AgitatedDove14 After investigation, another program on the machine consumed all the memory available, most likely making the OS killing the agent/task

Thanks!

What is weird is:
Executing the task from an agent: task.get_parameters() returns an empty dict Calling task.get_parameters() from a local standalone script returns the correct properties, as shown in web UI, even if I updated them in UI.So I guess the problem comes from trains-agent?

but if you do that and the package is already installed it will not install using the git repo, this is an issue with pip

Exactly, that’s my problem: I want to remove it to make sure it is reinstalled (because the version can change)

I think that since the agent installs everything from scratch it should work for you. Wdyt?

With env caching enabled, it won’t reinstall this private dependency, right?

Thanks @<1523701087100473344:profile|SuccessfulKoala55> ! Are alive workers sending ping to notify the server that they are alive or does the server infers that they are alive based on the last communication?

It worked with clearml server 0.17

What I put in the clearml.conf is the following:

agent.package_manager.pip_version = "==20.2.3" agent.package_manager.extra_index_url: [" "] agent.python_binary = python3.8

Yes, but a minor one. I would need to do more experiments to understand what is going on with pip skipping some packages but reinstalling others.

Yes this is correct. I am trying to create a minimal reproducable example

I can live with the current setup for now

Yes, I would like to update all references to the old bucket unfortunately… I think I’ll simply delete the old s3 bucket, wait or his name to be available again and recreate it where on the other aws account and move the data there. This way I don’t have to mess with clearml data - I am afraid to do something wrong and loose data

What is this cleanup service? where is it available?

Some context: I am trying to log an HTML file and I would like it to be easily accessible for preview

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

AgitatedDove14 Yes that might work, also the first one (with conda) might work as well, I will give it a try, thanks!

Otherwise I can try loading the file with custom loader, save as temp file, pass the temp file to connect_configuration, it will return me another temp file with overwritten config, and then pass this new file to OmegaConf

it should return the task regardless if it is complete or not

and this works. However, without the trick from UnevenDolphin73 , the following won’t work (return None):
if __name__ == "__main__": task = Task.current_task() task.connect(config) run() from clearml import Task Task.init()

UnevenDolphin73 , task = clearml.Task.get_task(clearml.config.get_remote_task_id()) worked, thanks

I think this is because this API is not available in elastic 5.6

To clarify: trains-agent run a single service Task only

I found, the filter actually has to be an iterable:
Task.get_tasks(project_name="my-project", task_name="my-task", task_filter=dict(type=["training"])))

Bottom line is: trains-server uses elastichsearch image: http://docker.elastic.co/elasticsearch/elasticsearch:5.6.16 which does not have an unlimited license (only free license that expires after some time). From versions 6.3, elasticsearch provides an unlimited free license. Trains should use >=6.3, WDYT?

ok, now I actually remember why I used _update_requirements instead of add_requirements: The first overwrites all the other, the later only add to the already detected packages. Since my deps are listed in the dependencies of my setup.py, I don't want clearml to list the dependencies of the current environment

but it is set here, right?

As to why: This is part of the piping that I described in a previous message: Task B requires an artifact from task A, so I pass the name of the artifact as a parameter of task B, so that B knows what artifact from A it should retrieve

AgitatedDove14 Is it fixed with trains-server 0.15.1?

Hi AgitatedDove14 , with 1.0.4rc1 it worked, thanks 😄

AgitatedDove14 I see https://github.com/allegroai/clearml-session/blob/main/clearml_session/interactive_session_task.py#L21= that a key pair is hardcoded in the repo. Is it being used to ssh to the instance?