Well, just as you can pass the 'task_type' argument in PipelineDecorator.component , it might be a good option to pass the rest of the 'Task.init' arguments as they are passed in the original method (without using a dictionary)
That is a good question ... let me check 🙂
Sadly, I think we need to add another option like task_init_kwargs to the component decorator.
what do you think would make sense ?
Hi! I was wondering why ClearML recognize Scikit-learn scalers as Input Models...
Hi GiganticTurtle0
any joblib.load/save is logged by clearml (it cannot actually differentiate what it is used for ...)
You can of course disable it with Task.init(..., auto_connect_frameworks={'joblib': False})
Oh, I see. This explains the surprising behavior. But what if Task.init code is created automatically by PipelineDecorator.component ? How can I pass arguments to the init method in that case?
BTW, let's say I accidentally removed the 'default' queue from the queue list. As a result, when I try to stop an agent using clearml-agent daemon --stop , I get the following error:clearml_agent: ERROR: APIError: code 400/707: No queue is tagged as the default queue for this company
I have already created another queue also called 'default' but it had no effect :/
Yes, before removing the 'default' queue I was able to shut down agents without specifying further options after the --stop command. I just had to run clearml-agent daemon --stop as many times as there were agents. Of course, I will open the issue as soon as possible :D
GiganticTurtle0 Hi!
Which versions are you using? Also do you have an snippet example by chance?
GiganticTurtle0 is it just --stop that throws this error ?
btw: if you add --queue default to the command line I assume it will work, the thing is , without --queue it will look for any queue with the "default" tag on it, since there are none, we get the error.
regardless that should not happen with --stop I will make sure we fix it
Just so we do not forget, can you please open an issue on clearml-agent github ?
I'm using the last commit. I'm just fitting a scikit-learn MinMaxScaler object to a dataset of type tf.data.Dataset inside a function (which represents the model training step) decorated with PipelineDecorator.component . The function does not even return the scaler object as an artifact. However, the scaler object is logged as an artifact of the task, as shown in the image below.