Lab 3 - Deploy your model

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.
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 environment file
myenv = CondaDependencies.create(pip_packages=['azureml-defaults ', 'torch', 'torchvision','pillow==5.4.1'])
​
with open("inference/myenv.yml","w") as f:
    f.write(myenv.serialize_to_string())
​
myenv = Environment.from_conda_specification(name="myenv", file_path="inference/myenv.yml")
Create an Inference config
inference_config = InferenceConfig(entry_script="inference/score.py", environment=myenv)
Create a 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​
Scoring URL
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
Get the scoring uri
print("Scoring endpoint:",aci_service.scoring_uri)
Create a new request in Postman
Send a raw body with the JSON below
{ "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 2 - Train your model
Last updated 5 months ago