not really, because it is in the middle of the controller task, there are other things to be done afterwards (retrieving results, logging new artifacts, creating new tasks, etc)

awesome 🎉
Maybe then we can extend task.upload_artifact ?
def upload_artifact(..., wait_for_upload: bool = False): ... if wait_for_upload: self.flush(wait_for_uploads=True)

So I cannot ssh anymore to the agent after starting clearml-session on it

So I created a symlink in /opt/train/data -> /data

Update: I successfully isolated one of the reason, mem leak in matplotib itself, I opened an issue on their repo here

So either I specify in the clearml-agent agent.python_binary: python3.8 as you suggested, or I enforce the task locally to run with python3.8 using task.data.script.binary

I don't know why it didn't detect it in first place

I assume you’re using a self-hosted server?

Yes

Ho nice, thanks for pointing this out!

Sorry, what I meant is that it is not documented anywhere that the agent should run in docker mode, hence my confusion

This is HUGE 🔥 🚀 🎉

ok, thanks SuccessfulKoala55 !

This is the issue, I will make sure wait_for_status() calls reload at the ends, so when the function returns you have the updated object

That sounds awesome! It will definitely fix my problem 🙂

In the meantime: I now do:
task.wait_for_status() task._artifacts_manager.flush() task.artifacts["output"].get()But I still get KeyError: 'output' ... Was that normal? Will it work if I replace the second line with task.refresh () ?

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

So it is there already, but commented out, any reason why?

Thanks AgitatedDove14 !
Could we add this task.refresh() on the docs? Might be helpful for other users as well 🙂 OK! Maybe there is a middle ground: For artifacts already registered, returns simply the entry and for artifacts not existing, contact server to retrieve them

Ok so what is the value that is set when it is run by the agent? agg ?

Thanks AgitatedDove14 ! I created a project with a default output destination to a s3 bucket but I don't have local access to this bucket (only agents have access to it for security reasons). Because of that, I cannot create a task in this project programmatically locally because it tries to access the bucket and fails. And there is no easy way to change the default output location (not in the web UI, not in the sdk)

OK but nowhere I specified that, I just checked my trains.conf file

thanks, I will do that

yes, the only thing I changed is:
install_requires=[ ... "my-dep @ git+ ]to:
install_requires=[ ... "git+ "]

torch==1.7.1 git+ .

I am already trying with latest of pip 😞

Hey SuccessfulKoala55 , unfortunately this doesn’t work, because the dict contains others dicts, and only the first level dict becomes a dict, the inner dicts still are ProxyDictPostWrite and will make OmegaConf.create fail

This is consistent: Each time I send a new task on the default queue, if trains-agent-1 has only one task running (the long one), it will pick another one. If I add one more experiment in the queue at that point (trains-agent-1 running two experiments at the same time), that experiment will stay in queue (trains-agent-2 and trains-agent-3 will not pick it because they also are running experiments)

line 13 is empty 🤔

No space, I will add and test 🙂

hooo now I understand, thanks for clarifying AgitatedDove14 !

Hi CostlyOstrich36 , I am not using Hydra, only OmegaConf, so you mean just calling OmegaConf.load should be enough?

No 🙂