Hi everyone,
I'm trying to deploy my first ClearML pipeline with a configuration for logging and caching to avoid repeating already computed steps. However, the caching doesn't seem to be working correctly. Despite not changing the configuration, the first step runs every time.
I've attached two images showing the pipeline executions. Both executions used the same configuration:

• In the first execution, the stage_data_retrieving step took 44 seconds.
• In the second execution, the same stage_data_retrieving step ran again, indicating that the caching did not work for this step.
• The second step, stage_data_preprocessing , was cached correctly and did not re-run but is based on the first step so it's kind of stupid.I suspect the issue might be related to the global_config and processors parameters. These parameters are dictionaries of specific configurations (dict of dict) that are the same but might not be taken into account properly by the caching mechanism.
  Can anyone help with this issue? I use the pipeline and call my step from functions !
  Thanks!