CooperativeSealion8 For future reference, notice there's a configuration reference available at https://allegro.ai/docs/references/trains_ref/
GentleSwallow91 For more information, look at what ClearML logs for your experiments: https://docs-testing.allegro.ai/docs/latest/docs/fundamentals/task#logging-task-information
GreasyPenguin14 That's an annoying bug indeed - Thanks for spotting it. If you need to circumvent it before a fix comes out in one of the near releases, you can programatically use the https://clear.ml/docs/latest/docs/references/api/endpoints#post-projectsupdate e.g.from clearml.backend_api.session.client import APIClient client = APIClient() client.projects.update(project='<project ID>', description='My new description')
Note you can get your project's ID either from the webapp URL...
DepressedChimpanzee34 Experience has shown that some mechanisms for mitigating large sets impact on browser performance are required.
Your 2nd suggestion for adding an in-app search tool for such sections seems to be completely in line with ClearML's behaviour in other UI sections (e.g. console logs) - It'd be great if you can https://github.com/allegroai/clearml/issues/new/choose
AverageRabbit65 Adding to SweetBadger76 's reference, e2e examples are available for the different pipeline implementation methods:
https://clear.ml/docs/latest/docs/guides/pipeline/pipeline_controller
https://clear.ml/docs/latest/docs/guides/pipeline/pipeline_decorator
https://clear.ml/docs/latest/docs/guides/pipeline/pipeline_functions
Take a look at https://clear.ml/docs/latest/docs/pipelines/pipelines_sdk_tasks#running-the-pipeline ;
By default pipelines are enqueued for execution by a ClearML Agent. You can explicitly change this behaviour in your code.
IrateDolphin19 ClearML provides for saving files generated as part of your code execution through the https://clear.ml/docs/latest/docs/references/sdk/task#upload_artifact . For your use case, you can have your code thus create the artifact as it runs, you can set the specific storage location when you edit your configuration, through the task's output_uri field.
Does this help?