It resolved.
I was doing 2 things wrong, defining params before task.init and while using task.connect(params) I was naming it and trying to set params using General/param_name

thanks for the support

How are you building your pipeline?
None
None

@<1523701205467926528:profile|AgitatedDove14> I am a bit lost, can you elaborate?

@<1523701205467926528:profile|AgitatedDove14>
Clearml version - 1.12.1

In the pipeline example None this specifically, at line 83
parameter_override={'General/num_boost_round': 250,
'General/test_size': 0.5,
'General/random_state': random_state}
these are fixed and cannot be changed by user when the pipeline is cloned, I am trying to make these parameters dynamic using .add_parameter method.

So, every task of has some hyperparameters,
eg task1 takes some int as it will filter out data
task2 takes some NN based params like optimizer, activation func

These params I am passing as whole, i mean as dictionary in my function to set parameters.
But apparently when I am cloning the pipeline and giving the inputs, it is not changing to those and running on original pipeline's inputs.
eg in the base task, epoch was 10, when I cloned the code I set the epoch to 5, it did not changed to 5

@<1585078763312386048:profile|ArrogantButterfly10> could it be that in the "base task" of the pipeline step, you do not have any hyper-parameter ? (I mean the Task that the pipeline clones and is supposed to set new hyperparameters for...)

anyway this got resolved\

like this.. But when I am cloning the pipeline and changing the parameters, it is running on default parameters, given when pipeline was 1st run

Just making sure, you are running the cloned pipeline with an agent. correct?
What is the clearml version you are using?
Is this reproducible with the pipeline example ?

@<1523701205467926528:profile|AgitatedDove14>
I am using pipeline by task and using pipe.add_parameter method to add the parameter through ui

pipe.add_parameter('random_state',2024) #model training
pipe.add_parameter('epochs',10)
pipe.add_parameter('learning_rate',0.001)

and then overriding the parameters using parameter_overide

pipe.add_step(
name='model_training',
parents=['preprocess_data'],
base_task_project=global_config.PROJECT_NAME,
base_task_name='model training',
parameter_override={'General/random_state': '${pipeline.random_state}',
'General/epochs': '${pipeline.epochs}',
'General/learning_rate': '${pipeline.learning_rate}'

like this.. But when I am cloning the pipeline and changing the parameters, it is running on default parameters, given when pipeline was 1st run

Hi @<1585078763312386048:profile|ArrogantButterfly10>

Now i want to clone the pipeline and change the hyperparameters of train task, is it possible? If so, how??

the pipeline arguments are for the pipeline DAG/logic, you need to pass one of the arguments as an argument for the training step/task. Make sense ?

Hi @<1585078763312386048:profile|ArrogantButterfly10> , can you please try with the new ClearML SDK v1.12.2rc0?

I will check

Image 1 shows original pipeline, image 2 show cloned pipeline and image 3 show parameters in cloned pipeline's run