hi FiercePenguin76
Can you also send your clearml packages versions ?
I would like to sum your issue up , so that you could check i got it right

you have a task that has a model, that you use to make some inference on a dataset you clone the task, and would like to make inferences on the dataset, but with another modelthe problem is that you have a cloned task with the first model....

How have you registered the second model ? Also can you share your logs ?

FiercePenguin76 Are you changing the model by pressing the circled button in the first photo? Are you promted with a menu like in the second photo?

MotionlessCoral18 If you provide the model as a hyperparam, then I believe you should query its value by calling https://clear.ml/docs/latest/docs/references/sdk/task/#get_parameters or https://clear.ml/docs/latest/docs/references/sdk/task/#get_parameter

all subsequent invocations are done by cloning this task in UI and changing the model task_id

no, I’m providing the id of task which generated the model as a “hyperparam”

I tried this, but didn’t help:
input_models = current_task.models["input"] if len(input_models) == 1: input_model_as_input = {"name": input_models[0].name, "type": ModelTypeEnum.input} response = current_task.send(DeleteModelsRequest( task=current_task.task_id, models=[input_model_as_input] ))

FiercePenguin76 Looks like there is actually a bug when loading models remotely. We will try to fix this asap

SmugDolphin23 sorry I don’t get how this will help with my problem

also, I don’t see an edit button near input models

in cloned tasks, the correct model is being applied, but the original one stays registered as input model

I guess you can easily reproduce it by cloning any task which has an input model - logs, hyperparams etc are being reset, but inputmodel stays.

clearml==1.5.0
WebApp: 1.5.0-192 Server: 1.5.0-192 API: 2.18

I had a bunch of training tasks each of which outputted a model. I want to apply each one of them to a specific dataset. I have a clearml task ( apply_model ) for that, which takes dataset_id and model-producing task_id as input. First time I initiate apply model by hardcoding ids and starting the run from my machine (it is then goes into cloud, when it reaches execute_remotely )

I am not registering a model explicitly in apply_model . I guess it is done automatically when I do this:
output_models = train_task_with_model.models["output"] model_descriptor = output_models[0] model_filename = model_descriptor.get_local_copy()

thanks for all those precisions. I will try to reproduce and keep you updated 🙂