyParameterType(output_sample))\n",
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_service.reload()\n",
|
||||
"print(\"--------------------------------------------------------------\")\n",
|
||||
"\n",
|
||||
"# After calling reload(), run() will return the updated message.\n",
|
||||
"local_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Update Service\n",
|
||||
"\n",
|
||||
"If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"\n",
|
||||
"local_service.update(models=[SomeOtherModelObject],\n",
|
||||
" deployment_config=local_config,\n",
|
||||
" inference_config=inference_config)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Delete Service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_service.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "keriehm"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,556 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register model and deploy locally\n",
|
||||
"\n",
|
||||
"This example shows how to deploy a web service in step-by-step fashion:\n",
|
||||
"\n",
|
||||
" 1. Register model\n",
|
||||
" 2. Deploy the image as a web service in a local Docker container.\n",
|
||||
" 3. Quickly test changes to your entry script by reloading the local service.\n",
|
||||
" 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize Workspace\n",
|
||||
"\n",
|
||||
"Initialize a workspace object from persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create trained model\n",
|
||||
"\n",
|
||||
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import joblib\n",
|
||||
"\n",
|
||||
"from sklearn.datasets import load_diabetes\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"\n",
|
||||
"dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
|
||||
"\n",
|
||||
"sk_model = Ridge().fit(dataset_x, dataset_y)\n",
|
||||
"\n",
|
||||
"joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
|
||||
"\n",
|
||||
"You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"register model from file"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
|
||||
" model_name=\"sklearn_regression_model\",\n",
|
||||
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
|
||||
" description=\"Ridge regression model to predict diabetes\",\n",
|
||||
" workspace=ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create Environment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sklearn\n",
|
||||
"\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"\n",
|
||||
"environment = Environment(\"LocalDeploy\")\n",
|
||||
"environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
|
||||
"environment.python.conda_dependencies.add_pip_package(\"joblib\")\n",
|
||||
"environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Provide the Scoring Script\n",
|
||||
"\n",
|
||||
"This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
"@output_schema(NumpyParameterType(output_sample))\n",
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return result.tolist()\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create Inference Configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\",\n",
|
||||
" environment=environment)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy Model as a Local Docker Web Service\n",
|
||||
"\n",
|
||||
"*Make sure you have Docker installed and running.*\n",
|
||||
"\n",
|
||||
"Note that the service creation can take few minutes.\n",
|
||||
"\n",
|
||||
"NOTE:\n",
|
||||
"\n",
|
||||
"The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
|
||||
"\n",
|
||||
" # PowerShell command to switch to Linux engine\n",
|
||||
" & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-localwebservice-deploy"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import LocalWebservice\n",
|
||||
"\n",
|
||||
"# This is optional, if not provided Docker will choose a random unused port.\n",
|
||||
"deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
|
||||
"\n",
|
||||
"local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
|
||||
"\n",
|
||||
"local_service.wait_for_deployment()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print('Local service port: {}'.format(local_service.port))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Check Status and Get Container Logs\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(local_service.get_logs())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test Web Service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the web service with some input data to get a prediction."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"sample_input = json.dumps({\n",
|
||||
" 'data': dataset_x[0:2].tolist()\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
"local_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Reload Service\n",
|
||||
"\n",
|
||||
"You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import joblib\n",
|
||||
"import json\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # Deserialize the model file back into a sklearn model.\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
|
||||
"output_sample = np.array([3726.995])\n",
|
||||
"\n",
|
||||
"@input_schema('data', NumpyParameterType(input_sample))\n",
|
||||
"@output_schema(NumpyParameterType(output_sample))\n",
|
||||
"def run(data):\n",
|
||||
" try:\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # You can return any JSON-serializable object.\n",
|
||||
" return 'Hello from the updated score.py: ' + str(result.tolist())\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_service.reload()\n",
|
||||
"print(\"--------------------------------------------------------------\")\n",
|
||||
"\n",
|
||||
"# After calling reload(), run() will return the updated message.\n",
|
||||
"local_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Update Service\n",
|
||||
"\n",
|
||||
"If you want to change your model(s), Conda dependencies or deployment configuration, call `update()` to rebuild the Docker image.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_service.update(models=[model],\n",
|
||||
" inference_config=inference_config,\n",
|
||||
" deployment_config=deployment_config)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy model to AKS cluster based on the LocalWebservice's configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This is a one time setup for AKS Cluster. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
|
||||
"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your AKS cluster\n",
|
||||
"aks_name = 'my-aks-9' \n",
|
||||
"\n",
|
||||
"# Verify the cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" # Use the default configuration (can also provide parameters to customize)\n",
|
||||
" prov_config = AksCompute.provisioning_configuration()\n",
|
||||
"\n",
|
||||
" # Create the cluster\n",
|
||||
" aks_target = ComputeTarget.create(workspace = ws, \n",
|
||||
" name = aks_name, \n",
|
||||
" provisioning_configuration = prov_config)\n",
|
||||
"\n",
|
||||
"if aks_target.get_status() != \"Succeeded\":\n",
|
||||
" aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AksWebservice\n",
|
||||
"# Set the web service configuration (using default here)\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()\n",
|
||||
"\n",
|
||||
"# # Enable token auth and disable (key) auth on the webservice\n",
|
||||
"# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service_name ='aks-service-1'\n",
|
||||
"\n",
|
||||
"aks_service = local_service.deploy_to_cloud(name=aks_service_name,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=aks_target)\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output = True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test aks service\n",
|
||||
"\n",
|
||||
"sample_input = json.dumps({\n",
|
||||
" 'data': dataset_x[0:2].tolist()\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
"aks_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Delete the service if not needed.\n",
|
||||
"aks_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Delete Service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_service.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "keriehm"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"compute": [
|
||||
"Local"
|
||||
],
|
||||
"datasets": [
|
||||
"None"
|
||||
],
|
||||
"deployment": [
|
||||
"Local"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"None"
|
||||
],
|
||||
"friendly_name": "Register a model and deploy locally",
|
||||
"index_order": 1,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"star_tag": [],
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Deployment"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,498 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Enabling App Insights for Services in Production\n",
|
||||
"With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## What does Application Insights monitor?\n",
|
||||
"It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## What is different compared to standard production deployment process?\n",
|
||||
"If you want to enable generic App Insights for a service run:\n",
|
||||
"```python\n",
|
||||
"aks_service= Webservice(ws, \"aks-w-dc2\")\n",
|
||||
"aks_service.update(enable_app_insights=True)```\n",
|
||||
"Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n",
|
||||
"\n",
|
||||
"If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
|
||||
"1. Update scoring file.\n",
|
||||
"2. Update aks configuration.\n",
|
||||
"3. Deploy the model with this new configuration. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Import your dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"from azureml.core import Workspace\n",
|
||||
"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
||||
"from azureml.core.webservice import AksWebservice\n",
|
||||
"\n",
|
||||
"print(azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Set up your configuration and create a workspace\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. Register Model\n",
|
||||
"Register an existing trained model, add descirption and tags."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(model_path=\"sklearn_regression_model.pkl\", # This points to a local file.\n",
|
||||
" model_name=\"sklearn_regression_model.pkl\", # This is the name the model is registered as.\n",
|
||||
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
|
||||
" description=\"Ridge regression model to predict diabetes\",\n",
|
||||
" workspace=ws)\n",
|
||||
"\n",
|
||||
"print(model.name, model.description, model.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 4. *Update your scoring file with custom print statements*\n",
|
||||
"Here is an example:\n",
|
||||
"### a. In your init function add:\n",
|
||||
"```python\n",
|
||||
"print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n",
|
||||
"\n",
|
||||
"### b. In your run function add:\n",
|
||||
"```python\n",
|
||||
"print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import json\n",
|
||||
"import numpy\n",
|
||||
"import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"import time\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" #Print statement for appinsights custom traces:\n",
|
||||
" print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
|
||||
"\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
"\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# note you can pass in multiple rows for scoring\n",
|
||||
"def run(raw_data):\n",
|
||||
" try:\n",
|
||||
" data = json.loads(raw_data)['data']\n",
|
||||
" data = numpy.array(data)\n",
|
||||
" result = model.predict(data)\n",
|
||||
" print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))\n",
|
||||
" # you can return any datatype as long as it is JSON-serializable\n",
|
||||
" return result.tolist()\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" print (error + time.strftime(\"%H:%M:%S\"))\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 5. *Create myenv.yml file*\n",
|
||||
"Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=['numpy==1.19.5','scikit-learn==0.22.1'],\n",
|
||||
" pip_packages=['azureml-defaults'])\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
" f.write(myenv.serialize_to_string())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 6. Create Inference Configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy to ACI (Optional)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"\n",
|
||||
"aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,\n",
|
||||
" memory_gb=1,\n",
|
||||
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
|
||||
" description=\"Predict diabetes using regression model\",\n",
|
||||
" enable_app_insights=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aci_service_name = \"aci-service-appinsights\"\n",
|
||||
"\n",
|
||||
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config, overwrite=True)\n",
|
||||
"aci_service.wait_for_deployment(show_output=True)\n",
|
||||
"\n",
|
||||
"print(aci_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if aci_service.state == \"Healthy\":\n",
|
||||
" test_sample = json.dumps({\n",
|
||||
" \"data\": [\n",
|
||||
" [1,28,13,45,54,6,57,8,8,10],\n",
|
||||
" [101,9,8,37,6,45,4,3,2,41]\n",
|
||||
" ]\n",
|
||||
" })\n",
|
||||
"\n",
|
||||
" prediction = aci_service.run(test_sample)\n",
|
||||
"\n",
|
||||
" print(prediction)\n",
|
||||
"else:\n",
|
||||
" raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aci_service.error)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 7. Deploy to AKS service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create AKS compute if you haven't done so.\n",
|
||||
"\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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AksCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"aks_name = \"my-aks-insights\"\n",
|
||||
"\n",
|
||||
"creating_compute = False\n",
|
||||
"try:\n",
|
||||
" aks_target = ComputeTarget(ws, aks_name)\n",
|
||||
" print(\"Using existing AKS compute target {}.\".format(aks_name))\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"Creating a new AKS compute target {}.\".format(aks_name))\n",
|
||||
"\n",
|
||||
" # Use the default configuration (can also provide parameters to customize).\n",
|
||||
" prov_config = AksCompute.provisioning_configuration()\n",
|
||||
" aks_target = ComputeTarget.create(workspace=ws,\n",
|
||||
" name=aks_name,\n",
|
||||
" provisioning_configuration=prov_config)\n",
|
||||
" creating_compute = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n",
|
||||
" aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(aks_target.provisioning_state)\n",
|
||||
"print(aks_target.provisioning_errors)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you already have a cluster you can attach the service to it:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"%%time\n",
|
||||
"resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
|
||||
"create_name= 'myaks4'\n",
|
||||
"attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
|
||||
"aks_target = ComputeTarget.attach(workspace=ws,\n",
|
||||
" name=create_name,\n",
|
||||
" attach_configuration=attach_config)\n",
|
||||
"## Wait for the operation to complete\n",
|
||||
"aks_target.wait_for_provisioning(True)```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### a. *Activate App Insights through updating AKS Webservice configuration*\n",
|
||||
"In order to enable App Insights in your service you will need to update your AKS configuration file:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set the web service configuration.\n",
|
||||
"aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### b. Deploy your service"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if aks_target.provisioning_state == \"Succeeded\":\n",
|
||||
" aks_service_name = \"aks-service-appinsights\"\n",
|
||||
" aks_service = Model.deploy(ws,\n",
|
||||
" aks_service_name,\n",
|
||||
" [model],\n",
|
||||
" inference_config,\n",
|
||||
" aks_deployment_config,\n",
|
||||
" deployment_target=aks_target,\n",
|
||||
" overwrite=True)\n",
|
||||
" aks_service.wait_for_deployment(show_output=True)\n",
|
||||
" print(aks_service.state)\n",
|
||||
"else:\n",
|
||||
" raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 8. Test your service "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"\n",
|
||||
"if aks_service.state == \"Healthy\":\n",
|
||||
" test_sample = json.dumps({\n",
|
||||
" \"data\": [\n",
|
||||
" [1,28,13,45,54,6,57,8,8,10],\n",
|
||||
" [101,9,8,37,6,45,4,3,2,41]\n",
|
||||
" ]\n",
|
||||
" })\n",
|
||||
"\n",
|
||||
" prediction = aks_service.run(input_data=test_sample)\n",
|
||||
" print(prediction)\n",
|
||||
"else:\n",
|
||||
" raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aks_service.error)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 9. See your service telemetry in App Insights\n",
|
||||
"1. Go to the [Azure Portal](https://portal.azure.com/)\n",
|
||||
"2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n",
|
||||
"3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n",
|
||||
"4. Click on the top banner \"Analytics\"\n",
|
||||
"5. In the \"Schema\" section select \"traces\" and run your query.\n",
|
||||
"6. Voila! All your custom traces should be there."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Disable App Insights"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service.update(enable_app_insights=False)\n",
|
||||
"aks_service.wait_for_deployment(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Clean up"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service.delete()\n",
|
||||
"aci_service.delete()\n",
|
||||
"model.delete()\n",
|
||||
"if creating_compute:\n",
|
||||
" aks_target.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "gopalv"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,4 +0,0 @@
|
||||
name: enable-app-insights-in-production-service
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
Binary file not shown.
@@ -1,2 +0,0 @@
|
||||
RUN apt-get update
|
||||
RUN apt-get install -y libgomp1
|
||||
@@ -1,39 +0,0 @@
|
||||
# ONNX on Azure Machine Learning
|
||||
|
||||
These tutorials show how to create and deploy Open Neural Network eXchange ([ONNX](http://onnx.ai)) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
|
||||
|
||||
## Tutorials
|
||||
|
||||
0. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
|
||||
|
||||
#### Obtain pretrained models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime
|
||||
1. [MNIST - Handwritten Digit Classification with ONNX Runtime](onnx-inference-mnist-deploy.ipynb)
|
||||
2. [Emotion FER+ - Facial Expression Recognition with ONNX Runtime](onnx-inference-facial-expression-recognition-deploy.ipynb)
|
||||
|
||||
#### Train model on Azure ML, convert to ONNX, and deploy with ONNX Runtime
|
||||
3. [MNIST - Train using PyTorch and deploy with ONNX Runtime](onnx-train-pytorch-aml-deploy-mnist.ipynb)
|
||||
|
||||
#### Demo Notebooks from Microsoft Ignite 2018
|
||||
Note that the following notebooks do not have evaluation sections for the models since they were deployed as part of a live demo. You can find the respective pre-processing and post-processing code linked from the ONNX Model Zoo Github pages ([ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [TinyYoloV2](https://github.com/onnx/models/tree/master/tiny_yolov2)), or experiment with the ONNX models by [running them in the browser](https://microsoft.github.io/onnxjs-demo/#/).
|
||||
|
||||
4. [ResNet50 - Image Recognition with ONNX Runtime](onnx-modelzoo-aml-deploy-resnet50.ipynb)
|
||||
5. [TinyYoloV2 - Convert from CoreML and deploy with ONNX Runtime](onnx-convert-aml-deploy-tinyyolo.ipynb)
|
||||
|
||||
## Documentation
|
||||
- [ONNX Runtime Python API Documentation](http://aka.ms/onnxruntime-python)
|
||||
- [Azure Machine Learning API Documentation](http://aka.ms/aml-docs)
|
||||
|
||||
## Related Articles
|
||||
- [Building and Deploying ONNX Runtime Models](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx)
|
||||
- [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview)
|
||||
- [What’s new in Azure Machine Learning](https://aka.ms/aml-blog-whats-new)
|
||||
|
||||
## License
|
||||
Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
Licensed under the MIT License.
|
||||
|
||||
## Acknowledgements
|
||||
These tutorials were developed by Vinitra Swamy and Prasanth Pulavarthi of the Microsoft AI Frameworks team and adapted for presentation at Microsoft Ignite 2018.
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
@@ -1,135 +0,0 @@
|
||||
# This is a modified version of https://github.com/pytorch/examples/blob/master/mnist/main.py which is
|
||||
# licensed under BSD 3-Clause (https://github.com/pytorch/examples/blob/master/LICENSE)
|
||||
|
||||
from __future__ import print_function
|
||||
import argparse
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
from torchvision import datasets, transforms
|
||||
import os
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
|
||||
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
|
||||
self.conv2_drop = nn.Dropout2d()
|
||||
self.fc1 = nn.Linear(320, 50)
|
||||
self.fc2 = nn.Linear(50, 10)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(F.max_pool2d(self.conv1(x), 2))
|
||||
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
|
||||
x = x.view(-1, 320)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = F.dropout(x, training=self.training)
|
||||
x = self.fc2(x)
|
||||
return F.log_softmax(x, dim=1)
|
||||
|
||||
|
||||
def train(args, model, device, train_loader, optimizer, epoch, output_dir):
|
||||
model.train()
|
||||
for batch_idx, (data, target) in enumerate(train_loader):
|
||||
data, target = data.to(device), target.to(device)
|
||||
optimizer.zero_grad()
|
||||
output = model(data)
|
||||
loss = F.nll_loss(output, target)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
if batch_idx % args.log_interval == 0:
|
||||
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
|
||||
epoch, batch_idx * len(data), len(train_loader.dataset),
|
||||
100. * batch_idx / len(train_loader), loss.item()))
|
||||
|
||||
|
||||
def test(args, model, device, test_loader):
|
||||
model.eval()
|
||||
test_loss = 0
|
||||
correct = 0
|
||||
with torch.no_grad():
|
||||
for data, target in test_loader:
|
||||
data, target = data.to(device), target.to(device)
|
||||
output = model(data)
|
||||
test_loss += F.nll_loss(output, target, size_average=False, reduce=True).item() # sum up batch loss
|
||||
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
|
||||
correct += pred.eq(target.view_as(pred)).sum().item()
|
||||
|
||||
test_loss /= len(test_loader.dataset)
|
||||
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
|
||||
test_loss, correct, len(test_loader.dataset),
|
||||
100. * correct / len(test_loader.dataset)))
|
||||
|
||||
|
||||
def main():
|
||||
# Training settings
|
||||
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
|
||||
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
|
||||
help='input batch size for training (default: 64)')
|
||||
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
|
||||
help='input batch size for testing (default: 1000)')
|
||||
parser.add_argument('--epochs', type=int, default=5, metavar='N',
|
||||
help='number of epochs to train (default: 5)')
|
||||
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
|
||||
help='learning rate (default: 0.01)')
|
||||
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
|
||||
help='SGD momentum (default: 0.5)')
|
||||
parser.add_argument('--no-cuda', action='store_true', default=False,
|
||||
help='disables CUDA training')
|
||||
parser.add_argument('--seed', type=int, default=1, metavar='S',
|
||||
help='random seed (default: 1)')
|
||||
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
|
||||
help='how many batches to wait before logging training status')
|
||||
parser.add_argument('--output-dir', type=str, default='outputs')
|
||||
args = parser.parse_args()
|
||||
use_cuda = not args.no_cuda and torch.cuda.is_available()
|
||||
|
||||
torch.manual_seed(args.seed)
|
||||
|
||||
device = torch.device("cuda" if use_cuda else "cpu")
|
||||
|
||||
output_dir = args.output_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
|
||||
# MNIST dataset
|
||||
datasets.MNIST.resources = [
|
||||
("train-images-idx3-ubyte.gz",
|
||||
"f68b3c2dcbeaaa9fbdd348bbdeb94873"),
|
||||
("train-labels-idx1-ubyte.gz",
|
||||
"d53e105ee54ea40749a09fcbcd1e9432"),
|
||||
("t10k-images-idx3-ubyte.gz",
|
||||
"9fb629c4189551a2d022fa330f9573f3"),
|
||||
("t10k-labels-idx1-ubyte.gz",
|
||||
"ec29112dd5afa0611ce80d1b7f02629c")
|
||||
]
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
datasets.MNIST('data', train=True, download=True,
|
||||
transform=transforms.Compose([transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))])
|
||||
),
|
||||
batch_size=args.batch_size, shuffle=True, **kwargs)
|
||||
test_loader = torch.utils.data.DataLoader(
|
||||
datasets.MNIST('data', train=False,
|
||||
transform=transforms.Compose([transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))])
|
||||
),
|
||||
batch_size=args.test_batch_size, shuffle=True, **kwargs)
|
||||
|
||||
model = Net().to(device)
|
||||
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
|
||||
|
||||
for epoch in range(1, args.epochs + 1):
|
||||
train(args, model, device, train_loader, optimizer, epoch, output_dir)
|
||||
test(args, model, device, test_loader)
|
||||
|
||||
# save model
|
||||
dummy_input = torch.randn(1, 1, 28, 28, device=device)
|
||||
model_path = os.path.join(output_dir, 'mnist.onnx')
|
||||
torch.onnx.export(model, dummy_input, model_path)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -1,434 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# YOLO Real-time Object Detection using ONNX on AzureML\n",
|
||||
"\n",
|
||||
"This example shows how to convert the TinyYOLO model from CoreML to ONNX and operationalize it as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
|
||||
"\n",
|
||||
"## What is ONNX\n",
|
||||
"ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
|
||||
"\n",
|
||||
"## YOLO Details\n",
|
||||
"You Only Look Once (YOLO) is a state-of-the-art, real-time object detection system. For more information about YOLO, please visit the [YOLO website](https://pjreddie.com/darknet/yolo/)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"\n",
|
||||
"To make the best use of your time, make sure you have done the following:\n",
|
||||
"\n",
|
||||
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
|
||||
"* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook to:\n",
|
||||
" * install the AML SDK\n",
|
||||
" * create a workspace and its configuration file (config.json)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Install necessary packages\n",
|
||||
"\n",
|
||||
"You'll need to run the following commands to use this tutorial:\n",
|
||||
"\n",
|
||||
"```sh\n",
|
||||
"pip install onnxmltools\n",
|
||||
"pip install coremltools # use this on Linux and Mac\n",
|
||||
"pip install git+https://github.com/apple/coremltools # use this on Windows\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Convert model to ONNX\n",
|
||||
"\n",
|
||||
"First we download the CoreML model. We use the CoreML model from [Matthijs Hollemans's tutorial](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). This may take a few minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import urllib.request\n",
|
||||
"\n",
|
||||
"coreml_model_url = \"https://github.com/hollance/YOLO-CoreML-MPSNNGraph/raw/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel\"\n",
|
||||
"urllib.request.urlretrieve(coreml_model_url, filename=\"TinyYOLO.mlmodel\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then we use ONNXMLTools to convert the model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import onnxmltools\n",
|
||||
"import coremltools\n",
|
||||
"\n",
|
||||
"# Load a CoreML model\n",
|
||||
"coreml_model = coremltools.utils.load_spec('TinyYOLO.mlmodel')\n",
|
||||
"\n",
|
||||
"# Convert from CoreML into ONNX\n",
|
||||
"onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n",
|
||||
"\n",
|
||||
"# Fix the preprocessor bias in the ImageScaler\n",
|
||||
"for init in onnx_model.graph.initializer:\n",
|
||||
" if init.name == 'scalerPreprocessor_bias':\n",
|
||||
" init.dims[1] = 1\n",
|
||||
"\n",
|
||||
"# Save ONNX model\n",
|
||||
"onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n",
|
||||
"\n",
|
||||
"import os\n",
|
||||
"print(os.path.getsize('tinyyolov2.onnx'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploying as a web service with Azure ML\n",
|
||||
"\n",
|
||||
"### Load Azure ML workspace\n",
|
||||
"\n",
|
||||
"We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Registering your model with Azure ML\n",
|
||||
"\n",
|
||||
"Now we upload the model and register it in the workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(model_path = \"tinyyolov2.onnx\",\n",
|
||||
" model_name = \"tinyyolov2\",\n",
|
||||
" tags = {\"onnx\": \"demo\"},\n",
|
||||
" description = \"TinyYOLO\",\n",
|
||||
" workspace = ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Displaying your registered models\n",
|
||||
"\n",
|
||||
"You can optionally list out all the models that you have registered in this workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"models = ws.models\n",
|
||||
"for name, m in models.items():\n",
|
||||
" print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Write scoring file\n",
|
||||
"\n",
|
||||
"We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import json\n",
|
||||
"import time\n",
|
||||
"import sys\n",
|
||||
"import os\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"import numpy as np # we're going to use numpy to process input and output data\n",
|
||||
"import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global session\n",
|
||||
" model = Model.get_model_path(model_name = 'tinyyolov2')\n",
|
||||
" session = onnxruntime.InferenceSession(model)\n",
|
||||
"\n",
|
||||
"def preprocess(input_data_json):\n",
|
||||
" # convert the JSON data into the tensor input\n",
|
||||
" return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
|
||||
"\n",
|
||||
"def postprocess(result):\n",
|
||||
" return np.array(result).tolist()\n",
|
||||
"\n",
|
||||
"def run(input_data_json):\n",
|
||||
" try:\n",
|
||||
" start = time.time() # start timer\n",
|
||||
" input_data = preprocess(input_data_json)\n",
|
||||
" input_name = session.get_inputs()[0].name # get the id of the first input of the model \n",
|
||||
" result = session.run([], {input_name: input_data})\n",
|
||||
" end = time.time() # stop timer\n",
|
||||
" return {\"result\": postprocess(result),\n",
|
||||
" \"time\": end - start}\n",
|
||||
" except Exception as e:\n",
|
||||
" result = str(e)\n",
|
||||
" return {\"error\": result}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Setting up inference configuration\n",
|
||||
"First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies \n",
|
||||
"\n",
|
||||
"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
" f.write(myenv.serialize_to_string())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then we create the inference configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploy the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"\n",
|
||||
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
|
||||
" memory_gb = 1, \n",
|
||||
" tags = {'demo': 'onnx'}, \n",
|
||||
" description = 'web service for TinyYOLO ONNX model')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following cell will take a few minutes to run as the model gets packaged up and deployed to ACI."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aci_service_name = 'my-aci-service-tiny-yolo'\n",
|
||||
"print(\"Service\", aci_service_name)\n",
|
||||
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
|
||||
"aci_service.wait_for_deployment(True)\n",
|
||||
"print(aci_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if aci_service.state != 'Healthy':\n",
|
||||
" # run this command for debugging.\n",
|
||||
" print(aci_service.get_logs())\n",
|
||||
" aci_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Success!\n",
|
||||
"\n",
|
||||
"If you've made it this far, you've deployed a working web service that does object detection using an ONNX model. You can get the URL for the webservice with the code below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(aci_service.scoring_uri)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When you are eventually done using the web service, remember to delete it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aci_service.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "viswamy"
|
||||
}
|
||||
],
|
||||
"category": "deployment",
|
||||
"compute": [
|
||||
"local"
|
||||
],
|
||||
"datasets": [
|
||||
"PASCAL VOC"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"ONNX"
|
||||
],
|
||||
"friendly_name": "Convert and deploy TinyYolo with ONNX Runtime",
|
||||
"index_order": 5,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"star_tag": [
|
||||
"featured"
|
||||
],
|
||||
"tags": [
|
||||
"ONNX Converter"
|
||||
],
|
||||
"task": "Object Detection"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: onnx-convert-aml-deploy-tinyyolo
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- git+https://github.com/apple/coremltools@v2.1
|
||||
- onnx<1.7.0
|
||||
- onnxmltools
|
||||
@@ -1 +0,0 @@
|
||||
{"inputs": {"Input3": {"dims": ["1", "1", "28", "28"], "dataType": 1, "rawData": "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"}}, "outputFilter": ["Plus214_Output_0"]}
|
||||
@@ -1,228 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register ONNX model and deploy as webservice\n",
|
||||
"\n",
|
||||
"Following this notebook, you will:\n",
|
||||
"\n",
|
||||
" - Learn how to register an ONNX in your Azure Machine Learning Workspace.\n",
|
||||
" - Deploy your model as a web service in an Azure Container Instance."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"\n",
|
||||
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"# Check core SDK version number.\n",
|
||||
"print('SDK version:', azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize workspace\n",
|
||||
"\n",
|
||||
"Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"create workspace"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register model\n",
|
||||
"\n",
|
||||
"Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-). For this example, we have provided a trained ONNX MNIST model(`mnist-model.onnx` in the notebook's directory).\n",
|
||||
"\n",
|
||||
"In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"register model from file"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(workspace=ws,\n",
|
||||
" model_name='mnist-sample', # Name of the registered model in your workspace.\n",
|
||||
" model_path='mnist-model.onnx', # Local ONNX model to upload and register as a model.\n",
|
||||
" model_framework=Model.Framework.ONNX , # Framework used to create the model.\n",
|
||||
" model_framework_version='1.3', # Version of ONNX used to create the model.\n",
|
||||
" description='Onnx MNIST model')\n",
|
||||
"\n",
|
||||
"print('Name:', model.name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy model\n",
|
||||
"\n",
|
||||
"Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets.\n",
|
||||
"\n",
|
||||
"For this example, we will deploy the ONNX model to an Azure Container Instance (ACI)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Use a default environment (for supported models)\n",
|
||||
"\n",
|
||||
"The Azure Machine Learning service provides a default environment for supported model frameworks, including ONNX, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n",
|
||||
"\n",
|
||||
"**Note**: This step can take several minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Webservice\n",
|
||||
"from azureml.exceptions import WebserviceException\n",
|
||||
"\n",
|
||||
"service_name = 'onnx-mnist-service'\n",
|
||||
"\n",
|
||||
"# Remove any existing service under the same name.\n",
|
||||
"try:\n",
|
||||
" Webservice(ws, service_name).delete()\n",
|
||||
"except WebserviceException:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"service = Model.deploy(ws, service_name, [model])\n",
|
||||
"service.wait_for_deployment(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"After your model is deployed, perform a call to the web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"headers = {'Content-Type': 'application/json', 'Accept': 'application/json'}\n",
|
||||
"\n",
|
||||
"if service.auth_enabled:\n",
|
||||
" headers['Authorization'] = 'Bearer '+ service.get_keys()[0]\n",
|
||||
"elif service.token_auth_enabled:\n",
|
||||
" headers['Authorization'] = 'Bearer '+ service.get_token()[0]\n",
|
||||
"\n",
|
||||
"scoring_uri = service.scoring_uri\n",
|
||||
"print(scoring_uri)\n",
|
||||
"with open('onnx-mnist-predict-input.json', 'rb') as data_file:\n",
|
||||
" response = requests.post(\n",
|
||||
" scoring_uri, data=data_file, headers=headers)\n",
|
||||
"print(response.status_code)\n",
|
||||
"print(response.elapsed)\n",
|
||||
"print(response.json())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When you are finished testing your service, clean up the deployment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"service.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "vaidyas"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,4 +0,0 @@
|
||||
name: onnx-model-register-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -1,416 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# ResNet50 Image Classification using ONNX and AzureML\n",
|
||||
"\n",
|
||||
"This example shows how to deploy the ResNet50 ONNX model as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
|
||||
"\n",
|
||||
"## What is ONNX\n",
|
||||
"ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
|
||||
"\n",
|
||||
"## ResNet50 Details\n",
|
||||
"ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/resnet). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"\n",
|
||||
"To make the best use of your time, make sure you have done the following:\n",
|
||||
"\n",
|
||||
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
|
||||
"* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
|
||||
" * install the AML SDK\n",
|
||||
" * create a workspace and its configuration file (config.json)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check core SDK version number\n",
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Download pre-trained ONNX model from ONNX Model Zoo.\n",
|
||||
"\n",
|
||||
"Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and extract it in the same folder as this tutorial notebook.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import urllib.request\n",
|
||||
"\n",
|
||||
"onnx_model_url = \"https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz\"\n",
|
||||
"urllib.request.urlretrieve(onnx_model_url, filename=\"resnet50v2.tar.gz\")\n",
|
||||
"\n",
|
||||
"!tar xvzf resnet50v2.tar.gz"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploying as a web service with Azure ML"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load your Azure ML workspace\n",
|
||||
"\n",
|
||||
"We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Register your model with Azure ML\n",
|
||||
"\n",
|
||||
"Now we upload the model and register it in the workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(model_path = \"resnet50v2/resnet50v2.onnx\",\n",
|
||||
" model_name = \"resnet50v2\",\n",
|
||||
" tags = {\"onnx\": \"demo\"},\n",
|
||||
" description = \"ResNet50v2 from ONNX Model Zoo\",\n",
|
||||
" workspace = ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Displaying your registered models\n",
|
||||
"\n",
|
||||
"You can optionally list out all the models that you have registered in this workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"models = ws.models\n",
|
||||
"for name, m in models.items():\n",
|
||||
" print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Write scoring file\n",
|
||||
"\n",
|
||||
"We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import json\n",
|
||||
"import time\n",
|
||||
"import sys\n",
|
||||
"import os\n",
|
||||
"import numpy as np # we're going to use numpy to process input and output data\n",
|
||||
"import onnxruntime # to inference ONNX models, we use the ONNX Runtime\n",
|
||||
"\n",
|
||||
"def softmax(x):\n",
|
||||
" x = x.reshape(-1)\n",
|
||||
" e_x = np.exp(x - np.max(x))\n",
|
||||
" return e_x / e_x.sum(axis=0)\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global session\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50v2.onnx')\n",
|
||||
" session = onnxruntime.InferenceSession(model, None)\n",
|
||||
"\n",
|
||||
"def preprocess(input_data_json):\n",
|
||||
" # convert the JSON data into the tensor input\n",
|
||||
" img_data = np.array(json.loads(input_data_json)['data']).astype('float32')\n",
|
||||
" \n",
|
||||
" #normalize\n",
|
||||
" mean_vec = np.array([0.485, 0.456, 0.406])\n",
|
||||
" stddev_vec = np.array([0.229, 0.224, 0.225])\n",
|
||||
" norm_img_data = np.zeros(img_data.shape).astype('float32')\n",
|
||||
" for i in range(img_data.shape[0]):\n",
|
||||
" norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n",
|
||||
"\n",
|
||||
" return norm_img_data\n",
|
||||
"\n",
|
||||
"def postprocess(result):\n",
|
||||
" return softmax(np.array(result)).tolist()\n",
|
||||
"\n",
|
||||
"def run(input_data_json):\n",
|
||||
" try:\n",
|
||||
" start = time.time()\n",
|
||||
" # load in our data which is expected as NCHW 224x224 image\n",
|
||||
" input_data = preprocess(input_data_json)\n",
|
||||
" input_name = session.get_inputs()[0].name # get the id of the first input of the model \n",
|
||||
" result = session.run([], {input_name: input_data})\n",
|
||||
" end = time.time() # stop timer\n",
|
||||
" return {\"result\": postprocess(result),\n",
|
||||
" \"time\": end - start}\n",
|
||||
" except Exception as e:\n",
|
||||
" result = str(e)\n",
|
||||
" return {\"error\": result}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create inference configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First we create a YAML file that specifies which dependencies we would like to see in our container."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime==1.15.1\", \"azureml-core\", \"azureml-defaults\"])\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
" f.write(myenv.serialize_to_string())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Create the inference configuration object. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"from azureml.core.environment import Environment\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploy the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AciWebservice\n",
|
||||
"\n",
|
||||
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
|
||||
" memory_gb = 1, \n",
|
||||
" tags = {'demo': 'onnx'}, \n",
|
||||
" description = 'web service for ResNet50 ONNX model')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following cell will likely take a few minutes to run as well."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from random import randint\n",
|
||||
"\n",
|
||||
"aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
|
||||
"print(\"Service\", aci_service_name)\n",
|
||||
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
|
||||
"aci_service.wait_for_deployment(True)\n",
|
||||
"print(aci_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if aci_service.state != 'Healthy':\n",
|
||||
" # run this command for debugging.\n",
|
||||
" print(aci_service.get_logs())\n",
|
||||
" aci_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Success!\n",
|
||||
"\n",
|
||||
"If you've made it this far, you've deployed a working web service that does image classification using an ONNX model. You can get the URL for the webservice with the code below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(aci_service.scoring_uri)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When you are eventually done using the web service, remember to delete it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aci_service.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "viswamy"
|
||||
}
|
||||
],
|
||||
"category": "deployment",
|
||||
"compute": [
|
||||
"Local"
|
||||
],
|
||||
"datasets": [
|
||||
"ImageNet"
|
||||
],
|
||||
"deployment": [
|
||||
"Azure Container Instance"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"ONNX"
|
||||
],
|
||||
"friendly_name": "Deploy ResNet50 with ONNX Runtime",
|
||||
"index_order": 4,
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"star_tag": [],
|
||||
"tags": [
|
||||
"ONNX Model Zoo"
|
||||
],
|
||||
"task": "Image Classification"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,4 +0,0 @@
|
||||
name: onnx-modelzoo-aml-deploy-resnet50
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
File diff suppressed because one or more lines are too long
@@ -1,5 +0,0 @@
|
||||
name: onnx-train-pytorch-aml-deploy-mnist
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,355 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
|
||||
"This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
|
||||
"We then test and delete the service, image and model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"print(azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Get workspace\n",
|
||||
"Load existing workspace from the config file info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Download the model\n",
|
||||
"\n",
|
||||
"Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. This cell will download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import requests\n",
|
||||
"import shutil\n",
|
||||
"import tarfile\n",
|
||||
"import tempfile\n",
|
||||
"\n",
|
||||
"from io import BytesIO\n",
|
||||
"\n",
|
||||
"model_url = \"http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz\"\n",
|
||||
"\n",
|
||||
"archive_prefix = \"./resnet_v1_fp32_savedmodel_NCHW_jpg/1538686758/\"\n",
|
||||
"target_folder = \"resnet50\"\n",
|
||||
"\n",
|
||||
"if not os.path.exists(target_folder):\n",
|
||||
" response = requests.get(model_url)\n",
|
||||
" archive = tarfile.open(fileobj=BytesIO(response.content))\n",
|
||||
" with tempfile.TemporaryDirectory() as temp_folder:\n",
|
||||
" archive.extractall(temp_folder)\n",
|
||||
" shutil.copytree(os.path.join(temp_folder, archive_prefix), target_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register the model\n",
|
||||
"Register an existing trained model, add description and tags."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"model = Model.register(model_path=\"resnet50\", # This points to the local directory to upload.\n",
|
||||
" model_name=\"resnet50\", # This is the name the model is registered as.\n",
|
||||
" tags={'area': \"Image classification\", 'type': \"classification\"},\n",
|
||||
" description=\"Image classification trained on Imagenet Dataset\",\n",
|
||||
" workspace=ws)\n",
|
||||
"\n",
|
||||
"print(model.name, model.description, model.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Provision the AKS Cluster\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AksCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your GPU cluster\n",
|
||||
"gpu_cluster_name = \"aks-gpu-cluster\"\n",
|
||||
"\n",
|
||||
"# Choose a location for your GPU cluster\n",
|
||||
"gpu_cluster_location = \"eastus\"\n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
|
||||
" print(\"Found existing gpu cluster\")\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"Creating new gpu-cluster\")\n",
|
||||
" \n",
|
||||
" # Specify the configuration for the new cluster\n",
|
||||
" compute_config = AksCompute.provisioning_configuration(cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST,\n",
|
||||
" agent_count=1,\n",
|
||||
" vm_size=\"Standard_NC6s_v3\",\n",
|
||||
" location=gpu_cluster_location)\n",
|
||||
" # Create the cluster with the specified name and configuration\n",
|
||||
" gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
" # Wait for the cluster to complete, show the output log\n",
|
||||
" gpu_cluster.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploy the model as a web service to AKS\n",
|
||||
"\n",
|
||||
"First create a scoring script"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import tensorflow.compat.v1 as tf\n",
|
||||
"import numpy as np\n",
|
||||
"import json\n",
|
||||
"import os\n",
|
||||
"from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
|
||||
"from azureml.contrib.services.aml_response import AMLResponse\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global session\n",
|
||||
" global input_name\n",
|
||||
" global output_name\n",
|
||||
" \n",
|
||||
" session = tf.Session()\n",
|
||||
"\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50')\n",
|
||||
" model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
|
||||
" if len(model.signature_def['serving_default'].inputs) > 1:\n",
|
||||
" raise ValueError(\"This score.py only supports one input\")\n",
|
||||
" input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
|
||||
" output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()]\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"@rawhttp\n",
|
||||
"def run(request):\n",
|
||||
" if request.method == 'POST':\n",
|
||||
" reqBody = request.get_data(False)\n",
|
||||
" resp = score(reqBody)\n",
|
||||
" return AMLResponse(resp, 200)\n",
|
||||
" if request.method == 'GET':\n",
|
||||
" respBody = str.encode(\"GET is not supported\")\n",
|
||||
" return AMLResponse(respBody, 405)\n",
|
||||
" return AMLResponse(\"bad request\", 500)\n",
|
||||
"\n",
|
||||
"def score(data):\n",
|
||||
" result = session.run(output_name, {input_name: [data]})\n",
|
||||
" return json.dumps(result[1].tolist())\n",
|
||||
"\n",
|
||||
"if __name__ == \"__main__\":\n",
|
||||
" init()\n",
|
||||
" with open(\"test_image.jpg\", 'rb') as f:\n",
|
||||
" content = f.read()\n",
|
||||
" print(score(content))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now create the deployment configuration objects and deploy the model as a webservice."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set the web service configuration (using default here)\n",
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"from azureml.core.webservice import AksWebservice\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"from azureml.core.environment import Environment, DEFAULT_GPU_IMAGE\n",
|
||||
"\n",
|
||||
"env = Environment('deploytocloudenv')\n",
|
||||
"# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n",
|
||||
"# for open-sourced GPU base images.\n",
|
||||
"env.docker.base_image = DEFAULT_GPU_IMAGE\n",
|
||||
"env.python.conda_dependencies = CondaDependencies.create(python_version=\"3.8\", pin_sdk_version=False,\n",
|
||||
" conda_packages=['tensorflow-gpu','numpy'],\n",
|
||||
" pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n",
|
||||
"\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()\n",
|
||||
"\n",
|
||||
"# # Enable token auth and disable (key) auth on the webservice\n",
|
||||
"# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service_name ='gpu-rn50'\n",
|
||||
"\n",
|
||||
"aks_service = Model.deploy(workspace=ws,\n",
|
||||
" name=aks_service_name,\n",
|
||||
" models=[model],\n",
|
||||
" inference_config=inference_config,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=gpu_cluster)\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output = True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Test the web service\n",
|
||||
"We test the web sevice by passing the test images content."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"# if (key) auth is enabled, fetch keys and include in the request\n",
|
||||
"key1, key2 = aks_service.get_keys()\n",
|
||||
"\n",
|
||||
"headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
|
||||
"\n",
|
||||
"# # if token auth is enabled, fetch token and include in the request\n",
|
||||
"# access_token, fetch_after = aks_service.get_token()\n",
|
||||
"# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
|
||||
"\n",
|
||||
"test_sample = open('snowleopardgaze.jpg', 'rb').read()\n",
|
||||
"resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Clean up\n",
|
||||
"Delete the service, image, model and compute target"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service.delete()\n",
|
||||
"model.delete()\n",
|
||||
"gpu_cluster.delete()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "vaidyas"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,4 +0,0 @@
|
||||
name: production-deploy-to-aks-gpu
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 61 KiB |
@@ -1,356 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
|
||||
"This notebook shows the steps for deploying a service: registering a model, provisioning a cluster with ssl (one time action), and deploying a service to it. \n",
|
||||
"We then test and delete the service, image and model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
||||
"from azureml.core.webservice import Webservice, AksWebservice\n",
|
||||
"from azureml.core.model import Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"print(azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Get workspace\n",
|
||||
"Load existing workspace from the config file info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register the model\n",
|
||||
"Register an existing trained model, add descirption and tags."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Register the model\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
|
||||
" model_name = \"sklearn_model\", # this is the name the model is registered as\n",
|
||||
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
|
||||
" description = \"Ridge regression model to predict diabetes\",\n",
|
||||
" workspace = ws)\n",
|
||||
"\n",
|
||||
"print(model.name, model.description, model.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create the Environment\n",
|
||||
"Create an environment that the model will be deployed with"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies \n",
|
||||
"\n",
|
||||
"conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.22.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
|
||||
"myenv = Environment(name='myenv')\n",
|
||||
"myenv.python.conda_dependencies = conda_deps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Use a custom Docker image\n",
|
||||
"\n",
|
||||
"You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
|
||||
"\n",
|
||||
"Only supported with `python` runtime.\n",
|
||||
"```python\n",
|
||||
"# use an image available in public Container Registry without authentication\n",
|
||||
"myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
|
||||
"\n",
|
||||
"# or, use an image available in a private Container Registry\n",
|
||||
"myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
|
||||
"myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
|
||||
"myenv.docker.base_image_registry.username = \"username\"\n",
|
||||
"myenv.docker.base_image_registry.password = \"password\"\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Write the Entry Script\n",
|
||||
"Write the script that will be used to predict on your model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score_ssl.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import json\n",
|
||||
"import numpy\n",
|
||||
"import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"from inference_schema.schema_decorators import input_schema, output_schema\n",
|
||||
"from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"standard_sample_input = {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }\n",
|
||||
"standard_sample_output = {'outcome': 1}\n",
|
||||
"\n",
|
||||
"@input_schema('param', StandardPythonParameterType(standard_sample_input))\n",
|
||||
"@output_schema(StandardPythonParameterType(standard_sample_output))\n",
|
||||
"def run(param):\n",
|
||||
" try:\n",
|
||||
" raw_data = [param['a'], param['b'], param['c'], param['d'], param['e'], param['f'], param['g'], param['h'], param['i'], param['j']]\n",
|
||||
" data = numpy.array([raw_data])\n",
|
||||
" result = model.predict(data)\n",
|
||||
" return { 'outcome' : result[0] }\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create the InferenceConfig\n",
|
||||
"Create the inference config that will be used when deploying the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inf_config = InferenceConfig(entry_script='score_ssl.py', environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Provision the AKS Cluster with SSL\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
|
||||
"\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/azure/machine-learning/v1/how-to-secure-web-service) for more details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Use the default configuration (can also provide parameters to customize)\n",
|
||||
"\n",
|
||||
"provisioning_config = AksCompute.provisioning_configuration()\n",
|
||||
"# Leaf domain label generates a name using the formula\n",
|
||||
"# \"<leaf-domain-label>######.<azure-region>.cloudapp.azure.net\"\n",
|
||||
"# where \"######\" is a random series of characters\n",
|
||||
"provisioning_config.enable_ssl(leaf_domain_label = \"contoso\", overwrite_existing_domain = True)\n",
|
||||
"\n",
|
||||
"aks_name = 'my-aks-ssl-1' \n",
|
||||
"# Create the cluster\n",
|
||||
"aks_target = ComputeTarget.create(workspace = ws, \n",
|
||||
" name = aks_name, \n",
|
||||
" provisioning_configuration = provisioning_config)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_target.wait_for_completion(show_output = True)\n",
|
||||
"print(aks_target.provisioning_state)\n",
|
||||
"print(aks_target.provisioning_errors)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploy web service to AKS"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-deploy-to-aks"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()\n",
|
||||
"\n",
|
||||
"aks_service_name ='aks-service-ssl-1'\n",
|
||||
"\n",
|
||||
"aks_service = Model.deploy(workspace=ws,\n",
|
||||
" name=aks_service_name,\n",
|
||||
" models=[model],\n",
|
||||
" inference_config=inf_config,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=aks_target,\n",
|
||||
" overwrite=True)\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output = True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Test the web service using run method\n",
|
||||
"We test the web sevice by passing data.\n",
|
||||
"Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"standard_sample_input = json.dumps({'param': {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }})\n",
|
||||
"\n",
|
||||
"aks_service.run(input_data=standard_sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Clean up\n",
|
||||
"Delete the service, image and model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service.delete()\n",
|
||||
"model.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "vaidyas"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: production-deploy-to-aks-ssl
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- matplotlib
|
||||
- tqdm
|
||||
- scipy
|
||||
- scikit-learn
|
||||
@@ -1,625 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
|
||||
"This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
|
||||
"We then test and delete the service, image and model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
||||
"from azureml.core.webservice import Webservice, AksWebservice\n",
|
||||
"from azureml.core.model import Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"print(azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Get workspace\n",
|
||||
"Load existing workspace from the config file info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Register the model\n",
|
||||
"Register an existing trained model, add descirption and tags."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Register the model\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
|
||||
" model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
|
||||
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
|
||||
" description = \"Ridge regression model to predict diabetes\",\n",
|
||||
" workspace = ws)\n",
|
||||
"\n",
|
||||
"print(model.name, model.description, model.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create the Environment\n",
|
||||
"Create an environment that the model will be deployed with"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies \n",
|
||||
"\n",
|
||||
"conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.22.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
|
||||
"myenv = Environment(name='myenv')\n",
|
||||
"myenv.python.conda_dependencies = conda_deps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Use a custom Docker image\n",
|
||||
"\n",
|
||||
"You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
|
||||
"\n",
|
||||
"Only supported with `python` runtime.\n",
|
||||
"```python\n",
|
||||
"# use an image available in public Container Registry without authentication\n",
|
||||
"myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
|
||||
"\n",
|
||||
"# or, use an image available in a private Container Registry\n",
|
||||
"myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
|
||||
"myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
|
||||
"myenv.docker.base_image_registry.username = \"username\"\n",
|
||||
"myenv.docker.base_image_registry.password = \"password\"\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Write the Entry Script\n",
|
||||
"Write the script that will be used to predict on your model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile score.py\n",
|
||||
"import os\n",
|
||||
"import pickle\n",
|
||||
"import json\n",
|
||||
"import numpy\n",
|
||||
"import joblib\n",
|
||||
"from sklearn.linear_model import Ridge\n",
|
||||
"\n",
|
||||
"def init():\n",
|
||||
" global model\n",
|
||||
" # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
|
||||
" # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
|
||||
" # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
|
||||
" model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
|
||||
" # deserialize the model file back into a sklearn model\n",
|
||||
" model = joblib.load(model_path)\n",
|
||||
"\n",
|
||||
"# note you can pass in multiple rows for scoring\n",
|
||||
"def run(raw_data):\n",
|
||||
" try:\n",
|
||||
" data = json.loads(raw_data)['data']\n",
|
||||
" data = numpy.array(data)\n",
|
||||
" result = model.predict(data)\n",
|
||||
" # you can return any data type as long as it is JSON-serializable\n",
|
||||
" return result.tolist()\n",
|
||||
" except Exception as e:\n",
|
||||
" error = str(e)\n",
|
||||
" return error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create the InferenceConfig\n",
|
||||
"Create the inference config that will be used when deploying the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import InferenceConfig\n",
|
||||
"\n",
|
||||
"inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Model Profiling\n",
|
||||
"\n",
|
||||
"Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
|
||||
"\n",
|
||||
"In order to profile your model you will need:\n",
|
||||
"- a registered model\n",
|
||||
"- an entry script\n",
|
||||
"- an inference configuration\n",
|
||||
"- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
|
||||
"\n",
|
||||
"Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n",
|
||||
"\n",
|
||||
"At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
|
||||
"\n",
|
||||
"Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n",
|
||||
"You can try get the dataset first to see if it's already registered."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"from azureml.core import Datastore\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.data import dataset_type_definitions\n",
|
||||
"\n",
|
||||
"dataset_name='sample_request_data'\n",
|
||||
"\n",
|
||||
"dataset_registered = False\n",
|
||||
"try:\n",
|
||||
" sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n",
|
||||
" dataset_registered = True\n",
|
||||
"except:\n",
|
||||
" print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n",
|
||||
"\n",
|
||||
"if not dataset_registered:\n",
|
||||
" input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
|
||||
" [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\n",
|
||||
" # create a string that can be put in the body of the request\n",
|
||||
" serialized_input_json = json.dumps(input_json)\n",
|
||||
" dataset_content = []\n",
|
||||
" for i in range(100):\n",
|
||||
" dataset_content.append(serialized_input_json)\n",
|
||||
" sample_request_data = '\\n'.join(dataset_content)\n",
|
||||
" file_name = \"{}.txt\".format(dataset_name)\n",
|
||||
" f = open(file_name, 'w')\n",
|
||||
" f.write(sample_request_data)\n",
|
||||
" f.close()\n",
|
||||
"\n",
|
||||
" # upload the txt file created above to the Datastore and create a dataset from it\n",
|
||||
" data_store = Datastore.get_default(ws)\n",
|
||||
" data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n",
|
||||
" datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n",
|
||||
" sample_request_data = Dataset.Tabular.from_delimited_files(\n",
|
||||
" datastore_path,\n",
|
||||
" separator='\\n',\n",
|
||||
" infer_column_types=True,\n",
|
||||
" header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
|
||||
" sample_request_data = sample_request_data.register(workspace=ws,\n",
|
||||
" name=dataset_name,\n",
|
||||
" create_new_version=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime\n",
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"from azureml.core.model import Model, InferenceConfig\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"environment = Environment('my-sklearn-environment')\n",
|
||||
"environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n",
|
||||
" 'pip==20.2.4'],\n",
|
||||
" pip_packages=[\n",
|
||||
" 'azureml-defaults',\n",
|
||||
" 'inference-schema[numpy-support]',\n",
|
||||
" 'joblib',\n",
|
||||
" 'numpy',\n",
|
||||
" 'scikit-learn==0.22.1',\n",
|
||||
" 'scipy'\n",
|
||||
"])\n",
|
||||
"inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
|
||||
"# if cpu and memory_in_gb parameters are not provided\n",
|
||||
"# the model will be profiled on default configuration of\n",
|
||||
"# 3.5CPU and 15GB memory\n",
|
||||
"profile = Model.profile(ws,\n",
|
||||
" 'sklearn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
|
||||
" [model],\n",
|
||||
" inference_config,\n",
|
||||
" input_dataset=sample_request_data,\n",
|
||||
" cpu=1.0,\n",
|
||||
" memory_in_gb=0.5)\n",
|
||||
"\n",
|
||||
"# profiling is a long running operation and may take up to 25 min\n",
|
||||
"profile.wait_for_completion(True)\n",
|
||||
"details = profile.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Provision the AKS Cluster\n",
|
||||
"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
|
||||
"\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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your AKS cluster\n",
|
||||
"aks_name = 'my-aks-9' \n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" # Use the default configuration (can also provide parameters to customize)\n",
|
||||
" prov_config = AksCompute.provisioning_configuration()\n",
|
||||
"\n",
|
||||
" # Create the cluster\n",
|
||||
" aks_target = ComputeTarget.create(workspace = ws, \n",
|
||||
" name = aks_name, \n",
|
||||
" provisioning_configuration = prov_config)\n",
|
||||
"\n",
|
||||
"if aks_target.get_status() != \"Succeeded\":\n",
|
||||
" aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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/how-to-network-security-overview) for more details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# from azureml.core.compute import ComputeTarget, AksCompute\n",
|
||||
"\n",
|
||||
"# # Create the compute configuration and set virtual network information\n",
|
||||
"# config = AksCompute.provisioning_configuration(location=\"eastus2\")\n",
|
||||
"# config.vnet_resourcegroup_name = \"mygroup\"\n",
|
||||
"# config.vnet_name = \"mynetwork\"\n",
|
||||
"# config.subnet_name = \"default\"\n",
|
||||
"# config.service_cidr = \"10.0.0.0/16\"\n",
|
||||
"# config.dns_service_ip = \"10.0.0.10\"\n",
|
||||
"# config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
|
||||
"\n",
|
||||
"# # Create the compute target\n",
|
||||
"# aks_target = ComputeTarget.create(workspace = ws,\n",
|
||||
"# name = \"myaks\",\n",
|
||||
"# provisioning_configuration = config)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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/how-to-network-security-overview#secure-the-inferencing-environment-v1) for more details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_target.wait_for_completion(show_output = True)\n",
|
||||
"print(aks_target.provisioning_state)\n",
|
||||
"print(aks_target.provisioning_errors)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Optional step: Attach existing AKS cluster\n",
|
||||
"\n",
|
||||
"If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# # Use the default configuration (can also provide parameters to customize)\n",
|
||||
"# resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
|
||||
"\n",
|
||||
"# create_name='my-existing-aks' \n",
|
||||
"# # Create the cluster\n",
|
||||
"# attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
|
||||
"# aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
|
||||
"# # Wait for the operation to complete\n",
|
||||
"# aks_target.wait_for_completion(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Deploy web service to AKS"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-deploy-to-aks"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set the web service configuration (using default here)\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()\n",
|
||||
"\n",
|
||||
"# # Enable token auth and disable (key) auth on the webservice\n",
|
||||
"# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"sample-deploy-to-aks"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service_name ='aks-service-1'\n",
|
||||
"\n",
|
||||
"aks_service = Model.deploy(workspace=ws,\n",
|
||||
" name=aks_service_name,\n",
|
||||
" models=[model],\n",
|
||||
" inference_config=inf_config,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=aks_target)\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output = True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Test the web service using run method\n",
|
||||
"We test the web sevice by passing data.\n",
|
||||
"Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"test_sample = json.dumps({'data': [\n",
|
||||
" [1,2,3,4,5,6,7,8,9,10], \n",
|
||||
" [10,9,8,7,6,5,4,3,2,1]\n",
|
||||
"]})\n",
|
||||
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
|
||||
"\n",
|
||||
"prediction = aks_service.run(input_data = test_sample)\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Test the web service using raw HTTP request (optional)\n",
|
||||
"Alternatively you can construct a raw HTTP request and send it to the service. In this case you need to explicitly pass the HTTP header. This process is shown in the next 2 cells."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# # if (key) auth is enabled, retrieve the API keys. AML generates two keys.\n",
|
||||
"# key1, Key2 = aks_service.get_keys()\n",
|
||||
"# print(key1)\n",
|
||||
"\n",
|
||||
"# # if token auth is enabled, retrieve the token.\n",
|
||||
"# access_token, refresh_after = aks_service.get_token()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# construct raw HTTP request and send to the service\n",
|
||||
"# %%time\n",
|
||||
"\n",
|
||||
"# import requests\n",
|
||||
"\n",
|
||||
"# import json\n",
|
||||
"\n",
|
||||
"# test_sample = json.dumps({'data': [\n",
|
||||
"# [1,2,3,4,5,6,7,8,9,10], \n",
|
||||
"# [10,9,8,7,6,5,4,3,2,1]\n",
|
||||
"# ]})\n",
|
||||
"# test_sample = bytes(test_sample,encoding = 'utf8')\n",
|
||||
"\n",
|
||||
"# # If (key) auth is enabled, don't forget to add key to the HTTP header.\n",
|
||||
"# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
|
||||
"\n",
|
||||
"# # If token auth is enabled, don't forget to add token to the HTTP header.\n",
|
||||
"# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
|
||||
"\n",
|
||||
"# resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# print(\"prediction:\", resp.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Clean up\n",
|
||||
"Delete the service, image and model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service.delete()\n",
|
||||
"model.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "vaidyas"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8 - AzureML",
|
||||
"language": "python",
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: production-deploy-to-aks
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- matplotlib
|
||||
- tqdm
|
||||
- scipy
|
||||
- scikit-learn
|
||||
Binary file not shown.
@@ -1,17 +0,0 @@
|
||||
name: explain-model-on-amlcompute
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.33.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
@@ -1,18 +0,0 @@
|
||||
name: save-retrieve-explanations-run-history
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.33.0
|
||||
- packaging>=20.9
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
@@ -370,7 +370,7 @@
|
||||
"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
|
||||
"myenv = CondaDependencies.create(\n",
|
||||
" python_version=python_version,\n",
|
||||
" conda_packages=['pip==20.2.4', numpy_dep],\n",
|
||||
" conda_packages=['pip==22.3.1', numpy_dep],\n",
|
||||
" pip_packages=['pyyaml', sklearn_dep, pandas_dep, numba_dep] + azureml_pip_packages)\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
@@ -404,7 +404,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
|
||||
" memory_gb=2, \n",
|
||||
" memory_gb=4, \n",
|
||||
" tags={\"data\": \"IBM_Attrition\", \n",
|
||||
" \"method\" : \"local_explanation\"}, \n",
|
||||
" description='Get local explanations for IBM Employee Attrition data')\n",
|
||||
|
||||
@@ -1,18 +0,0 @@
|
||||
name: train-explain-model-locally-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- ipython
|
||||
- matplotlib
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.33.0
|
||||
- packaging>=20.9
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
@@ -1,18 +0,0 @@
|
||||
name: train-explain-model-on-amlcompute-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- flask
|
||||
- flask-cors
|
||||
- gevent>=1.3.6
|
||||
- ipython
|
||||
- matplotlib
|
||||
- azureml-core
|
||||
- ipywidgets
|
||||
- raiwidgets~=0.33.0
|
||||
- itsdangerous==2.0.1
|
||||
- markupsafe<2.1.0
|
||||
- scipy>=1.5.3
|
||||
- protobuf==3.20.0
|
||||
- jinja2==3.0.3
|
||||
@@ -33,7 +33,6 @@
|
||||
"| Data store | Supported as a source | Supported as a sink |\n",
|
||||
"| --- | --- | --- |\n",
|
||||
"| Azure Blob Storage | Yes | Yes |\n",
|
||||
"| Azure Data Lake Storage Gen 1 | Yes | Yes |\n",
|
||||
"| Azure Data Lake Storage Gen 2 | Yes | Yes |\n",
|
||||
"| Azure SQL Database | Yes | Yes |\n",
|
||||
"| Azure Database for PostgreSQL | Yes | Yes |\n",
|
||||
@@ -151,53 +150,6 @@
|
||||
" path_on_datastore=\"testdata\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Azure Data Lake Storage Gen1\n",
|
||||
"\n",
|
||||
"Please consult the following articles for detailed steps on setting up service principal authentication and assigning correct permissions to Data Lake Storage account:\n",
|
||||
"\n",
|
||||
"https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory\n",
|
||||
"https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-store#use-service-principal-authentication"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"datastore_name='MyAdlsDatastore'\n",
|
||||
"subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\", \"<my-subscription-id>\") # subscription id of ADLS account\n",
|
||||
"resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\", \"<my-resource-group>\") # resource group of ADLS account\n",
|
||||
"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",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" adls_datastore = Datastore.get(ws, datastore_name)\n",
|
||||
" print(\"Found datastore with name: %s\" % datastore_name)\n",
|
||||
"except UserErrorException:\n",
|
||||
" adls_datastore = Datastore.register_azure_data_lake(\n",
|
||||
" workspace=ws,\n",
|
||||
" datastore_name=datastore_name,\n",
|
||||
" subscription_id=subscription_id, # subscription id of ADLS account\n",
|
||||
" resource_group=resource_group, # resource group of ADLS account\n",
|
||||
" 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",
|
||||
" print(\"Registered datastore with name: %s\" % datastore_name)\n",
|
||||
"\n",
|
||||
"adls_data_ref = DataReference(\n",
|
||||
" datastore=adls_datastore,\n",
|
||||
" data_reference_name=\"adls_test_data\",\n",
|
||||
" path_on_datastore=\"testdata\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -440,10 +392,16 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# TODO: 3012801 - Use ADLS Gen2 datastore.\n",
|
||||
"blob_data_ref2 = DataReference(\n",
|
||||
" datastore=blob_datastore,\n",
|
||||
" data_reference_name=\"blob_test_data2\",\n",
|
||||
" path_on_datastore=\"testdata2\")\n",
|
||||
"\n",
|
||||
"transfer_adls_to_blob = DataTransferStep(\n",
|
||||
" name=\"transfer_adls_to_blob\",\n",
|
||||
" source_data_reference=adls_data_ref,\n",
|
||||
" destination_data_reference=blob_data_ref,\n",
|
||||
" source_data_reference=blob_data_ref,\n",
|
||||
" destination_data_reference=blob_data_ref2,\n",
|
||||
" compute_target=data_factory_compute)\n",
|
||||
"\n",
|
||||
"print(\"Data transfer step created\")"
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-data-transfer
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,6 +0,0 @@
|
||||
name: aml-pipelines-getting-started
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- protobuf==3.20.0
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-how-to-use-modulestep
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-how-to-use-pipeline-drafts
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -292,7 +292,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tf_env = Environment.get(ws, name='AzureML-tensorflow-2.6-ubuntu20.04-py38-cuda11-gpu')"
|
||||
"tf_env = Environment.get(ws, name='AzureML-tensorflow-2.12-cuda11')"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,9 +0,0 @@
|
||||
name: aml-pipelines-parameter-tuning-with-hyperdrive
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- numpy
|
||||
- pandas_ml
|
||||
- azureml-dataset-runtime[pandas,fuse]
|
||||
@@ -1,6 +0,0 @@
|
||||
name: aml-pipelines-publish-and-run-using-rest-endpoint
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- requests
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-setup-schedule-for-a-published-pipeline
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,6 +0,0 @@
|
||||
name: aml-pipelines-setup-versioned-pipeline-endpoints
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- requests
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-showcasing-datapath-and-pipelineparameter
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-showcasing-dataset-and-pipelineparameter
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,4 +0,0 @@
|
||||
name: aml-pipelines-with-automated-machine-learning-step
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-with-commandstep-r
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-with-commandstep
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,5 +0,0 @@
|
||||
name: aml-pipelines-with-data-dependency-steps
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,6 +0,0 @@
|
||||
name: aml-pipelines-with-notebook-runner-step
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- azureml-contrib-notebook
|
||||
@@ -1,7 +1,7 @@
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.7
|
||||
- python=3.8
|
||||
- pip:
|
||||
- azureml-defaults
|
||||
- tensorflow-gpu==2.3.0
|
||||
|
||||
@@ -1,11 +0,0 @@
|
||||
name: nyc-taxi-data-regression-model-building
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- certifi
|
||||
- azureml-widgets
|
||||
- azureml-opendatasets
|
||||
- azureml-train-automl
|
||||
- matplotlib
|
||||
- pandas
|
||||
- pyarrow
|
||||
@@ -1,7 +0,0 @@
|
||||
name: file-dataset-image-inference-mnist
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
@@ -1,7 +0,0 @@
|
||||
name: file-dataset-partition-per-folder
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
@@ -1,7 +0,0 @@
|
||||
name: tabular-dataset-inference-iris
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
@@ -1,7 +0,0 @@
|
||||
name: tabular-dataset-partition-per-column
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
@@ -1,7 +0,0 @@
|
||||
name: pipeline-style-transfer-parallel-run
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- requests
|
||||
@@ -1,5 +0,0 @@
|
||||
name: fastai-with-custom-docker
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- fastai==1.0.61
|
||||
@@ -1,6 +0,0 @@
|
||||
name: train-hyperparameter-tune-deploy-with-keras
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
@@ -1,5 +0,0 @@
|
||||
name: distributed-pytorch-with-distributeddataparallel
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -1,5 +0,0 @@
|
||||
name: distributed-pytorch-with-horovod
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -273,7 +273,7 @@
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"\n",
|
||||
"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.11-cuda11.3')"
|
||||
"pytorch_env = Environment.get(ws, name='azureml-acpt-pytorch-1.13-cuda11.7')"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
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Reference in New Issue
Block a user