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Is There Any Simple Way To Orchestrate A Batch To Train A Model With Different Features (In Order To Do Feature Selection, For Example) Through A Single .Py File? I Saw The Following Example

Is there any simple way to orchestrate a batch to train a model with different features (in order to do feature selection, for example) through a single .py file? I saw the following example https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py , but it only changes model hyperparameters

  
  
Posted one year ago
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Answers 14


Correct, but do notice that (1) task names are not unique and you can change them after the Task was executed (2) when you clone the Task, you can actually rename it, when an agent is running the Task, basically the init function is ignored, because the Task already exists. Make sense ?

  
  
Posted one year ago

So yes, it creates the Task on your machine (with the name and project etc.)
Then it stops the local process, and pushes it into the exxecution queue, when the agent pulls it, and re-executes the code, it will ignore the Task.init

  
  
Posted one year ago

yes, but is there a way to generate multiple tasks like I mentioned using task.init in different points of a .py and and run each of them as a separate remote exercution? Didn you just say that once I trigger the task.execute_remotely it will ignore the task.init?

  
  
Posted one year ago

Oh think I understand you point now.
basically you can:

Create the initial Task, once it is in the system clone it and adjust parameters externally. A simple example here:
https://github.com/allegroai/clearml/blob/0397f2b41e41325db2a191070e01b218251bc8b2/examples/automation/manual_random_param_search_example.py#L41

wdyt?

  
  
Posted one year ago

using only a subset of the features

ShallowGoldfish8 if you have some parameter that controls it (i.e. select different features) then you can launch it with two sets f parameters.
Am I missing something?
for example:
` my_features_select = {"type": "set_a"}
Task.current_task().connect(my_features_select)

if my_features_select["type"] == "set_a":

do something

else

do something else `wdyt?

  
  
Posted one year ago

I was checking here, and apparently if I use a parameter as suggested, together with a Task.init(task_name=f'{task name in this loop}') for each of the loops it should work, right? Creating different tasks in the server

  
  
Posted one year ago

Hi ShallowGoldfish8 , can you elaborate please? You mean train with different data?

  
  
Posted one year ago

yes, variations of the data, using only a subset of the features

  
  
Posted one year ago

Could I just build it and log these parameters using

task.set_parameters()

so that I call

task.get_parameters()

later?

instead of manually calling set/get, you call task.connect(some_dict_or_object) , it does both:
When running manually (i.e. without an agent) it logs the keys/values on the Task,
when running with an agents, it takes the values from the backend (Task) and sets them on the dict/object
Make sense ?

  
  
Posted one year ago

Looks quite good indeed! Thanks! Is there in the repository the experiment template used in this example? Just not fully sure how the parameters are used/connected in it. Could I just build it and log these parameters using task.set_parameters() so that I call task.get_parameters() later?

  
  
Posted one year ago

Totally! Thank you very much!

  
  
Posted one year ago

Is there a way to do that to trigger separate remote executions?

  
  
Posted one year ago

regarding (2), if use run_remote, does it also ignore the init?
Considering something along the lines of
https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py

  
  
Posted one year ago
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