How to fix real-time endpoint deloyment error for Azure ML Designer Inference Pipeline

Question

I have created an Azure ML Inference Pipeline using the classical pre-built components from Azure ML Designer, but didn't succeed with the deployment of an online endpoint. The error is: "Failed building the environment" both for Azure Container Instances (ACI) and AKS. Outside of the ML Workspace, I have no problem to create an ACI. I have followed exactly the instructions described here: https://microsoftlearning.github.io/AI-900-AIFundamentals/instructions/02b-create-classification-model.html#deploy-a-service Any ideas how to solve this problem?

Answer 1

Hello Annette Ryser,

Welcome to the Microsoft Q&A and thank you for posting your questions here.

I understand that you would like to know how to fix real-time endpoint deployment error for Azure ML Designer Inference Pipeline.

Regarding your scenarios, I’m sorry to hear you’re having trouble with deploying your Azure ML Inference Pipeline. The error “Failed building the environment” can be frustrating. These are a few steps you can take to troubleshoot and resolve this issue:

• Ensure that the environment definition in your pipeline is correctly specified. Verify that all dependencies and versions are correctly listed in your environment YAML file.
• Check the detailed logs for the deployment process. These logs can provide more specific information about what might be causing the environment build to fail.
• Ensure that your Azure subscription has sufficient quotas for the resources you are trying to deploy. Sometimes, deployments fail due to quota limits on resources like CPU, memory, or the number of instances.
• Verify that your Azure ML Workspace has the necessary network configurations to communicate with ACI and AKS. This includes checking virtual network (VNet) settings, firewall rules, and any network security groups (NSGs) that might be blocking traffic.
• Make sure that any required environment variables are correctly set in your deployment configuration. Missing or incorrect environment variables can cause the environment build to fail.
• Check that the versions of the Azure ML SDK, Docker, and other tools you are using are compatible with each other. Sometimes, version mismatches can lead to deployment failures.
• Sometimes, transient issues can cause deployment failures. Try redeploying the endpoint to see if the issue persists.

This a basic example of how you might define an environment in your YAML file:

name: my-environment
dependencies:
  - python=3.8
  - pip:
    - azureml-defaults
    - scikit-learn
    - pandas

And in your pipeline script, you would reference this environment:

from azure.ai.ml import MLClient
from azure.ai.ml.entities import Environment
ml_client = MLClient.from_config()
env = Environment(name="my-environment", image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04")
# Use this environment in your pipeline

I hope this is helpful! Do not hesitate to let me know if you have any other questions.

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