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Hello Everyone, I'M Working On A Flexible Machine Learning Pipeline Using Clearml, Covering Data Retrieval, Preprocessing, Training, And Evaluation. I Want To Add Hyperparameter Optimization (Hpo) As A Pipeline Step That Could Optimize The Preprocessing A

Hello everyone,
I'm working on a flexible machine learning pipeline using ClearML, covering data retrieval, preprocessing, training, and evaluation. I want to add hyperparameter optimization (HPO) as a pipeline step that could optimize the preprocessing and training steps (I'm using add_function_step), but I'm facing some challenges:

  • How can I integrate HPO into the ClearML pipeline in a generic way?
  • What's the best way to pass optimized parameters between steps in an adaptable pipeline?
  • How can I ensure I'm following ClearML best practices for this kind of integration?Has anyone successfully tackled this before? I'd love to hear about your experiences, particularly:
  • How you integrated HPO into a ClearML pipeline without tying it to specific parameters
  • Examples of setting up HPO as a pipeline step that works well with other steps
  • Methods for passing optimized hyperparameters between steps in a versatile pipelineAny advice or examples would be greatly appreciated. Thanks in advance for your help!
  
  
Posted one month ago
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Answers 2


thank you very much, my pipeline is already set up and works but the concern was on how integrating HPO for some of the steps within the pipeline. I will have a look on the HPO optimizer class

  
  
Posted one month ago

Hi @<1730396272990359552:profile|CluelessMouse37> , I would suggest reviewing the relevant docs regarding pipelines & HPO and then running said examples before integrating them 🙂

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I think if you run all examples and understand what the code is doing, sewing the two together should be fairly streightforward

  
  
Posted one month ago
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one month ago
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