I think the main issue is running with python -m module.name --args
Which is a bit different, when trying to "understand" what is the actual repository.
Can you try to run it from the repository folder (same command, just to see if it will have any effect on the detected packages)

BTW: how is it missing listing torch ? Do you have "import torch" in the code ?

👍

but I don't see any change...where is the link to the file removed from

In the meta data section, check the artifacts "state" object

How are these two datasets different?

Like comparing two experiments :)

SweetGiraffe8
That might be it, could you test with the Demo server ?

NICE! MagnificentSeaurchin79 could you PR this fix?

Thanks!
I think this one will cover both case (the issue is with files on the root of the dataset)
if not (fnmatch(k, path) and fnmatch(k if '/' in k else '/{}'.format(k), '*/' + wildcard))}

Please go ahead with the PR 🙂

@<1547390422483996672:profile|StaleElk72> when you go to the dataset in the UI, and press on "Full Details" then go to the Artifacts tab, what is the link you see there?

In the UI you can see all the agents and their IDs
Then you can so

clearml-agent daemon --stop <agent id>

actually no

hmm, are those packages correct ?

that must have been it. here’s the installed packages when not using 

-m

:

Hmm yes, can you open a GitHub issue on that? (this seems like a bug)

clearml-agent daemon --detached --queue manual_jobs automated_jobs --docker --gpus 0

If the user running this command can run "docker run", then you should ne fine

It seems to try to p[ull with SSH credentials, add your user/pass(or better APIkey) to the clearml.conf
(look for git_user /git_pass)
Should solve the issue

YEY!

Hi BurlyRaccoon64
What do you mean by "custom_build_script" ? not sure I found it in "clearml,conf"
https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf

We do upload the final model manually.

If this is the case just name it based on the parameters, no? am I missing soemthing?
https://github.com/allegroai/clearml/blob/cf7361e134554f4effd939ca67e8ecb2345bebff/clearml/model.py#L1229

I was just wondering if i can make the autologging usable.

It kind of assumes these are different "checkpoints" on the same experiment, and then stores them based on the file name
You can however change the model names later:
` Task.current_task().mo...

Is that normal or a possible bug?

This sounds like xgboost internal format, it makes sense to me to be joblib (which is like pickle only faster and safer)
Let me see if we can also add the model object to the callback...

Hi MotionlessSeagull22
Hmm I'm not this is possible in the UI.
You can compare multiple experiments and view the images in form of thumbnails one next to the other, But full view will be a single image...
You can however right click on the image and get a direct link, then open a new tab ... :(

E.g. I might need to have different N-numbers for the local and remote (ClearML) storage.

Hmm yes, that makes sense

That'd be a great solution, thanks! I'll create a PR shortly

Thank you! 🙏 🤩

if in the "installed packages" I have all the packages installed from the requirements.txt than I guess I can clone it and use "installed packages"

After the agent finished installing the "requirements.txt" it will put back the entire "pip freeze" into the "installed packages", this means that later we will be able to fully reproduce the working environment, even if packages change (which will eventually happen as we cannot expect everyone to constantly freeze versions)

My problem...

LovelyHamster1 from the top, we have two steps:
We run the code "manually" (i.e. without the agent) this step create the experiment (Task) and automatically feels in the "installed packages" (which are in the same format as regular requirements.txt) An agent is running a cloned copy of the experiment (Task). The agents creates a new venv on the agent's machine, then the agent is using the "Installed packages" section as a replacement to regular "requirements.txt" and installs everything fro...

@<1571308003204796416:profile|HollowPeacock58> seems like an internal issue copying this object config.model
This is a complex object, and it seems that for some reason
None

As a workaround just do not connect this object. it seems you cannot pickle it / copy it (see GH issue)

Make sure you have the S3 credentials in your agent's clearml.conf :
https://github.com/allegroai/clearml-agent/blob/822984301889327ae1a703ffdc56470ad006a951/docs/clearml.conf#L210

Hmm okay let me check that, I think I understand the issue

the SDK is unable to see each of the nodes?

Exactly ! I mean I love the idea of "nested" component, but implementation wise this is not trivial, it will also hurt the ability of caching individual component. The workaround is to have all the "business logic" in the pipeline function itself, routing data between components is basically "free". The data does not actually go through the pipeline logic, it only passes reference (unless the pipeline logic actually tries to access the data o...

Hi @<1555362936292118528:profile|AdventurousElephant3>
I think your issue is that Task supports two types of code,

• single script/jupyter notebook
• git repo + git diffIn your example (If I understand correctly) you have a notebook calling another notebook, which means the first notebook will be stored on the Task, but the second notebook (not being part of a repository) will not be stored on the task, and this is why when the agent is running the code it fails to find the second notebook....

are you planning on changing to f-strings incrementally?

There is still py 2.7 & 3.5 support...
Hopefully we will be able to drop both (apparently enough users have legacy code), then we will probably switch to the nicer f' strings 🙂

You are doing great 🙂 don't worry about it

And the agent continue running.

oh just kill al the processes with clearml-agent in the cmd line

pkill -9 -f clearml-agent