No worries! I asked more to be informed, I don't have a real use-case behind. This means that you guys internally catch the argparser object somehow right? Because you could also simply use sys argv to find the parameters, right?

Hi AgitatedDove14 , How should we proceed to fix this bug? Should I open an issue in github? Should I try to make a minimal reproducible example? It’s blocking me atm

-> seems to run properly now

PS: in the new env, I’v set num_replicas: 0, so I’m only talking about primary shards…

ha nice thanks 🙏

Yea, the config is not appearing in the webUI anymore with this method 😞

No I agree, it’s probably not worth it

clearml 1.1.1 works

ClearML has a task.set_initial_iteration , I used it as such:
checkpoint = torch.load(checkpoint_fp, map_location="cuda:0") Checkpoint.load_objects(to_load=self.to_save, checkpoint=checkpoint) task.set_initial_iteration(engine.state.iteration)But still the same issue, I am not sure whether I use it correctly and if it’s a bug or not, AgitatedDove14 ? (I am using clearml 1.0.4rc1, clearml-agent 1.0.0)

Yes that’s correct - the weird thing is that the error shows the right detected region

and the agent says agent.cudnn_version = 0

And I do that each time I want to create a subtask. This way I am sure to retrieve the task if it already exists

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

yes

Is there any channel where we can see when new self hosted server version are published?

See my answer in the issue - I am not using docker

thanks Jake, good to know!

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)

on a p3.2xlarge instance

venv mode

(I am not part of the awesome ClearML team, just a happy user 🙂 )

AppetizingMouse58 btw I had to delete the old logs index before creating the alias, otherwise ES won’t let me create an alias with the same name as an existing index

AgitatedDove14 I see that the default sample_frequency_per_sec=2. , but in the UI, I see that there isn’t such resolution (ie. it logs every ~120 iterations, corresponding to ~30 secs.) What is the difference with report_frequency_sec=30. ?

Hi SuccessfulKoala55 , not really wrong, rather I don't understand it, the docker image with the args after it

Just found yea, very cool! Thanks!

What do you mean by aws scalar?

So the wheel that was working for me was this one: [torch-1.11.0+cu115-cp38-cp38-linux_x86_64.whl](https://download.pytorch.org/whl/cu115/torch-1.11.0%2Bcu115-cp38-cp38-linux_x86_64.whl)

but then why do I have to do task.connect_configuration(read_yaml(conf_path))._to_dict() ?
Why not task.connect_configuration(read_yaml(conf_path)) simply?
I mean what is the benefit of returning ProxyDictPostWrite instead of a dict?

I see what I described in https://allegroai-trains.slack.com/archives/CTK20V944/p1598522409118300?thread_ts=1598521225.117200&cid=CTK20V944 :
randomly, one of the two experiments is shown for that agent

Still the same problem 😞