JitteryCoyote63 Not sure how/why the X-Pack feature was on (it is not used by the system), but you can disable it with an environment variable in the docker-compose
xpack.security.enabled=falseShould solve the problem ...

From here:
https://medium.com/@ospaarmann/tidbits-solving-the-elasticsearch-x-pack-license-issue-in-docker-d15bb22d82fd

it is using the wrong URL - an https:// instead of an s3:// so it can't upload the preprocess.py file

What do you see as the link in the UI for that specific model ?
What is the full model add command ?

Hmm that is odd.
Can you verify with the latest from GitHub?
Is this reproducible with the pipeline example code?

Nice SoreHorse95 !
BTW: you can edit the entire omegaconf yaml externally with set/get configuration object (name = OmegaConf) , do notice you will need to change Hydra/allow_omegaconf_edit to true

And it is not working ? what's the Working Dir you have under the Execution Tab ?

Hmm MiniatureHawk42 how many files in the zip ?

Hi UnevenDolphin73
If you "remove" the lock file the agent will default to pip.
You can hack it with uncommitted changes section?

CloudyHamster42
RC probably in a few days, but notice that it will just remove the warnings, I still can't reproduce the double axis issue.

It will be helpful if you could send a small script to reproduce the problem.

Maybe this example code can help ? https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py

Ubuntu? which version?

VexedCat68

But what's happening is, that I only publish a dataset once but every time it polls,

this seems wrong (i.e a bug?!), how do you setup the trigger ? is the Trigger Task constantly running or are you re-launching it?

It will automatically switch to docker mode

Yes, experiments are standalone as they do not have to have any connecting thread.
When would you say a new "run" vs a new "experiment" ? when you change a parameter ? change data ? change code ?
If you want to "bucket them" use projects 🙂 it is probably the easiest now that we have support for nested projects.

SmarmySeaurchin8 it could be a switch, the problem is that when you have automatic stopping flows, they will abort a task, which is legitimate (e.g. should not considered failed)
How come you have aborted tasks in the pipeline ? If you want to abort the pipeline, you need to first abort the pipeline Task then the tasks themselves.

make sure you follow all the steps :
https://clear.ml/docs/latest/docs/deploying_clearml/upgrade_server_linux_mac
(basically make sure you get the latest docker-compose.yml and the pull it
curl -o /opt/clearml/docker-compose.yml docker-compose -f /opt/clearml/docker-compose.yml pull docker-compose -f /opt/clearml/docker-compose.yml up -d

Hi UnsightlySeagull42

does anyone know how this works with git ssh credentials?

These will be taken from the host ~/.ssh folder

set a parameter in that task and enqueue it

how do you do that?

oh that makes sense.
I would add to your Task's docker startup script the following:

ls -la /.ssh
ls -la ~/.ssh
cat ~/.ssh/id_rsa

Let's see what you get

It might do the same ?!

s there any way to see datasets uploaded to ClearML Data without downloading them using ClearML Data?

Hi VexedCat68
Currently when you create datasets with clearml-data it has to repackage your files, i.e. upload them. That said we have received numerous requests on "registering data", and we are looking into it.
Here is the main technical hurdles we are facing, and I would love to get your perspective:
If the data is not available locally, we cannot calculate the hash of the conten...

(We should probably better state it in the GitHub readme)

Thanks! Let me check if we can reproduce it. BTW what's your clearml package version?

AdorableFrog70 taking another look at the MLFlow exporter, it would not be complicated to convert it to MLFlow to ClearML exporter, that would also be cool

😞 It's working as expected for me...
That said I tested on Linux & pip,
Any specific req to test with? from the log I see this is conda on windows, are you using the base conda env or a venv inside conda?

IdealPanda97 Hmm I see...
Well, unfortunately, Trains is all about free access to all 🙂
That said, the Enterprise edition does add permissions and data management on top of Trains. You can get in touch through the https://allegro.ai/enterprise/#contact , I'm sure someone will get back to you soon.

What's the OS / Python version?

No, clearml uses boto, this is internal boto error, which points bucket size limit, see the error itself

CheerfulGorilla72 as I understand there were some delays wit the current release, so it is going to be out this week. The one after that includes this feature and as far as I understand would be mid Dec.

ohh, could it be a 32bit version of python ?

Hmmm, are you running inside pycharm, or similar ?

Wait, how did you end up with
clearml_task_id = os.environ['CLEARML_TASK_ID']printing "01b77a220869442d80af42efce82c617" ?
This means you are running by an agent?!