Also interested in how this is being approached 🙂 What you mentioned is exactly what I am doing

Currently running it on a t3.xlarge which has 4CPU's, 16GB RAM and 300GB SSD

If anyone knows a better way, would love to hear about it 🙂

If you can identify a patten in the YOLOv8 output files you can probably also filter them out 🙂

If you added a print there like:

def filter_out_pt_files(operation_type, model_info):
    print(model_info.__dict__)

    return model_info

You can see what is bring picked up. If there is a common path etc you can filter that out

I need to add callback for it to filter out anything with .pt

Hey 🙂 I had a similar issue today and found this solution:

In my case this codebase was using a .pt filetype which was being picked up and logged as a model even though it was not.

import os
from clearml import Task
from clearml.binding.frameworks import WeightsFileHandler

task = Task.init(
    project_name="task_project",
    task_name="task_name",
    task_type=Task.TaskTypes.training,
)


def filter_out_pt_files(operation_type, model_info):
    is_pt_file = os.path.splitext...

This is a great place to start on PyTorch Lightning

None

For an update 🙂
I think we identified that when moving from a training to fine tuning dataset (which was 1/1000th the size) our training script was set to upload every epoch. Seems like this resulted in a torrent of metrics being uploaded.

Since modifying this to be less frequent we have seen the index latency drop dramatically

Yep, almost a self DDoS

As pytorch lightning is a framework on top of Pytorch it will work the same, if not better with Clear ML

Might be worth running the command again with the --verbose flag. It will likely give more details on what is causing the failure

What does it look like when you instantiate the output_model object?

That looks good to me, not sure

One option might be to delete the local copy of the dataset and try to re-download it. Perhaps something has gone wrong with the local copy?

Looks like its a /mnt which might mean its a drive or something similar that was connected and may not be any more?

For something quick, if you create a new folder to put your dataset:
mkdir ./test_dataset_location
Then you can run your command with
CLEARML_CACHE_DIR='./test_dataset_location' clearml-data ... <your command here>

It will try to download into that folder

Hope you can get something to work 🤞

It happens, happy training 🚀

Also the error you are showing is inside the calculate_metrics.py

Is that a clear-ml lib or something custom