I've tracked down our messages when this occurred and I think we had a different error to you, sorry.

In case it helps our problem was when the below command was run in the repository:
$ git remote -vReturned the https address rather than the ssh address.
Then clearml tried to convert this to the ssh address, which looked like
<org>/<repo>/rather than
:<org>/<repo>.git(Which is possible a separate bug?)

Amazing, task.__dict__ is just what I was looking for!

Could well be the same as https://github.com/allegroai/clearml-server/issues/112 which is also discussed https://clearml.slack.com/archives/CTK20V944/p1648547056095859 🙂

FrothyDog40 https://github.com/allegroai/clearml-docs/pull/235

Yep can do 👍

CostlyOstrich36 thanks for getting back to me!

yes!

That's great! Please can you let me know how to do it/how to set the default files server?

However it would be advisable to also add the following argument to your code :

That's useful thanks, I didn't know about this kwarg

I ran into something similar, for me I'd actually cloned the repository using the address without the git@ (something made it work). ClearML read it from the remote repository URL and used it. When I updated the URL of the remote repository in my git client it then worked.

Yes please that would be great 👍

Updating the server has solved the issue 👍

Thank you 🙂

Yeah it's strange isn't it!

connect_configuration seems to take about the same amount of time unfortunately!

That said, maybe the connect dict is not the best solution for thousand key dictionary

Seems like it isn't haha!
What is the difference with connect_configuration ? The nice thing about it not being an artifact is that we can use the gui to see which hashes have changed (which admittedly when there are a few thousand is tricky anyway)

Another option would be to do
task.close() task.reset()And then execute an agent to pick up that task, but I don’t think reset is part of the public API. Is this risky?

From my limited understanding of it, I think it's the client that does the saving and communicating to the fileserver not the server, whereas deletion is done by the GUI/server which I guess could have different permissions somehow?

And regarding the first question - Edit your

~/clearml.conf

That would change what file server is used by me locally or an agent yes, but I want to change what is shown by the GUI so that would need to be a setting on the server itself?

And here is a PR for the other part.

task.get_parameters and task.get_parameters_as_dict have the keyword argument cast which attempts to convert values back to their original type, but interestingly doesn't seem to work for properties:

` task = Task.init()
task.set_user_properties(x=5)
task.connect({"a":5})
task.get_parameters_as_dict(cast=True)

{'General': {'a': 5}, 'properties': {'x': '5'}} Hopefully would be a relatively easy extension of get_user_properties ` !

And what is the difference in behaviour between
Task.init(..., output_uri=True) and Task.init(..., output_uri=None) ?

Hi CostlyOstrich36 thanks for the response and makes sense.

What sort of problems could happen, would it just be the corruption of the data that is being written or could it be more breaking?

For context, I’m currently backing up the server (spinning it down) every night but now need to run tasks over night and don’t want to have any missed logs/artifacts when the server is shutdown.

Hi CostlyOstrich36 , thanks for getting back to me!

I want to launch multiple tasks from one python process to be run by multiple agents simultaneously.

My current process for launching one task remotely is to use task.execute_remotely , and then I separately spin up a VM and execute a ClearML agent on that VM with the task ID.

Ideally, I would like to create multiple tasks in this way - so do Task.init(…) , set up some configuration, and then task.execute_remotely in a l...

I think a note about the fileserver should be added to the https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_security page!

it might be an issue in the UI due to this unconventional address or network settings

I think this is related to an https://github.com/allegroai/clearml-server/issues/112#issue-1149080358 that seems to be a reoccurring issue across many different setups

Maybe it was the load on the server? meaning dealing with multiple requests at the same time delayed the requests?!

Possibly but I think the server was fine as I could run the same task locally and it took a few seconds (rather than 75) to upload. The egress limit on the agent was 32 Gbps which seems much larger than what I though I was sending but I don't have a good idea of what that limit actually means in practice!

Will do! What’s the process for adding task.reset to the public API, just adding it to the docs?

Ah apologies for getting the wrong end of the stick a bit!

Not sure if it helps you or not, but when the link to an artifact didn't work for me it was because the URL being used was internal to the server (I had an agent that had access to internal endpoints). In my case setting the agent fileserver url to the public domain solved my issue.

It seems to be an issue that a few people are having problems with: https://github.com/allegroai/clearml-server/issues/112

Thanks @<1523701087100473344:profile|SuccessfulKoala55> , I’ve taken a look and is this force merging you’re referring to? Do you know how often ES is configured to merge in clearml server?

Shards that I can see are using a lot of disk space are

• events-training_stats_scalar
• events-log
• And then various worker_stats_*

That's great, thank you very much. Will give it a whirl today or tomorrow