Internal Server Error when starting compute session in Prompt Flow in classic Foundry UI

Question

I’m encountering an issue in Azure AI Foundry/Prompt Flow where the starting compute session operation fails with an internal server error, with no additional information.

Error message:

Session operation failed: InternalServerError
Trace ID: fc73b2e1-5789-4084-83c8-67f07e5e1e30

This issue occurs when attempting to operate the Prompt Flow compute session.

Anyway to debug this?

Answer 1

I think the simplest way to debug this is to reset the compute state and let Foundry re-check the environment. Stopping and starting the compute, then opening a fresh compute session, usually makes the real error appear, and checking the workspace logs is the next reliable place to see details.

Answer 2

For an internal server error when starting a Prompt Flow compute session in the classic Foundry UI, the available guidance focuses on checking the compute session environment and image build, because missing dependencies or image build failures commonly surface as opaque runtime errors.

Use the following checks and debugging steps:

1. Verify required Python packages and base image
   • If using a default environment, ensure the compute session image is on the latest version. An outdated image can miss required packages and cause failures when the session starts.
   • If using a custom base image, confirm that all required packages are installed in the Docker context. A missing module often appears as No module named XXX once logs are available, but can also manifest as generic failures at session start.
   • For Prompt Flow in Azure Machine Learning, compute sessions support requirements.txt or a custom base image specified in flow.dag.yaml. The recommended approach is:
     • Prefer requirements.txt for typical Python dependencies; the platform runs pip install -r requirements.txt.
     • Only build a custom image if non-Python dependencies are needed, following the “Customize base image” guidance.
     • Do not pin promptflow or promptflow-tools versions in requirements.txt, because they are already included in the base image and pinning older versions can cause unexpected behavior.
2. Ensure a supported Prompt Flow base image is used
   • Compute sessions cannot use arbitrary base images. The base image must be one of the Prompt Flow–provided images; otherwise, the session can fail to start with low-information errors.
   • If a custom image is required, build it on top of a Prompt Flow base image and reference it in flow.dag.yaml as documented in the compute-session customization guidance.
3. Check for environment image build failures
   • Internal server errors during session start or deployment can be caused by image build failures in the underlying Azure ML environment.
   • For deployments, an ImageBuildFailure or timeout while waiting for the environment image is a common symptom. In those cases, the build log in the workspace storage account contains the real error.
   • For a compute session that fails to start, inspect the corresponding environment/image build logs in the Azure ML workspace or storage account (under the environment’s container path) to see if dependency installation or Docker build failed.
4. Confirm serverless instance and compute session configuration
   • In the Foundry/Prompt Flow experience, the serverless instance used by a compute session can be viewed on the compute session list tab under the Compute page. Confirm that:
     • The session is bound to a valid serverless instance.
     • The instance is healthy and not in a failed or deallocated state.
5. If using a custom base image in Foundry classic
   • When flows are deployed or run with a custom base image, misconfiguration can cause backend errors:
     • Ensure the container image URI from the custom environment detail page is correctly set as the flow base image in flow.dag.yaml.
     • When deploying from the UI, select Use environment of current flow definition so the backend builds the environment from that base image plus requirements.txt.
     • Alternatively, ensure inference_config is correctly added in the custom environment definition if that pattern is used.
6. Re-run after updating runtime or environment
   • If the flow or compute session is tied to an older runtime, upgrade the compute instance/runtime as per the “Upgrade compute instance runtime” guidance, re-run the flow in the latest runtime, and then start the compute session again.
7. Use the trace ID with Azure support
   • The Trace ID in the error (fc73b2e1-5789-4084-83c8-67f07e5e1e30) is primarily for backend diagnostics. After verifying the above configuration and environment points, open a support ticket and provide this trace ID so the service team can correlate it with internal logs.

These steps focus on surfacing the underlying cause (image build failure, missing packages, unsupported base image, or outdated runtime) that is currently hidden behind the generic InternalServerError.

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References:

• Troubleshoot guidance for prompt flow (classic)
• Troubleshoot guidance
• How to troubleshoot your deployments and monitors in Microsoft Foundry portal (classic)