Hi TimelyPenguin76 , I guess it tries to spin them down a second time, hence the double print

That was also my feeling! But I though that spawning the trains-agent from a conda env would isolate me from cuda drivers on the system

I can probably have a python script that checks if there are any tasks running/pending, and if not, run docker-compose down to stop the clearml-server, then use boto3 to trigger the creating of a snapshot of the EBS, then wait until it is finished, then restarts the clearml-server, wdyt?

--- /data ---------- 48.4 GiB [##########] /elastic_7 1.8 GiB [ ] /shared 879.1 MiB [ ] /fileserver . 163.5 MiB [ ] /clearml_cache . 38.6 MiB [ ] /mongo 8.0 KiB [ ] /redis

BTW, is there any specific reason for not upgrading to clearml?

I just didn't have time so far 🙂

Very nice! Maybe we could have this option as a toggle setting in the user profile page, so that by default we keep the current behaviour, and users like me can change it 😄 wdyt?

no, one worker (trains-agent-1) "forget from time to time" the current experiment he is running and picks another experiment on top of the one he is currently running

trains-agent-1: runs an experiment for a long time (>12h). Picks a new experiment on top of the long one running trains-agent-2: runs only one experiment at a time, normal trains-agent-3: runs only one experiment at a time, normalIn total: 4 experiments running for 3 agents

awesome! Unfortunately, calling artifact["foo"].get() gave me:
Could not retrieve a local copy of artifact foo, failed downloading file:///checkpoints/test_task/test_2.fgjeo3b9f5b44ca193a68011c62841bf/artifacts/foo/foo.json
It tries to get it from the local storage, but the json is stored in s3 (it does exists) and I did create both tasks specifying the correct output_uri (to s3)

Disclaimer: I didn't check this will reproduce the bug, but that's all the components that should reproduce it: a for loop creating figures and clearml logging them

from the ClearML UI

Thanks SuccessfulKoala55 😁

you mean to run it on the CI machine ?

yes

That should not happen, no? Maybe there is a bug that needs fixing on clearml-agent ?

It just to test that the logic being executed in if not Task.running_locally() is correct

and in the logs:
`
agent.worker_name = worker1
agent.force_git_ssh_protocol = false
agent.python_binary =
agent.package_manager.type = pip
agent.package_manager.pip_version = ==20.2.3
agent.package_manager.system_site_packages = true
agent.package_manager.force_upgrade = false
agent.package_manager.conda_channels.0 = pytorch
agent.package_manager.conda_channels.1 = conda-forge
agent.package_manager.conda_channels.2 = defaults
agent.package_manager.torch_nightly = false
agent.venvs_dir = /...

It failed as well

I’m not too fond of many user configurations, it’s confusing.

100% agree, nevertheless, how much is too many? Currently, there are only two settings in the user preferences category, so one more wouldn’t hurt?

however, clearml is open source, nothing stops you from adding the code and sending a PR

I’d be super happy to contribute yes! Nevertheless, I am not sure where to start: clearml-server repo? clearml-web repo?

In the comparison the problem will be the same, right? If I choose last/min/max values, it won’t tell me the corresponding values for others metrics. I could switch to graphs, group by metric and look manually for the corresponding values, but that becomes quickly cumbersome as the number of experiments compared grow

super, thanks SuccessfulKoala55 !

Yes AnxiousSeal95 , stopped instance meaning you don’t pay for it, but just its storage, as described https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Stop_Start.html . So AgitatedDove14 increasing the IDLE timeout would still make me pay for the instance while they are idle.

Do you get stopped instances instantely when you ask for them?

Well that’s a good question, that’s what I observed some time ago, but according to their https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/...

I am looking for a way to gracefully stop the task (clean up artifacts, shutdown backend service) on the agent

I tested by installing flask in the default env -> which was installed in the ~/.local/lib/python3.6/site-packages folder. Then I created a venv with flag --system-site-packages . I activated the venv and flask was indeed available

Could you please share the stacktrace?

I was able to fix by applying for a license and registering it

Oof now I cannot start the second controller in the services queue on the same second machine, it fails with
` Processing /tmp/build/80754af9/cffi_1605538068321/work
ERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: '/tmp/build/80754af9/cffi_1605538068321/work'
clearml_agent: ERROR: Could not install task requirements!
Command '['/home/machine/.clearml/venvs-builds.1.3/3.6/bin/python', '-m', 'pip', '--disable-pip-version-check', 'install', '-r'...

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

Thanks, I will create an issue. I am fine with both ways :)

Which commit corresponds to RC version? So far we tested with latest commit on master (9a7850b23d2b0e1f2098ab051de58ce806143fff)

I also would like to avoid any copy of these artifacts on s3 (to avoid double costs, since some folders might be big)

should I try to roll back to clearml-server 1.0.2? I am very anxious now…

Alright SuccessfulKoala55 I was able to make it work by downgrading clearml-agent to 0.17.2