I am not sure I can do both operations at the same time (migration + splitting), do you think it’s better to do splitting first or migration first?

So that I don’t loose what I worked on when stopping the session, and if I need to, I can ssh to the machine and directly access the content inside the user folder

automatically promote models to be served from within clearml

Yes!

AgitatedDove14 That's a good point: The experiment failing with this error does show the correct aws key:
... sdk.aws.s3.key = ***** sdk.aws.s3.region = ...

even if I explicitely use previous_task.output_uri = " s3://my_bucket " , it is ignored and still saves the json file locally

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

Ok, so after updating to trains==0.16.2rc0, my problem is different: when I clone a task, update its script and enqueue it, it does not have any Hyper-parameters/argv section in the UI

` ssh my-instance
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@ WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED! @
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
IT IS POSSIBLE THAT SOMEONE IS DOING SOMETHING NASTY!
Someone could be eavesdropping on you right now (man-in-the-middle attack)!
It is also possible that a host key has just been changed.
The fingerprint for the ED25519 key sent by the remote host is
SHA256:O2++ST5lAGVoredT1hqlAyTowgNwlnNRJrwE8cbM...

in my clearml.conf, I only have:
sdk.aws.s3.region = eu-central-1 sdk.aws.s3.use_credentials_chain = true agent.package_manager.pip_version = "==20.2.3"

Also, this is maybe a separate issue but could be linked, if I add Task.current_task().get_logger().flush(wait=True) like this:
` def log_loss(engine):
idist.barrier()
device = idist.device()
print("IDIST", device)
from clearml import Task
Task.current_task().get_logger().report_text(f"{device}, FIRED, {engine.state.iteration}, {engine.state.metrics}")
Task.current_task().get_logger().report_scalar("train", "loss", engine.state.metrics["loss"], engine.state.itera...

The task requires this service, so the task starts it on the machine - Then I want to make sure the service is closed by the task upon completion/failure/abortion

under /opt/clearml/config/apiserver.conf

Well not really

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"])))

yes

The task with id a445e40b53c5417da1a6489aad616fee is not aborted and is still running

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

no, at least not in clearml-server version 1.1.1-135 • 1.1.1 • 2.14

AppetizingMouse58 After some thoughts, we decided to install from scratch 0.16, with no data migration, because we believe this was an edge case not worth spending efforts on. Thank you very much for your help there, very appreciated. You guys rock! 🙂

no, I think I could reproduce with multiple queues

sure, will be happy to debug that 🙂

here it is

So the migration from one server to another + adding new accounts with password worked, thanks for your help!

Make sure the cloned task is in Draft mode, if not, reset it
Then in the Execution tab of th task, in the Source Code section (first one), you can edit the values

AgitatedDove14 Yes with the command you shared I can now ssh again manually to the agent, but I still clearml-agent will raise the same error

So it looks like the agent, from time to time thinks it is not running an experiment

hooo now I understand, thanks for clarifying AgitatedDove14 !

Stopping the server Editing the docker-compose.yml file, adding the logging section to all services Restarting the serverDocker-compose freed 10Go of logs

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.

AgitatedDove14 So I copied pasted locally the https://github.com/pytorch-ignite/examples/blob/main/tutorials/intermediate/cifar10-distributed.py from the examples of pytorch-ignite. Then I added a requirements.txt and called clearml-task to run it on one of my agents. I adapted a bit the script (removed python-fire since it’s not yet supported by clearml).