Same for regexp, damn

So it seems like it doesn't copy /root/clearml.conf and it doesn't pass the environment variables (CLEARML_API_HOST, CLEARML_API_ACCESS_KEY, CLEARML_API_SECRET_KEY)

SuccessfulKoala55 I can try to make one, let’s see 🙂

So when I create a task using `task = Task.init(project_name=config.get("project_name"), task_name=config.get("task_name"), task_type=Task.TaskTypes.training, output_uri=" s3://my-bucket ") locally, the artifact is correctly logged remotely, but when I create the task remotely (from an agent) the artifact is logged locally (in the agent machine, not on s3)

Sure, where can I find this file?

yes -> but I still don't understand why the post_packages didn't work, could be worth investigating

I can live with the current setup for now

yes exactly

erf, I have the same problem with ProxyDictPreWrite 😄 What is the use case of this one ?

in the UI the value is correct one (not empty, a string)

This https://stackoverflow.com/questions/65109764/wildcard-search-issue-with-long-datatype-in-elasticsearch says long types can be converted to string to do the search

AgitatedDove14 Good news, I was able to reproduce the bug on the pytorch distributed sample 🤩
Here it is > https://github.com/H4dr1en/trains/commit/642c1130ad1f76db10ed9b8e1a4ff0fd7e45b3cc

How about the overhead of running the training on docker on a VM?

SuccessfulKoala55 I tried to setup in a different machine the clearml-agent and now I get a different error message in the logs:
Warning: could not locate requested Python version 3.6, reverting to version 3.6 clearml_agent: ERROR: Python executable with version '3.6' defined in configuration file, key 'agent.default_python', not found in path, tried: ('python3.6', 'python3', 'python')

This is new right? it detects the local package, uninstalls it and reinstalls it?

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

Ok, I could reproduce with Firefox and Chromium. Steps:
Add creds (either via the popup or in the settings) Go the /settings/webapp-configuration -> Creds should be there Hit F5 Creds are gone

CostlyOstrich36 How is clearml-session setting the ssh config?

Ok, but that means this cleanup code should live somewhere else than inside the task itself right? Otherwise it won't be executed since the task will be killed

AgitatedDove14 Unfortunately no, I already had the problem before using the function, I added it hoping it would fix the issue but it didn’t

Ok, good to know, thanks!

I am happy if I can be of any help to fix that 😄

self.clearml_task.get_initial_iteration() also gives me the correct number

Ho wow! is it possible to not specify a remote task? (If i am working with Task.set_offline(True))

I don’t have a registry to push my image to.I think I can get around it actually - Will it work if I just build the image locally once, then start the agent? Docker would recognise that image locally and just use it right? I won’t need to update that image often anyway

same as the first one described

I don’t think it is, I was rather wondering how you handled it to understand potential sources of slow down in the training code

I am actually calling later in the start_training function the following:
with idist.Parallel(backend="nccl") as parallel: parallel.run(training_func)So my backend should be nccl and not gloo , right? Not sure how important it is, I read in the https://pytorch.org/docs/stable/distributed.html#which-backend-to-use that nccl should be used for distributed GPU training and gloo for distributed CPU training
I will try with clearml==1.1.5rc2

Yes, I set:
auth { cookies { httponly: true secure: true domain: ".clearml.xyz.com" max_age: 99999999999 } }It always worked for me this way

Ok, so what worked for me in the end was:
config = task.connect_configuration(read_yaml(conf_path)) cfg = OmegaConf.create(config._to_dict())