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How to debug your failed pipeline job by comparing it to a completed oneĭuring iterative model development, you may have a baseline pipeline, and then do some modifications such as changing a parameter, dataset or compute resource, etc.

In Managed preview feature panel, toggle on Compare pipeline jobs to debug failures or unexpected issues feature.

Select Manage preview features (megaphone icon) among the icons on the top right side of the screen.

Navigate to Azure Machine Learning studio UI.

Debug your failed node in a pipeline by comparing it to a similar completed one.

Debug your failed pipeline job by comparing it to a completed one.

Two major scenarios where you can use pipeline comparison to help with debugging: For example you can compare a successful pipeline and a failed pipeline, which helps you find what modifications make your pipeline fail. Pipeline comparison identifies the differences (including topology, component properties, and job properties) between multiple jobs.

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If you don't see those folders, this is due to the compute run time update isn't released to the compute cluster yet, and you can look at 70_driver_log.txt under azureml-logs folder first.Ĭompare different pipelines to debug failure or other unexpected issues (preview) Learn more about View and download diagnostic logs. The system_logs folder contains logs generated by Azure Machine Learning. The std_log.txt is where your code's logs (for example, print statements) show up. This folder is open by default, and the std_log.txt log is selected. The user_logs folder contains information about user code generated logs. Select Outputs+logs tab and you can explore all the outputs and logs of this node. You can select the specific node and open the right pane. If your pipeline fails or gets stuck on a node, first view the logs.

The left pane shows the matched nodes with more information including status, duration, and created time. You can filter failed or completed nodes, and filter by only components or dataset for further search. Hovering on any row, you can select the 'Locate' button to locate that node in the canvas. In pipeline job detail page, there's an outline left to the canvas, which shows the overall structure of your pipeline job. Certain features might not be supported or might have constrained capabilities.įor more information, see Supplemental Terms of Use for Microsoft Azure Previews. The preview version is provided without a service level agreement, and it's not recommended for production workloads. Items marked (preview) in this article are currently in public preview.