Do we have it on the git issue ?

Could you please add it, I really do not want to miss it 🙂

ReassuredTiger98 when you look for task "dca2e3ded7fc4c28b342f912395ab9bc" there are no artifacts ?
Could you add some prints? this should have worked...

ReassuredTiger98 both are running with pip as package manager, I thought you mentioned conda as package manager, no?
agent.package_manager.type = pipAlso the failed execution is looking for "ruamel_yaml_conda" but it is nowhere to be found on the original one?! how is that possible ?

As we use a custom CUDA image, we do not want this running on user login, and get ugly error messages about missing symlinks.

You can customize the startup bash script (running inside Any container) here:
https://github.com/allegroai/clearml-agent/blob/bf07b7f76d3236c1118b81730c6d9718705a795a/docs/clearml.conf#L145
LackadaisicalOtter14 Would that help?

CooperativeFox72 a bit of info on how it works:
In "manual" execution (i.e. without an agent)

path = task.connect_configuration(local_path, name=name

path = local_path , and the content of local_path is stored on the Task

In "remote" execution (i.e. agent)

path = task.connect_configuration(local_path, name=name

"local_path" is ignored, path is a temp file, and the content of the temp file is the content that is stored (or edited) on the Task configuration.
Make sense ?

So if I will edit the temp file

you mean from the UI or code?

Just making sure I understand, basically same ArgParser support we already have, but for python-fire (which is the ability to automatically log the arguments, and then change them when executed by trains-agent), correct?
If this is the case, are you familiar with the implementation of python-fire ? What I'm looking for is where exactly the parsing happens, so we could patch it, and log/override values

VexedCat68

a Dataset is published, that activates a Dataset trigger. So if every day I publish one dataset, I activate a Dataset Trigger that day once it's published.

From this description it sounds like you created a trigger cycle, am I missing something ?
Basically you can break the cycle by saying, trigger only on New Dataset with a specific Tag (or create the auto dataset in a different project/sub-project).
This will stop your automatic dataset creation from triggering the "orig...

hmm... try to run the trains-agent from the ml environment with "system_site_packages: true", it might do the trick. Anyhow please let me know if it worked 🙂

does this work for multiple levels?

Yep 😄

Hi SlipperyDove40
plotly is about 4Mb... trains about 0.5MB what'd the breakdown of the packages ? This seems far away from 250Mb limit

but when the dependencies are installed, the git creds are not taken in account

I have to admit, we missed that use case 😞
A quick fix will be to use git ssh, which is system wide.

but I want know to switch to git auth using Personal Access Token for security reasons)

Smart move 😉

As for the git repo credentials, we can always add them, when you are using user/pass. I guess that would be the behavior you are expecting, unless the domain is different......

I thought this is the issue on the thread you linked, did I miss something ?

mostly out of curiosity, what is the motivation behind introducing this as an environment variable knob rather then a flag with some default in Task.init?

DepressedChimpanzee34 we will deprecate the demo server (not exactly sure when) as we have the free community one that gives better service and stores the data. It was originally set for easy on-boarding and testing, but I think that now the user experience might be better with using the community free tier.
Make sense ? btw: what ...

well.. having the demo server by default lowers the effort threshold for trying ClearML and getting convinced it can deliver what it promises, and maybe test some simple custom use cases. I

This was exactly what we thought when we set it up in the first place 🙂
(I can't imagine the cost is an issue, probably maintenance/upgrades ...)
There is still support for the demo server, you just need to set the env key:
CLEARML_NO_DEFAULT_SERVER=0 python ...

Done!

Thanks

fatal: unable to find a suitable socket path; use --socket

 )

Look here https://stackoverflow.com/questions/5015732/why-do-i-get-unable-to-connect-a-socket-when-i-try-to-clone-via-a-git-url

I think that's the root cause, we should probably also add https://github.com/allegroai/trains-agent/issues/16

DepressedChimpanzee34 <character> will almost always be converted into \ because otherwise it will not support \t or \n etc.
What I'm looking here is some logic that will allow us not to break backwards compatibility on the one hand, but still will allow you to have something like "first\second" entry.
WDYT? any ideas? (I really want to make sure we fix it as soon as possible)

BTW:
str('\.') Out[4]: '\\.' str(('\.', )) Out[5]: "('\\\\.',)"This is just python str casting

DepressedChimpanzee34
so parsing bask is done via a yaml reader:
https://github.com/allegroai/clearml/blob/49fcbd7bbf3236f4175cdff29fa951847b0923cc/clearml/backend_interface/task/args.py#L506
We could add extra test here, checking for \ in the string, that should solve it and will be backwards compatible (I think)
https://github.com/allegroai/clearml/blob/49fcbd7bbf3236f4175cdff29fa951847b0923cc/clearml/backend_interface/task/task.py#L935

DepressedChimpanzee34 any string serialization package I tried will convert r"some\blah" into "some\\blah" (json yaml hocon) otherwise you end up with \b as an escape character. I'm really not sure what to do here. (And reinventing the standard seems unhealthy)

Ohh so even easier:
print(client.workers.get_all())

Hi VirtuousFish83
Apologies for the documentation in the docs 🙂 It sounds complicated but actually should be relatively simple. Based on what I understand, you already have the server setup and you code integrated. The question is "can you see an experiment in the UI"? If you do, then you can right click it, clone the experiment , edit parameters and send for execution (enqueue). If the experiment is not in the UI you can either (1) run the code with the Task.init call, it ill automatica...

Ohh, two options:
From the script itself you can do:
from clearml import Task task = Task.init(...) task.execute_remotely(queue='default')Then run the script locally, it will get until the "execute_remotely call, quit the process and re-launch it on the "default" queue.
Option B:
Use the cleaml-task
$ clearml-task --folder <where the script is> --project ...See https://github.com/allegroai/clearml/blob/master/docs/clearml-task.md#launching-a-job-from-a-local-script

Awesome 🙂

Could it be the code is not in a git repository ?
clearml support either a single script or a git repository, but Not a collection of standalone files. wdyt?

VirtuousFish83
Hmm that is odd, could you send the full log?

Okay that actually makes sense, let me check I think I know what's going on

btw: any specific reason to call current_task after you closed the main Task ?

Verified, and already fixed with 1.0.6rc2