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BitingDeer35
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2 Questions, 4 Answers
  Active since 10 November 2023
  Last activity 4 months ago

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4 months ago
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4 months ago
0 Hi There, Currently I Have A Clearml Pipeline That Takes In A Bunch Of Parameters For Various Tasks And Passes These Parameters Via Parameter_Override For Every Pipe.Add_Step(). However, I Have A Lot Of Parameters, And So My Pipeline Code Is A Little Unwi

I see I see... I'll keep the decorator way to do it in mind; for these step configs, would it make sense if they are in the form of, for example {$pipeline.parameter} and {$step_1.id}? Or if not what is the way to go about referencing other steps?

4 months ago
0 Hi There, Currently I Have A Clearml Pipeline That Takes In A Bunch Of Parameters For Various Tasks And Passes These Parameters Via Parameter_Override For Every Pipe.Add_Step(). However, I Have A Lot Of Parameters, And So My Pipeline Code Is A Little Unwi

@<1523702000586330112:profile|FierceHamster54> That's probably a good idea yeah, my question is would PipelineDecorator still be okay if I have multiple iterations of certain steps? For example if I call

for i in range(5):
step_x(...)

And how would consolidating all these step_xs work?

4 months ago
0 Hi There, Currently I Have A Clearml Pipeline That Takes In A Bunch Of Parameters For Various Tasks And Passes These Parameters Via Parameter_Override For Every Pipe.Add_Step(). However, I Have A Lot Of Parameters, And So My Pipeline Code Is A Little Unwi

Hi @<1523701435869433856:profile|SmugDolphin23> , would that mean that multiple pre_callback()s would have to be defined for every add_step, since every step would have different configs? Sorry if there's something I'm missing, I'm still not quite good at working with ClearML yet.

4 months ago
0 Hi, I Have A Question About The Pipeline, Especially About The Parallelism Part. We Are Considering Implementing A Use Case And Are Interested In Knowing Whether It Can Be Efficiently Managed Using Clearml Pipeline. Our Use Case Involves A Dataset That

Hi Jason, yes this can be done. Your pipeline code will look like this:

Execution of preprocessing task

for i in range(125):
Execution of data splitting and inference task(s); each of the 125 tasks have the same base task name but different names, e.g. name = "inference_task" + str(i)
<end loop>

ids = ["${inference_task_" + str(i) + ".id}" for i in range(125)]
Execution of aggregation task with the ids passed in as some part of parameter_override e.g. "General/inference_ids": '[' + ','.jo...

7 months ago