sorted by using command below before docker-compose call
export DOCKER_DEFAULT_PLATFORM=linux/amd64

I noticed that when a pipeline step returns an instance of a class, it tries to pickle. I am currently facing the issue with it not being able to pickle the output of the "load_baseline_model" function
` Traceback (most recent call last):
File "/tmp/tmpqr2zwiom.py", line 37, in <module>
task.upload_artifact(name=name, artifact_object=artifact)
File "/home/zanini/repo/RecSys/.venv/lib/python3.9/site-packages/clearml/task.py", line 1877, in upload_artifact
return self._artifacts_man...

yes, but is there a way to generate multiple tasks like I mentioned using task.init in different points of a .py and and run each of them as a separate remote exercution? Didn you just say that once I trigger the task.execute_remotely it will ignore the task.init?

I will try the suggested edit here

regarding (2), if use run_remote, does it also ignore the init?
Considering something along the lines of
https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py

It worked!

Looks quite good indeed! Thanks! Is there in the repository the experiment template used in this example? Just not fully sure how the parameters are used/connected in it. Could I just build it and log these parameters using task.set_parameters() so that I call task.get_parameters() later?

That's the script that produces the error. You can also observe the struggle with importing the load_model function. (Any tips on best practices to structure the pipeline are also gladly accepted)

Apparently the error comes when I try to access from get_model_and_features the pipeline component load_model . If it is not set as pipeline component and only as helper function (provided it is declared before the components that calls it (I already understood that and fixed, different from the code I sent above).

Also, I was using tensorboard

After commenting all the metric/plot reporting, we noticed the model was not uploading the artifacts to S3. A solution was to add wait_for_upload in task.upload_artifact()