The task is created using Task.clone() yes

yes

very cool, good to know, thanks SuccessfulKoala55 🙂

Hi SuccessfulKoala55 , super that’s what I was looking for

Indeed, I actually had the old configuration that was not JSON - I converted to json, now works 🙂

Very nice! Maybe we could have this option as a toggle setting in the user profile page, so that by default we keep the current behaviour, and users like me can change it 😄 wdyt?

no it doesn't! 3. They select any point that is an improvement over time

Thanks!3. I don't know, I never used Highcharts 🙂

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))

But I am not sure it will connect the parameters properly, I will check now

Doing it the other way around works:
` cfg = OmegaConf.create(read_yaml(conf_yaml_path))
config = task.connect(cfg)
type(config)

<class 'omegaconf.dictconfig.DictConfig'> `

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?

Same, it also returns a ProxyDictPostWrite , which is not supported by OmegaConf.create

I mean, inside a parent, do not show the project [parent] if there is nothing inside

Because it lives behind a VPN and github workers don’t have access to it

Some more context: the second experiment finished and now, in the UI, in workers&queues tab, I see randomly
trains-agent-1 | - | - | - | ... (refresh page) trains-agent-1 | long-experiment | 12h | 72000 |

Why is it required in the case where boto3 can figure them out itself within the ec2 instance?

Add carriage return flush support using the sdk.development.worker.console_cr_flush_period configuration setting (GitHub trains Issue 181)

I am doing so

Nevermind, i was able to make it work, but no idea how

with 1.1.1 I get
User aborted: stopping task (3)

clearml 1.1.1 works

no, one worker (trains-agent-1) "forget from time to time" the current experiment he is running and picks another experiment on top of the one he is currently running

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?

Does the agent install the nvidia-container toolkit, so that GPUs of the instance can be accessed from inside the docker running jupyterlab?

Is there a typo in your message? I don't see the difference between what I wrote and what you suggested: TRAINS_WORKER_NAME = "trains-agent":$DYNAMIC_INSTANCE_ID

So this message appears when I try to ssh directly into the instance

There is no need to add creds on the machine, since the EC2 instance has an attached IAM profile that grants access to s3. Boto3 is able retrieve the files from the s3 bucket

You are right, thanks! I was trying to move /opt/trains/data to an external disk, mounted at /data

Yes, but I am not certain how: I just deleted the /data folder and restarted the server