Hi ExuberantParrot61 , that's a good question. This is a bit hacky but what if you try to catch the task with Task.current_task() from inside the step and try to change the output_uri attribute there?
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Hi, Question About Setting Output_Uri=True For A Pipeline Step Using Decorators. What I’M Seeing Is That Training Results In No Output Destination Specified, And Logs To Local Disk (When Run Remotely In Docker Container), Where Is The Proper Place To Set
Hi, question about setting output_uri=True for a pipeline step using Decorators. What I’m seeing is that training results in no output destination specified, and logs to local disk (when run remotely in docker container), Where is the proper place to set output_uri=True at the pipeline step or script level? I’m able to train and have it log to ClearML properly in other scripts too. See below for current pipeline script. Here was the info message too:2022-08-26 04:43:01,784 - clearml.model - INFO - No output storage destination defined, registering local model
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