Thanks for the explanations,
Yes that was the case This is also what I would think, although I double checked yesterday:I create a task on my local machine with trains 0.16.2rc0 This task calls task.execute_remotely() The task is sent to an agent running with 0.16 The agent install trains 0.16.2rc0 The agent runs the task, clones it and enqueues the cloned task The cloned task fails because it has no hyper-parameters/args section (I can seen that in the UI) When I clone the task manually usin...

I mean that I have a taskA (controller) that is in charge of creating a taskB with the same argv parameters (I just change the entry point of taskB)

yes exactly

CostlyOstrich36 , this also happens with clearml-agent 1.1.1 on a aws instance…

This is how I start the agent that is running the two experiments in parallel:
python3 -m trains_agent --config-file "~/trains.conf" daemon --queue default --log-level DEBUG --detached

ok, what is the 3.8 release? a server release? how does this number relates to the numbers above?

when can we expect the next self hosted release btw?

Yes I am 🙂

I hit F12 to check projects.get_all_ex but nothing is fired, I guess the web ui is just frozen in some weird state

btw CostlyOstrich36 , I can see in Profile > Version: 1.1.1-135 • 1.1.1 • 2.14 . What these numbers correspond to?

Thanks! (Maybe could be added to the docs ?) 🙂

Thanks!

Yea thats what I thought, I do have trains server 0.15

AgitatedDove14 Is it fixed with trains-server 0.15.1?

Awesome, thanks!

Yes that was my assumption as well, it could be several causes to be honest now that I see that also matplotlib itself is leaking 😄

And after the update, the loss graph appears

Thanks!

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

ok, so there is no way to cache it and detect when the ref changes?

yes, in setup.py I have:
..., install_requires= [ "my-private-dep @ git+ ", ... ], ...

I call task._update_requirements(my_reqs) regardless whether I am in the local machine or in the clearml agent, so "installed packages" section is always updated to the list my_reqs that I pass to the function, in this case ["."]

This one doesn’t have _to_dict unfortunately

yes that makes sense, I will do that. Thanks!

yes

Hi CostlyOstrich36 , I mean insert temporary access keys

They are, but this doesn’t work - I guess it’s because temp IAM accesses have an extra token, that should be passed as well, but there is no such option on the web UI, right?

I get the following error:

automatically promote models to be served from within clearml

Yes!

I am confused now because I see in the master branch, the clearml.conf file has the following section:
# Or enable credentials chain to let Boto3 pick the right credentials. # This includes picking credentials from environment variables, # credential file and IAM role using metadata service. # Refer to the latest Boto3 docs use_credentials_chain: falseSo it states that IAM role using metadata service should be supported, right?