Lab 3 - Deploy your model - Developers Guide to Azure AI

In this lab we are going to deploy the model wrappend in an API in an Azure Container Instance and sending data to it with postman.
1. Deploy to an Azure Container Instance
Download the scoring script
inference_folder = "./inference"
​
inference_script_url = "https://raw.githubusercontent.com/hnky/DevelopersGuideToAI/master/amls/resources/score.py"
inference_script_download_path = os.path.join(inference_folder,"score.py")
if not os.path.exists(inference_folder):
    os.mkdir(inference_folder);
urllib.request.urlretrieve(inference_script_url, filename=inference_script_download_path)
Create an inference environment
inference_env = Environment(name="simpsons-inference")
​
conda_dep = CondaDependencies()
conda_dep.add_pip_package("azureml-defaults")
conda_dep.add_pip_package("torch")
conda_dep.add_pip_package("torchvision")
conda_dep.add_pip_package("pillow==5.4.1")
​
inference_env.python.conda_dependencies=conda_dep
Create an Inference config
inference_config = InferenceConfig(
    entry_script="inference/score.py", 
    environment=inference_env 
)
Create a Azure Container Instance deployment config
deploy_config = AciWebservice.deploy_configuration(
    cpu_cores = model.resource_configuration.cpu, 
    memory_gb = model.resource_configuration.memory_in_gb,
    description='Simpson Lego Classifier'
)
Deploy the model to an ACI
aci_service = Model.deploy(ws, 
                name="simpsons-pt-aci", 
                models = [model], 
                inference_config = inference_config, 
                deployment_config = deploy_config, 
                overwrite = True)
​
aci_service.wait_for_deployment(show_output=True)
print("Scoring endpoint:",aci_service.scoring_uri)
This step can take up to 10 minutes
You can find the deployment location from your model back under the model: https://ml.azure.com 
Note: if you don't see it immediately refresh the tab
Scoring URL
2. Test the model in the API
Post an image to the endpoint
The easiest way to test your scoring endpoint is the code below.
image_uri = "https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Bart.jpg"
result = aci_service.run(input_data=json.dumps({ "url": image_uri}))
print(result)
Use Postman
Download Postman to your local machine​
Get the scoring uri
print("Scoring endpoint:",aci_service.scoring_uri)
Create a new request in Postman
POST request
Put scoring URL in call
Send a raw body with the JSON below. Make sure JSON is selected in orange next to the raw radio button
{ "url": "https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Bart.jpg"}
Scoring URL
Try other images
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Krusty.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Bart.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Flanders.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Homer.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Lisa.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/marge.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Milhouse.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/MrBurns.jpg
https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Wiggum.jpg
End

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Lab 1 - Computer Vision

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On this page

1. Deploy to an Azure Container Instance

Download the scoring script

Create an inference environment

Create an Inference config

Create a Azure Container Instance deployment config

Deploy the model to an ACI

2. Test the model in the API

Post an image to the endpoint

Use Postman

Try other images