yes, because it won’t install the local package which has this setup.py with the problem in its install_requires described in my previous message

torch==1.7.1 git+ .

When an experiment on trains-agent-1 is finished, I see randomly no experiment/long experiment and when two experiments are running, I see randomly one of the two experiments

Yes 🙂 Thanks!

Ok, by setting PyJWT==1.7.1 in the setup.py of the experiment pip did not enforced the update

If the reporting is done on a subprocess, I can imagine that the task.set_initial_iteration(0) call is only effective in the main process, not in the subprocess used for reporting. Could it be the case?

AgitatedDove14 I see https://github.com/allegroai/clearml-session/blob/main/clearml_session/interactive_session_task.py#L21= that a key pair is hardcoded in the repo. Is it being used to ssh to the instance?

AgitatedDove14 awesome! by "include it all" do you mean wizard for azure and gcp?

Yes! not a strong use case though, rather I wanted to ask if it was supported somehow

Yea, the config is not appearing in the webUI anymore with this method 😞

AgitatedDove14 I am actually considering rolling back to 1.1.0, so 1.3.0 is not really an option for now

See my answer in the issue - I am not using docker

yes, will do now!

I also don't understand what you mean by unless the domain is different... The same way ssh keys are global, I would have expected the git creds to be used for any git operation

0.17.1

(I didn't have this problem so far because I was using ssh keys globaly, but I want know to switch to git auth using Personal Access Token for security reasons)

this is the last line, same a before

I got some progress TimelyPenguin76 , Now the task runs and I get the error from docker:
docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

is there a command / file for that?

Hi CostlyOstrich36 , I mean insert temporary access keys

Sure, just sent you a screenshot in PM

So I want to be able to visualise it quickly as a table in the UI and be able to download it as a dataframe, which of report_media or artifact is better?

As a quick fix, can you test with auto refresh (see top right button with the pause sign you have on the video)

That doesn’t work unfortunately

That said, v1.3.1 is already out, with what seems like a fix:

So you mean 1.3.1 should fix this bug?

Done! Also I tried to use git cache ( https://git-scm.com/docs/git-credential-cache ) as a workaround (hoping that the first time it clones the experiment repo, it caches the creds for the next times, but I then get a different error: fatal: unable to find a suitable socket path; use --socket )

under /opt/clearml/config/apiserver.conf

That would be awesome, yes, only from my side I have 0 knowledge of the pip codebase 😄

MagnificentSeaurchin79 You could also just fork the tensorflow repo, make changes in a specific branch and specify your forked repo with your custom branch in the install_requires of your setup.py

edited the aws_auto_scaler.py, actually I think it’s just a typo, I just need to double the brackets

no, simple GET