Hi @<1669152726245707776:profile|ManiacalParrot65> ,

Yes, you can wrap the separate function with a decorator so the function will run as a separate step in the pipeline, and even can cache the step for multi runs.

You can also add the function without a decorator, as a step to the pipeline with PipelineController.add_function_step() .

You can read about it [here](https://clear.ml/docs/latest/docs/pip...

Hi @<1742355077231808512:profile|DisturbedLizard6> ,

Currently, only argparse arguments are supported for clearml-task , click is also support, but for now, with the python sdk.

Hi @<1747066118549278720:profile|WhoppingToad71> , can you share the use case? You want to upload the file to some storage? Or upload to a task?

Can I suggest using the sdk? It will do both, log it to the task and will upload it to any storage wanted, like in this example

With the API you can register an artifact to a task, but the upload will be done separately with the ClearML sdk (the sdk wrap the registration and upload, with some other things inside the upload_artifact function).

Hi @<1742355077231808512:profile|DisturbedLizard6> , not sure I get that, did you use torch.save (like in here ) or some other command to save the models? When running with the clearml-agent. you have a print of all the configurations at the beginning of the log, can you verify your values are as you configure it?

Additionally, which version of clearml , clearml-agent and `...