Great! Thank you

I don’t mean a serving endpoint, just the equivalent of “cloning an experiment” and running it on a different (larger) dataset.

Yes

If you were to add this, where would you put it? I can use a modified version of clearml-agent

AgitatedDove14 this works: pip install git+ssh://git@github.com/user/repo.git

Well this doesn’t work
pip install -e

The commit is valid for sure.

Ok!

Our code is tightly integrated with protobuffers which needs to be re-compiled every now and then. We have a script to do that. If that’s not done, some imports end up failing.

I am already forcing ssh auth

Is there a way to make it use ssh+git instead of git+git ? Maybe add a force_ssh_pip_install to the agent config?

If venv works inside containers that’s even better. We actually have custom containers that build on master merges. I wonder if using our own containers which should have most the deps will work better than a simpler container.

AgitatedDove14 can I specify a script to be run after pip install packages is done? I see that it’s possible in docker mode.

It doesn’t install it automatically, I think I need to specify it somewhere, see the above error. Or am I misunderstanding?

I think it works, I’m fixing something else that came up.

... more-itertools==8.6.0 -e git+git@github.com:user/private_package.git@57f382f51d124299788544b3e7afa11c4cba2d1f#egg=private_package msgpack==1.0.2 msgpack-numpy==0.4.7.1 ...

I hadn’t enabled that line when the failure happened.

Being able to create and remove queues as well as list their contents.

I’m wondering, would an older version of the agent work well with a newer server version and vice-versa?

AgitatedDove14 it was executed with Python 3 and I’m running in venv mode.

Issue seems fixed now, thanks! Is the fact that clearml-agent needs to be installed from system python mentioned anywhere in the docs, if not I suggest it gets added.

Thank you so much for helping.

OH! I was installing it on an env

I’ll try that!

I’m not sure but it seems like you get different kinds of flexibility depending on whether you enqueue the task yourself or whether you rely on execute_remotely . I think ideally if I could choose to get the benefit of auto-scanning provided by execute_remotely as well as more flexibility it would be great.

Great find! So a pip upgrade should fix it hopefully.

You’re saying there’s a built-in scheduler? SuccessfulKoala55
If so where can I find it?

The private_package can be installed by doing pip install git+ssh://git@github.com/user/private_package.git but the agent is trying to do pip install private_package which won’t work.

In fact, if there is a good python API to list/duplicate/edit/run experiments by ID, it seems straightforward to do that from Airflow (or any other job scheduler). I’m just wondering if there is some built-in scheduler.

It is indeed autopopulated by init

$ python --version Python 3.6.8 $ python repo/toy_workflow.py --logtostderr --logtoclearml --clearml_queue=ada_manual_jobs 2021-08-07 04:04:16,844 - clearml - WARNING - Switching to remote execution, output log page https://...
On the webpage logs I see this:
2021-08-07 04:04:12 ClearML Task: created new task id=f1092bcbe30249639122a49a9b3f9145 ClearML results page: `
2021-08-07 04:04:14
ClearML Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring
2021-08...