Send a scoring request to the server using curl: curl -p 127.0.0.1:5001/score Start the server and set score.py as the entry script: azmlinfsrv -entry_script score.py
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Install the azureml-inference-server-http package from the pypi feed: python -m pip install azureml-inference-server-http Learn more about Azure Machine Learning inference HTTP Server Instead, it will throw an exception & the location where the issues occurred. In case the underlying score script has a bug, the server will fail to initialize or serve the model. The local inference server allows you to quickly debug your entry script ( score.py). Azure Machine learning inference HTTP server To troubleshoot a deployment locally, see the local troubleshooting article. Using a local web service makes it easier to troubleshoot problems. If you have problems when deploying a model to ACI or AKS, deploy it as a local web service. # Choose the webservice you are interested in To get the logs from a deployed webservice, do: az ml service get-logs -verbose -workspace-name -name Īssuming you have an object of type called ws, you can do the following: print(ws.webservices) First, follow the instructions here to connect to your workspace. The first step in debugging errors is to get your deployment logs. Understanding these high-level steps should help you understand where errors are happening. The main difference when using a local deployment is that the container image is built on your local machine, which is why you need to have Docker installed for a local deployment. When your deployed model receives a request, your run() function handles that request.Your web server is initialized by running your entry script's init() function.Your workspace's default Blob store is mounted to your compute target, giving you access to registered models.The Docker image from your container registry is downloaded to your compute target.If a previously built image is not available in your container registry, a new Docker image is built in the cloud and stored in your workspace's default container registry.The Dockerfile you specified in your Environments object in your InferenceConfig is sent to the cloud, along with the contents of your source directory.When you deploy a model to non-local compute in Azure Machine Learning, the following things happen: Steps for Docker deployment of machine learning models The CLI extension for Azure Machine Learning.
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Try the free or paid version of Azure Machine Learning. If you need help troubleshooting AKS cluster problems please contact AKS Support. If you are trying to deploy a model to an unhealthy or overloaded cluster, it is expected to experience issues.
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