Normally it's an environment variable set to the current user, you can check by typing echo $USER in the terminal

Note that you might need to log out login or reboot the machine for the change to take effect

Make sure you have entered the command
usermod -aG docker $USEROn the VM you are running your agent on

looks like the user running your clearML agent is not added to the docker group

If you're reffering to the https://www.nvidia.com/en-us/technologies/multi-instance-gpu/ I heard it was only supported by the Enterprise edition, since this tech is only available for the A100 GPUs, they most likely assumed that if you were rich enough to have one you would not mind buying the enterprise edition

Well its not working, this params seems to be used to override the repo to pull since it has a str type annotation anyway, ClearML still attempted to pull the repo

Ah nice, thanks ❤

(currently I am a SaaS customer in Pro tier)

Well aside from the abvious removal of the line PipelineDecorator.run_locally() on both our sides, the decorators arguments seems to be the same:
@PipelineDecorator.component( return_values=['dataset_id'], cache=True, task_type=TaskTypes.data_processing, execution_queue='Quad_VCPU_16GB', repo=False )And my pipeline controller:
` @PipelineDecorator.pipeline(
name="VINZ Auto-Retrain",
project="VINZ",
version="0.0.1",
pipeline_execution_queue="Quad_V...

(if for instance in wanna pull a yolov5 repo in the retraining component)

And Ithen can override it by specifying a repo on one of the components ?

Okay the force_store_standalone_script() works

Ah thank you I'll try that ASAP

The simplest would be to have your reverse proxy (eg: nginx)on your GCP VM directly and redirect the requests to that domain toward the clearml-server container imho

Yup, so if I understand this is strictly an Enterprise feature and is not planned to be available in the Pro version ?

Have you checked that annex ? None

you correctly assigned a domain and certificate ?

Yup, if you want to access it through https you're required to have a domain pointing to that IP with a certificate in place (using letsencrypt as instance) or else you'll get some SSL error

It seems like it, cause it's impossible to access an IP directly through https without using a domain name without certificate, it will solve this immediate problem at least

I suppose your worker is not persistent, so I might suggest having a very cheap instance as a persistent worker where you have your dataset persistently synced using . https://clear.ml/docs/latest/docs/references/sdk/dataset/#sync_folder and then taking the subset of files that interests you and pushing it as a different dataset, marking it as a subset of your main dataset id using a tag

AgitatedDove14 I have annotation logs from the end-user that I fetch periodically, I process it and I want to add it as a new version of my dataset where all versions correspond to the data collected during a precise time window, currently I'm doing it by fetching the latest dataset, incrementing the versionmm and creating a new dataset version

I would like instead of having to:
Fetch latest dataset to get the current latest version Increment the version number Create and upload a new version of the datasetTo be able to:
Select a dataset project by name Create a new version of the dataset by choosing what increment in SEMVER standard I would like to add for this version number (major/minor/patch) and upload

And by extension is there a way to upsert a dataset by automatically creating an entry wich a incremented version or create it if it does not exists ? Or am I forced to do a get, check if the latest version is fainallyzed, then increment de version of that version and create my new version ?

Well solved, it's not as beautiful but I guess i can put them in a env file with an arbitrary name in the init script and just pass that file as exec argument...

Well having a network inbcidient at HQ so this doesn't help.... but I'll keep you updqted with the tests I run tommorow

AgitatedDove14 Here you go, I think it's inside the container since it's after the worker pulls the image

Thanks for your reactivity 🎉

Yup I already setup my aws configs for clearML that way but I needed to have generally accessible credentials too so I used the init script option in this config menu ^^

SuccessfulKoala55 Mostly the VM instances types and properties, execution queue and app name.

CostlyOstrich36 Done ✅
https://github.com/allegroai/clearml/issues/811