@<1523701070390366208:profile|CostlyOstrich36> Then when I try to get the dataset I get the following error

Failed getting object size: RetryError('HTTPSConnectionPool(host='files.clearml.dbrain.io', port=443): Max retries exceeded with url: /Labeled%20datasets/.datasets/printed%20multilang%20crops/printed%20multilang%20crops.1e76fd4ad77f4d2790e4acf1c8241c59/artifacts/state/state.json (Caused by ResponseError('too many 503 error responses'))')
Could not download 

 , err: HTTPSConnectionPool(host='files.clearml.dbrain.io', port=443): Max retries exceeded with url: /Labeled%20datasets/.datasets/printed%20multilang%20crops/printed%20multilang%20crops.1e76fd4ad77f4d2790e4acf1c8241c59/artifacts/state/state.json (Caused by ResponseError('too many 502 error responses')) 
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[34], line 1
----> 1 dataset1 = Dataset.get(dataset_id='1e76fd4ad77f4d2790e4acf1c8241c59')

File ~/miniconda3/lib/python3.9/site-packages/clearml/datasets/dataset.py:1731, in Dataset.get(cls, dataset_id, dataset_project, dataset_name, dataset_tags, only_completed, only_published, include_archived, auto_create, writable_copy, dataset_version, alias, overridable, shallow_search, **kwargs)
   1727     instance = Dataset.create(
   1728         dataset_name=dataset_name, dataset_project=dataset_project, dataset_tags=dataset_tags
   1729     )
   1730     return finish_dataset_get(instance, instance._id)
-> 1731 instance = get_instance(dataset_id)
   1732 # Now we have the requested dataset, but if we want a mutable copy instead, we create a new dataset with the
   1733 # current one as its parent. So one can add files to it and finalize as a new version.
   1734 if writable_copy:

File ~/miniconda3/lib/python3.9/site-packages/clearml/datasets/dataset.py:1643, in Dataset.get.<locals>.get_instance(dataset_id_)
   1635     local_state_file = StorageManager.get_local_copy(
   1636         remote_url=task.artifacts[cls.__state_entry_name].url,
   1637         cache_context=cls.__cache_context,
   (...)
   1640         force_download=force_download,
   1641     )
   1642     if not local_state_file:
-> 1643         raise ValueError("Could not load Dataset id={} state".format(task.id))
   1644 else:
   1645     # we could not find the serialized state, start empty
   1646     local_state_file = {}

ValueError: Could not load Dataset id=1e76fd4ad77f4d2790e4acf1c8241c59 state

@<1523701070390366208:profile|CostlyOstrich36>
One more moment. When I look at the dataset in web UI, I see like dataset switched to final

Problem solved:

• removed limits everywhere and live time for downloading everywhere
• increased limits for the file server

Hi @<1524560082761682944:profile|MammothParrot39> , I think that might be the issue. Is this a self deployed server?

@<1523701070390366208:profile|CostlyOstrich36> Yes, we deployed clearml in our outline

Hi @<1524560082761682944:profile|MammothParrot39> , I have the same problem. Could you elaborate on the solution you found?