To be fully transparent, I did a manual reindexing of the whole ES DB one year ago after it run out of space, at that point I might have changed the mapping to strict, but I am not sure. Could you please confirm that the mapping is correct?

super, thanks!

Does what you suggested here > https://github.com/allegroai/trains-agent/issues/18#issuecomment-634551232 also applies for containers used by the services queue?

AgitatedDove14 I eventually found a different way of achieving what I needed

Here is the minimal reproducable example.
Run test_task_a.py - It will register a dummy artifact, create a new task, set a parameter in that task and enqueue it test_task_b will try to retrieve parameter from parent task and fail

I have a custom way of reading the config file

RobustRat47 It can also simply be that the instance type you declared is not available in the zone you defined

Ok so the problem was indeed the way docker was installed (with snap)

I’d like to move to a setup where I don’t need these tricks

SuccessfulKoala55 They do have the right filepath, eg:
https://***.com:8081/my-project-name/experiment_name.b1fd9df5f4d7488f96d928e9a3ab7ad4/metrics/metric_name/predictions/sample_00000001.png

But that was too complicated, I found an easier approach

Ok AgitatedDove14 SuccessfulKoala55 I made some progress in my investigation:
I can exactly pinpoint the change that introduced the bug, it is the one changing the endpoint "events.get_task_log", min_version="2.9"
In the firefox console > Network, I can edit an events.get_task_log and change the URL from …/api/v2.9/events.get_task_log to …/api/v2.8/events.get_task_log (to use the endpoint "events.get_task_log", min_version="1.7" ) and then all the logs are ...

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

yes, something like that

Hi @<1523701205467926528:profile|AgitatedDove14> @<1537605940121964544:profile|EnthusiasticShrimp49> , the issue above seemed to be the memory leak and it looks like there is no problem from clearml side.
I trained successfully without mem leak with num_workers=0 and I am now testing with num_workers=8.
Sorry for the false positive :man-bowing:

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

even if I move the Github workers internally where they could have access to the prod server, I am not sure I would like that, because it would pile up test data in the prod server that is not necessary

I can ssh into the agent and:
source /trains-agent-venv/bin/activate (trains_agent_venv) pip show pyjwt Version: 1.7.1

with the CLI, on a conda env located in /data

🚀 Thanks @<1523701205467926528:profile|AgitatedDove14> !

AgitatedDove14 I now tested with a real experiment, it works, but I saw two issues:
It first doesnt detect torch, downloads it but then says that it is already installed so it doesn't install it. One of the dependency of my repository is another repository (repo-2 in the logs). Both my repositories require numpy . When installing the first repository, it says Requirement already satisfied: numpy in /home/workeruser/.local/lib/python3.6/site-packages . Correct. But then it says `...

Hi @<1523701087100473344:profile|SuccessfulKoala55> I was able to find the issue, I was creating a queue and worker subprocess that were not properly cleaned up

For the moment this is what I would be inclined to believe

Sure 🙂 Opened https://github.com/allegroai/clearml/issues/568

Still investigating, task.data.last_iteration is correct (equal to engine.state["iteration"] ) when I resume the training

I am not using hydra, I am reading the conf with:
config_dict = read_yaml(conf_yaml_path) config = OmegaConf.create(task.connect_configuration(config_dict))

ha nice, where can I find the mapping template of the original clearml so that I can copy and adapt?

Also I can simply delete the /elastic_7 folder, I don’t use it anymore (I have a remote ES cluster). In that case, I guess I would have enough space?

Sorry, I refreshed the page and it’s gone 😅