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Answered
Currently, I’M Manually Tracking Multiple Output Models At The End Of The Training Process

Currently, Iā€™m manually tracking multiple output models at the end of the training process
model_paths = list(Path(checkpoint_dir).absolute().glob('*')) for model_path in model_paths: output_model = OutputModel(task=task, name=f'{args.task_name}-{model_path.stem}', framework='PyTorch') output_model.update_weights(weights_filename=str(model_path), target_filename=model_path.stem) # task.update_output_model(model_path=str(model_path), # model_name=f'{args.task_name}-{model_path.stem}')The problem with this approach is when I publish my experiment, only one (the first or the last OutputModel, idk) of all logged models is published, the others remain Draft .
Is this a feature of ClearML? If not, how could I fix it? Thanks!

  
  
Posted one year ago
Votes Newest

Answers 5


Hi GrittyKangaroo27 ,
By default, ClearML publishes the last output model when publishing a task - the reasoning it that this is the latest model and represents the result.
Do you publish you task programmatically (i.e. by calling task.publish() )?

  
  
Posted one year ago

Thanks!

  
  
Posted one year ago

If the use-case is important to you, please open a GitHub issue with a feature request šŸ™‚

  
  
Posted one year ago

I see... Well, currently this will only publish the latest model. You can publish the other models programmatically using something like:
for model in task.models.output[:-1]: model.publish()

  
  
Posted one year ago

I publish the task with UI interface

  
  
Posted one year ago