I'll try and see if it reproduces on my side, thanks! 🙂
I'd suggest using the agent in --docker mode
Hi FancyWhale93 , do you have a snippet that reproduces this?
VexedCat68 , that's a good question! I'm not sure that ClearML keeps track of that, I need to check on that.
However, I think a neat solution could be using the datasets as task configuration parameters. This way you can track which datasets were used and you can set up new runs with different datasets.
Role based access controls are supported only in Scale/Enterprise versions.
Hi @<1618780810947596288:profile|ExuberantLion50> , what happens if you set the python binary path itself? Also, any specific reason you're not running in docker mode?
How are you saving your models? torch.save ("<MODEL_NAME>")
?
Hi @<1577468611524562944:profile|MagnificentBear85> , are you running the services agent in docker mode or venv?
Hi @<1655744373268156416:profile|StickyShrimp60> , do you have any code that can reproduce this behavior?
Hi @<1558986867771183104:profile|ShakyKangaroo32> , you can do it but keep in mind that models/artifacts/debug samples are all referenced as links inside mongo/ES, you'd have to migrate the databases for that
StickyCoyote36 , Hi!
Is the agent ignoring the requirements.txt
and only installing the "installed packages" in the task or installing them both?
Hi @<1570220858075516928:profile|SlipperySheep79> , nested pipelines aren't supported currently. What is the use case that you need it for?
Hi @<1817731748759343104:profile|IrritableHippopotamus34> , are you only using StorageManager
or also the Task
object? Do you have a code snippet that reproduces this behaviour?
Hi @<1708653001188577280:profile|QuaintOwl32> , you can set some default image to use. My default for most jobs is nvcr.io/nvidia/pytorch:23.03-py3
What actions did you take exactly to get to this state?
Hi @<1523702496097210368:profile|ScantChimpanzee51> , that information is saved on the task object. You can fetch it either with the API or the SDK
you can find the different cache folders that clearml uses in ~/clearml.conf
VexedCat68 , correct. But not only arg parse. The entire configuration section 🙂
I think you need to do latest_dataset = Dataset.get(dataset_id=<DATASET_ID>)
Hi JumpyPig73 ,
It appears that only the AWS autoscalar is in the open version and other autoscalars are only in advanced tiers (Pro and onwards):
https://clear.ml/pricing/
Hi CostlyFox64 ,
Can you try configuring your ~/clearml.conf
with the following?agent.package_manager.extra_index_url= [ "https://<USER>:<PASSWORD>@packages.<HOSTNAME>/<REPO_PATH>" ]
Hi @<1582179652133195776:profile|LudicrousPanda17> , I suggest doing a similar filtering in the UI with dev tools open (F12) and see what is sent by the web UI 🙂
I would suggest googling that error
Hi @<1695969549783928832:profile|ObedientTurkey46> , this is supported in the Scale/Enterprise licensees of ClearML (external IdP support). API access is always done using credentials.
Hi @<1648134232087728128:profile|AlertFrog99> , not sure I understand. Can you please elaborate on your use case?
@<1664079296102141952:profile|DangerousStarfish38> , are you running different python versions on the different machines? Remote vs local
Hi @<1736919317200506880:profile|NastyStarfish19> , the services queue is for running the pipeline controller itself. I guess you are self hosting the OS?
Then the services agent should be part of it. There also should be a 'services' queue that by default should listen to the services queue