How or why is this the issue? I great something is getting lost in translation :D
On the local machine, we have all the packages needed. The code gets sent for remote execution, and all the local packages are frozen correctly with pip.
The pipeline controller task is then generated and executed remotely, and it has all the relevant packages.
Each component it launches, however, is missing the internal packages available earlier :(

I have no idea what’s the difference, but it does not log the internal repository 😞 If I knew why, I would be able to solve it myself… hehe

Pinging about this still, unresolved 🤔

ClearML does not capture our internal libraries and so our functions (pipeline steps) crash with missing modules.

Alternatively, it would be good to specify both some requirements and auto-detect 🤔

Either one would be nice to have. I kinda like the instant search option, but could live with an ENTER to search.

I opened this meanwhile - https://github.com/allegroai/clearml-server/issues/138

Generally, it would also be good if the pop-up presented some hints about what went wrong with fetching the experiments. Here, I know the pattern is incomplete and invalid. A less advanced user might not understand what's up.

Hey @<1523701205467926528:profile|AgitatedDove14> , thanks for the reply!

We would like to avoid dockerizing all our repositories. And for the time being we have not used the decorators, but we can do that too.
The pipeline is instead built dynamically at the moment.

The issue is that the components do not have their dependency. For example:

def step_one(...):
    from internal.repo import private
    # do stuff

When step_one is added as a component to the pipeline, it does ...

I’d like to refrain from manually specifying the dependencies, since it adds a lot of overhead to extend

Exactly, it should have auto-detected the package.

It is. In what format should I specify it? Would this enforce that package on various components? Would it then no longer capture import statements?

Still crashing, I think that may not be the correct virtual environment to edit 🤔
It's the one created later down the line

I'll try that in a bit (that requires some access control changes). Any idea how can I modify the dynamically created virtualenv?

` Poetry Enabled: Ignoring requested python packages, using repository poetry lock file!
The currently activated Python version 3.10.6 is not supported by the project (~3.8.0).
Trying to find and use a compatible version.
Using python3.8 (3.8.16)
Creating virtualenv ... in /root/.clearml/venvs-builds/3.10/task_repository/...git/.venv
Installing dependencies from ...

SuccessfulKoala55 it does not

Ultimately we're trying to avoid docker in AWS autoscaler (virtualization on top of virtualization seems redundant), and instead we maintain an AMI for a faster boot sequence.
We had no issues when we used pip , but now when trying to work with poetry all these issues came up.
The way I understand poetry to work, is that it is expected there is one system-wide installation that is used for virtual environment creation and manipulation. So at least it may be desired that the ...

I've tried also e.g. setting gent.package_manager.priority_packages = ["poetry"] , and/or agent.package_manager.poetry_version = ">1.2.0" , and other flags, but these affect only the main /clearml_agent_venv environment, and not the one actually generated by the clearml-agent when executing the task

This happened again 🤔
How many files does ClearML touch? :shocked_face_with_exploding_head:

Let me know if there's any additional information that can help SuccessfulKoala55 !

SuccessfulKoala55 no that did not solve the issue 😞

Let me have a quick look.

I'll try a hacky-way around it with sed -i 's/include-system-site-packages = false/include-system-site-packages = true/g' clearml_agent_venv/pyvenv.cfg and report back.

Hi SuccessfulKoala55 !
Could you elaborate on how best to delete these from the database?

It is

Created this for follow up, SuccessfulKoala55 ; I'm really stumped. Spent the entire day on this 🥹
https://github.com/allegroai/clearml-agent/issues/134

It failed on some missing files in my remote_execution, but otherwise seems fine now

Hi AgitatedDove14 !

Ah, thanks! I'll use the artifacts for linking.

We've forgone the "use current task" already because it indeed made things even more difficult (the task that was used is then automatically hidden by this automatic renaming of dataset tasks).
The current implementation (since 1.6.3 I think) creates the issues in the linked comment (with images to visualize).

That's a nice work around of course - I'm sure it works and I'll give it a shot momentarily. I'm just wondering if ClearML could automatically recognize image files in upload_artifact (and other well known suffixes) and do that for me.

Indeed. I'll open an issue, sure!

I guess I'll have to rerun the experiment without tags for this?

So the ..data referenced in the example above are part of the git repository?
What about setting the working_directory to the user working directory using Task.init or Task.create ?

I commented on your suggestion to this on GH. Uploading the artifacts would happen via some SDK before switching to remote execution.
When cloning a task (via WebUI or SDK), a user should have an option to also clone these input artifacts or simply linking to the original. If linking to the original, then if the original task is deleted - it is the user's mistake.

Alternatively, this potentially suggests "Input Datasets" (as we're imitating now), such that they are not tied to the original t...

That gives us the benefit of creating "local datasets" (confined to the scope of the project, do not appear in Datasets tabs, but appear as normal tasks within the project)