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update samples - test

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vizhur
2019-11-01 14:48:01 +00:00
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how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
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@@ -20,12 +20,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register Model and deploy as Webservice\n",
"# Register model and deploy as webservice\n",
"\n",
"This example shows how to deploy a Webservice in step-by-step fashion:\n",
"Following this notebook, you will:\n",
"\n",
" 1. Register Model\n",
" 2. Deploy Model as Webservice"
" - Learn how to register a model in your Azure Machine Learning Workspace.\n",
" - Deploy your model as a web service in an Azure Container Instance."
]
},
{
@@ -33,7 +33,8 @@
"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."
"\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."
]
},
{
@@ -42,19 +43,19 @@
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
"# Check core SDK version number.\n",
"print('SDK version:', azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"## Initialize workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
"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."
]
},
{
@@ -77,16 +78,37 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Register Model"
"## Register input and output datasets\n",
"\n",
"For this example, we have provided a small model (`sklearn_regression_model.pkl` in the notebook's directory) that was trained on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). Here, you will register the data used to create this model in your workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Dataset\n",
"\n",
"datastore = ws.get_default_datastore()\n",
"datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
" target_path='sklearn_regression/',\n",
" overwrite=True)\n",
"\n",
"input_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/features.csv')])\n",
"output_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/labels.csv')])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can add tags and descriptions to your Models. Note you need to have a `sklearn_regression_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a Model with the same name `sklearn_regression_model.pkl` in the workspace.\n",
"## Register model\n",
"\n",
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
"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-).\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."
]
},
{
@@ -99,29 +121,120 @@
},
"outputs": [],
"source": [
"from azureml.core.model import Model\n",
"from azureml.core import Model\n",
"from azureml.core.resource_configuration import ResourceConfiguration\n",
"\n",
"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
" model_name=\"sklearn_regression_model.pkl\",\n",
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
" description=\"Ridge regression model to predict diabetes\",\n",
" workspace=ws)"
"model = Model.register(workspace=ws,\n",
" model_name='my-sklearn-model', # Name of the registered model in your workspace.\n",
" model_path='./sklearn_regression_model.pkl', # Local file to upload and register as a model.\n",
" model_framework=Model.Framework.SCIKITLEARN, # Framework used to create the model.\n",
" model_framework_version='0.19.1', # Version of scikit-learn used to create the model.\n",
" sample_input_dataset=input_dataset,\n",
" sample_output_dataset=output_dataset,\n",
" resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n",
" description='Ridge regression model to predict diabetes progression.',\n",
" tags={'area': 'diabetes', 'type': 'regression'})\n",
"\n",
"print('Name:', model.name)\n",
"print('Version:', model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Environment"
"## 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. For more information on all your options when deploying models, see the [next steps](#Next-steps) section at the end of this notebook.\n",
"\n",
"For this example, we will deploy your scikit-learn model to an Azure Container Instance (ACI)."
]
},
{
"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. Only Environments that were created using azureml-defaults version 1.0.48 or later will work with this new handling however.\n",
"### Use a default environment (for supported models)\n",
"\n",
"More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
"The Azure Machine Learning service provides a default environment for supported model frameworks, including scikit-learn, 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 = 'my-sklearn-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 using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"input_payload = json.dumps({\n",
" 'data': [\n",
" [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n",
" -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
" ],\n",
" 'method': 'predict' # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
"})\n",
"\n",
"output = service.run(input_payload)\n",
"\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use a custom environment (for all models)\n",
"\n",
"If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method.\n",
"\n",
"Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
]
},
{
@@ -131,61 +244,42 @@
"outputs": [],
"source": [
"from azureml.core import Environment\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
"\n",
"# This is optional at this point\n",
"# env.register(workspace=ws)"
"environment = Environment('my-sklearn-environment')\n",
"environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
" 'azureml-defaults',\n",
" 'inference-schema[numpy-support]',\n",
" 'joblib',\n",
" 'numpy',\n",
" 'scikit-learn'\n",
"])"
]
},
{
"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, your entry_script, conda_file, and extra_docker_file_steps paths are relative paths to the source_directory path.\n",
"\n",
"Sample code for using a source directory:\n",
"\n",
"```python\n",
"inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
" runtime= \"python\", \n",
" entry_script=\"x/y/score.py\",\n",
" conda_file=\"env/myenv.yml\", \n",
" extra_docker_file_steps=\"helloworld.txt\")\n",
"```\n",
"\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",
" - runtime = Which runtime to use for the image. Current supported runtimes are 'spark-py' and 'python\n",
" - entry_script = contains logic specific to initializing your model and running predictions\n",
" - conda_file = manages conda and python package dependencies.\n",
" - extra_docker_file_steps = optional: any extra steps you want to inject into docker file"
"When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create image"
]
},
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"\n",
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
"with open('score.py') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy Model as Webservice on Azure Container Instance\n",
"Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [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-).\n",
"\n",
"Note that the service creation can take few minutes."
"**Note**: This step can take several minutes."
]
},
{
@@ -198,33 +292,31 @@
},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"from azureml.core import Webservice\n",
"from azureml.core.model import InferenceConfig\n",
"from azureml.exceptions import WebserviceException\n",
"\n",
"deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
"aci_service_name = 'aciservice1'\n",
"service_name = 'my-custom-env-service'\n",
"\n",
"# Remove any existing service under the same name.\n",
"try:\n",
" # if you want to get existing service below is the command\n",
" # since aci name needs to be unique in subscription deleting existing aci if any\n",
" # we use aci_service_name to create azure aci\n",
" service = Webservice(ws, name=aci_service_name)\n",
" if service:\n",
" service.delete()\n",
"except WebserviceException as e:\n",
" print()\n",
" Webservice(ws, service_name).delete()\n",
"except WebserviceException:\n",
" pass\n",
"\n",
"service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n",
"inference_config = InferenceConfig(entry_script='score.py',\n",
" source_directory='.',\n",
" environment=environment)\n",
"\n",
"service.wait_for_deployment(True)\n",
"print(service.state)"
"service = Model.deploy(ws, service_name, [model], inference_config)\n",
"service.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Test web service"
"After your model is deployed, make a call to the web service using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)."
]
},
{
@@ -234,32 +326,30 @@
"outputs": [],
"source": [
"import json\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",
"\n",
"test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
"prediction = service.run(input_data=test_sample_encoded)\n",
"print(prediction)"
"input_payload = json.dumps({\n",
" 'data': [\n",
" [ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235,\n",
" -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
" ]\n",
"})\n",
"\n",
"output = service.run(input_payload)\n",
"\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Delete ACI to clean up"
"When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"metadata": {},
"outputs": [],
"source": [
"service.delete()"
@@ -269,7 +359,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Profiling\n",
"### Model profiling\n",
"\n",
"You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
"\n",
@@ -285,7 +375,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Packaging\n",
"### Model packaging\n",
"\n",
"If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
"\n",
@@ -305,6 +395,19 @@
"package.save(\"./local_context_dir\")\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
" - To run a production-ready web service, see the [notebook on deployment to Azure Kubernetes Service](../production-deploy-to-aks/production-deploy-to-aks.ipynb).\n",
" - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n",
" - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets.ipynb).\n",
" - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n",
" - For information on all the available deployment targets, see [“How and where to deploy models”](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)."
]
}
],
"metadata": {
@@ -313,6 +416,22 @@
"name": "aashishb"
}
],
"category": "deployment",
"compute": [
"None"
],
"datasets": [
"Diabetes"
],
"deployment": [
"Azure Container Instance"
],
"exclude_from_index": false,
"framework": [
"Scikit-learn"
],
"friendly_name": "Register model and deploy as webservice",
"index_order": 3,
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
@@ -329,7 +448,14 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"star_tag": [
"featured"
],
"tags": [
"None"
],
"task": "Deploy a model with Azure Machine Learning"
},
"nbformat": 4,
"nbformat_minor": 2