I just shared manually the logs because it had email and other details mentioned in the complete logs. If it helps, I'll share the logs as soon as I can.

What about amount of storage required?

Basically the environment/container the agent is running in needs to have specific cuda installed. Is that correct CostlyOstrich36 ?

dataset = Dataset.create(data_name, project_name)
            print('Dataset Created, Adding Files...')
            dataset.add_files(data_dir)
            print('Files added succesfully, Uploading Files...')
            dataset.upload(output_url=upload_dir, show_progress

I download the dataset and model, and load them. Before training them again.

Thus I wanted to pass the model id from the prior step to the next one.

any command starting with clearml

CostlyOstrich36 This didn't work, the value is -1 however the pipe didn't stop.

I'm assuming the triton serving engine is running on the serving queue in my case. Is the serving example also running on the serving queue or is it running on the services queue? And lastly, I don't have a clearml agent listening to the services queue, does clearml do this on its own?

You mean I should set it to this?

I was looking to see if I can just get away with using get_local_copy instead of the mutable one but I guess that is unavoidable.

Basically if I pass an arg with a default value of False, which is a bool, it'll run fine originally, since it just accepted the default value.

You could be right, I just had a couple of packages with this issue so I just removed the version requirement for now. Another issue that might be the case, might be that I'm on ubuntu some of the packages might've been for windows thus the different versions not existing

https://github.com/ultralytics/yolov5

It works this way. Thank you.

this is the console output

Well yeah, you can say that. In add function step, I pass in a function which returns something. And since I've written the name of the returned parameter in add_function_step, I can use it, but I can't seem to figure out a way to do something similar using a task in add_step

Thank you for the help guys.

Also the repository is on bitbucket which is why I set git_host to that.

{"meta":{"id":"c3edee177ae348e5a92b65604b1c7f58","trx":"c3edee177ae348e5a92b65604b1c7f58","endpoint":{"name":"","requested_version":1.0,"actual_version":null},"result_code":400,"result_subcode":0,"result_msg":"Invalid request path /","error_stack":null,"error_data":{}},"data":{}}

I'm getting this error.

clearml_agent: ERROR: Failed cloning repository.

1. Make sure you pushed the requested commit:
2. Check if remote worker has valid credentials

That but also in proper directory on the File System

so code goes

I use that model in another step.

So I got my answer, for the first one. I found where the data is stored in the server

CostlyOstrich36

I think I understand now that I first need to have clearml server up and running.

AgitatedDove14 Your second option is somewhat like how shortcuts work right? Storing pointers to the actual data?

before pipe.add_step(train_model)?

When I try to access the server with the IP I set as CLEARML_HOST_IP, it looks like this. I set that IP to the ip assigned to me by the network