In the case of api call,

given that i have id of the task I want to stop, I would make a post request to [CLEARML_SERVER_URL]:8080/tasks.stop

with the request body set up like the one mentioned in the api?

AgitatedDove14

Also, the steps say that I should run the serving process on the default queue but I've run it on a queue I created called a serving queue and have an agent listening for it.

I'm kind of new to developing end to end applications so I'm also learning how the predefined pipelines work as well. I'll take a look at the clear ml custom pipelines

This works, thanks. Do you have any link to where I can also see the parameters of the Dataset class or was it just on git?

In another answer, I was shown that I can access it like this. How can I go about accessing the value of merged_dataset_id which was returned by merge_n_datasets and stored as an artifact.

Have never done something like this before, and I'm unsure about the whole process from successfully serving the model to sending requests to it for inference. Is there any tutorial or example for it?

It was nargs="?" I think

I'm getting a response 401

Still unsure between finalize and publish? Since upload should upload the data to the server

Thank you, this is a big help. I'll give this a go now.

I then did what MartinB suggested and got the id of the task from the pipeline DAG, and then it worked.

I understand your problem. I think you normally can specify where you want the data to be stored in a conf file somewhere. people here can better guide you. However in my experience, it kinda uploads the data and stores it in its own format.

I think so, thank you.

Also my execution just completed and as of yet, I can only see the hyperparameters as a report. not in a configurable form. I've just started with ClearML and am having these issues.

pip install clearml

bump

Can you spot something here? Because to me it still looks like it should only create a new Dataset object if batch size requirement is fulfilled, after which it creates and publishes the dataset and empties the directory.

Once the data is published, a dataset trigger is activated in the checkbox_.... file. which creates a clearml-task for training the model.

On both the main ubuntu and the vm, I simply installed it in a conda environment using pip

Found it.

https://clear.ml/docs/latest/docs/guides/clearml-task/clearml_task_tutorial/

The second example here, executing a local script. I think that was it. Thank you for the help.

You're suggesting that the false is considered a string and not a bool? Am I understanding it correctly? Also, in that case, wouldn't this problem also occur when I originally create the task using clearml-task?

Or am I not understanding it clearly.

Basically, as soon as I get the trigger that a new dataset has been published, I want to pass the dataset id to the script as an cli argument and pass the code to the agent

Previously I wasn't. I would just call model.save, but I was unsure how to do modifications in the output model, which is why I made the output model.

I hope you understood my problem statement. I want to solve the issue with or without output model. Any help would be appreciated.

Basically, at the least, would like to be able to add tags, set the name and choose to publish the model that I'm saving.

I'll look into it. Thank you everyone.

yeah.

I checked the value is being returned, but I'm having issues accessing merged_dataset_id in the preexecute_callback like the way you showed me.

I've tried the ip of the ClearML Server and the IP of my local machine on which the agent is also running on and none of the two work.

I'm both printing it and writing it to a file

I'm not using decorators. I have a bunch of function_steps followed by a normal task step, where I've passed a base_task_id.

I want to check the value of one of the functional steps, and if it holds true, I want to execute the task step otherwise I want the pipeline to end there, since the task step is the last one.

It's an artifact.