Thanks IntriguedRat44 !
I'll follow up on GitHub 🙂

Awesome, PRs are always welcome, and we try to help with any request and feature coming for users. We just added audio support (RC releasing in a few days) based only on users request.
https://github.com/allegroai/trains/issues/120

SourLion48 you mean the wraparound ?
https://github.com/allegroai/clearml/blob/168074acd97589df58436a3ec122a95a077620c2/docs/clearml.conf#L33

data it is going to s3 as well as ebs. Why so it should only go to s3

This sounds odd, if this is mounted then it goes to the S3 (the link will point to the files server, but it will be stored on the mounted drive i.e. S3)
wdyt?

can I mount the s3 bucket as file system on place where

you need to mount it where the file server is storing it's files, correct (notice, not the DBs, just the files server)

Hmmm, that actually connects with something we were thinking about: introducing sections to the hyper parameters. This way we could easily differentiate between the command line arguments and other types of parameters. DilapidatedDucks58 what do you think?

It's dead simple to install:
Pip install trains-agent
the.n you can simply do:
Trains-agent execute --id myexperimentid

I see... We could definitely add an argument to control it. I'll update here once there is an RC

BTW copying the cmd line assumes that you are running it in the same machine...

:)

Is this the case?

AstonishingSeaturtle47 I think there's a workaround for the GitHub multiple repo issue. See https://gist.github.com/gubatron/d96594d982c5043be6d4

AstonishingSeaturtle47 that's awesome! Could you explain the hack, it might be helpful for others (I assume :))

Hmmm, I'm not sure that you can disable it. But I think you are correct it should be possible. We will add it as another argument to Task.init. That said, FriendlyKoala70 what's the use case for disabling the code detection? You don't have to use it later, but it is always nice to know :)

The idea is that it is not necessary, using the trains-agent you can not only launch the experiment on a remote machine, you can override the parameters, not just cmd line arguments, but any dictionary you connected with the Task or configuration...

DilapidatedDucks58 if you have so many parameters, why don't you use the
task.connect_configuration(dict)
It will put it in the artifacts, as an editable json alike string.

You can always access the entire experiment data from python
'Task.get_task(Id).data'
It should all be there.
What's the exact use case you had in mind?

With offline mode,
Later if you need you can actually import the execution (including artifacts etc.) you just need the zip file it creates when you are done.

AstonishingSeaturtle47 yes it does. But I have to ask how come you have sub modules that one will have credentials for the master repo and not the sub ones? Also it sounds like a good solution would be for the trains-agent to try and pull the sub-modules and if it cannot, it should just print a warning and continue. What do you think?

AstonishingSeaturtle47 How would the code run without the sub-modules? And what is the problem we are trying to solve? (Because unfortunately there is no switch to disable it)

FYI all the git pulls are cached even in docker mode so there is no "tax" to pay for pulling the sub-modules (only the first time of course)

Trains is fully open-source, that said properly publishing and maintaining the web client is still on our to do list (I mean there is totally readable JavaScript code packaged in the trains-server and the dockers). It is constantly pushed because there is generally less contributions on the front-end with these kind of projects. That said of you guys are willing to help, it will greatly help in pushing it forward... LivelyLion31 what do you think, would you guys like to help with the fronte...

Hi CloudySwallow27

Is there a way to still use the auto_connect but limit the amount of debug imgs?

Basically you can set the number of image it will store for you (per title/series combination)m the way it works it rotates the image names so essentially overriding old images (the UI is ware and will only show the last X of them)
See here on setting it:
https://github.com/allegroai/clearml/blob/81de18dbce08229834d9bb0676446a151046e6a7/docs/clearml.conf#L32

Hi @<1624579015031394304:profile|JitterySeal56>

... and credentials in clearml.conf file on client side, but I have restrictions of aws keys expiring each hour

This means that you need to configure IAM role on your client machine, the data never goes through the server it is uploaded directly from the dev machine to the S3 bucket.

You can however just store the data on your clearml-files server ...

I have mounted my s3 bucket at the location /opt/clearml/data/fileserver/ but I can see my data is not being stored in s3 but its storing in ebs. How so?

I'm assuming the mount was not successful
What you should see is a link to the files server inside clearml, and actual files in your S3 bucket

Check the links that are generated in the ui when you upload an artifact or model

woot woot 🙂

EnviousStarfish54

and the 8 charts are actually identical

Are you plotting the same plot 8 times?

I think that what you need is the triggers, check this one:
https://clear.ml/docs/latest/docs/references/sdk/trigger

Hi @<1523701601770934272:profile|GiganticMole91>

to use https although the scheduled task is using ssh for git?

Sure as long as it has git_user / git_pass configured in the agents clearml.conf it will automatically convert ssh to http git pull
None