jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
mirror of https://github.com/Azure/MachineLearningNotebooks.git synced 2025-12-19 17:17:04 -05:00
Code Issues Projects Releases Wiki Activity
Files
3228bbfc63d0c7652c06c9cc240b77117eb9d5a7
MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb

495 lines
17 KiB
Plaintext
Raw Blame History

{
"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": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.png)"
]
},
{
"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
}
Reference in New Issue View Git Blame Copy Permalink