https://github.com/allegroai/clearml-agent.git@f019905720529acbd316bd39b67c5ab0c02fcd55 to be exact

the first problem I had, that didn’t gave useful infos, was that docker was not installed in the agent machine x)

Well, as long as you’re using a single node, it should indeed alleviate the shard disk size limit, but I’m not sure ES will handle that too well. In any case, you can’t change that for existing indices, you can modify the mapping template and reindex the existing index (you’ll need to index to another name, delete the original and create an alias to the original name as the new index can’t be renamed...)

Ok thanks!

Well, as long as you use a single node, multiple shards offer no sca...

the instances takes so much time to start, like 5 mins

meaning the RestAPI returns nothing, is that correct

Yes exactly, this is the response from the api server when I try to scroll down on the console to get more logs

This is no coincidence - Any data versioning tool you will find are somehow close to how git works (dvc, etc.) since they aim to solve a similar problem. In the end, datasets are just files.
Where clearml-data stands out imo is the straightfoward CLI combined with the Pythonic API that allows you to register/retrieve datasets very easily

So it looks like it tries to register a batch of 500 documents

I came up with the same code, thanks for the fast answer (yes having a setter for that would be cool!)

Nevermind, i was able to make it work, but no idea how

Yes, it works now! Yay!

thanks for your help!

Alright, I will try with that one

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 !