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Clearml Pipelines Can Be Build From Tasks, Functions, And Decorated Functions, According To The Examples In

ClearML pipelines can be build from tasks, functions, and decorated functions, according to the examples in None . I am guessing there was a certain way of doing things, that allgegro.ai started with and then there was a pain that the other approaches solved. So, what are the rules-of-thumb when to use which approach?

  
  
Posted one year ago
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Hi @<1523704157695905792:profile|VivaciousBadger56>
No these are 3 different ways of building pipelines.
Creating from decorators is recommended when each component can be easily packages into a single function (every function can have an accompanying repository).
Here the idea it is very easy to write complex execution logic, basically the automagic does serialization/deserialization so you can write pipelines like you would code python.

Creating from Tasks is a good match if you need to just run Tasks in a DAG manner, where input and outputs are connected, and the execution logic is basically the DAG. It does mean the components (i.e. Tasks) are aware of the way inputs/outputs are passed (i.e. hyper parameters, artifacts / models etc.)

Creating from functions is a middle ground between these two approaches, it is DAG execution where each component is a standalone function (again, you can attach git repository and base docker image per component)

Does that help?

  
  
Posted one year ago
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