How to fix "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed." while using Promt Flow.

Question

I have made a custom image to use python's version 3.10.1 on Azure Ai Foundry 's Prompt Flow ( Earlier Ai Studio). The Flow is running successfully by ignoring the error.

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.mlflow 2.13.0 requires protobuf<5,>=3.12.0, but you have protobuf 5.29.1 which is incompatible.mlflow-skinny 2.13.0 requires protobuf<5,>=3.12.0, but you have protobuf 5.29.1 which is incompatible.

But, I according to me this causing trouble in the final production deployment. And, I have also checked the version of "protobuf" on my custom image but that was 4.25.5 and that should have worked in this scenario. Below is the screen capture of the error and docker container.

The error which I am getting while doing the find deployment is as below:

The Error is ResourceNotReady as per the documentation for troubleshooting this error 'https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2&tabs=cli#error-resourcenotready'. there is a mention of score.py file. I want to know what is Score.py file, does this automatically get generated while doing deployment or there is a need to create this file separately while custom image. And ultimately how I can resolve this issue.

Answer 1

@Lovedeep Singh The first two conditions that should be satisfied are:

• the docker image must be created based on prompt flow base image, mcr.microsoft.com/azureml/promptflow/promptflow-runtime-stable:<newest_version>. You can find the newest version here.
• the environment definition must include the inference_config.

If your image is already created on top of base image, then the current flow.dag.yaml file should point to the correct location of this image.

The second requirement of inference_config this should be added after the image field in your current yaml.

inference_config:
  liveness_route:
    port: 8080
    path: /health
  readiness_route:
    port: 8080
    path: /health
  scoring_route:
    port: 8080
    path: /score

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