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

I’ve tracked it down further, it seems the pigar utility does not apply any smart logic there.
The case we have is the following -

• We have a monorepo, but all modules/libs share a common namespace foo ; so e.g. working on module mod , we use from foo.mod import …
• This then looks for a module called foo , even though it’s just a namespace
• In the dist-info requirement, it seems any hyphen, dot, etc are swapped for an underscore, so our site-packages represents this as foo_mod-x.y.z-distinfo
• This ends showing the available package is foo_mod
• Specifically since foo is not generated, it is assumed local and dropped 🤔

Exactly, it should have auto-detected the package.

Yes. Though again, just highlighting the naming of

foo-mod

is arbitrary. The actual module simply has a folder structured with an implicit namespace:

Yep I think this is exactly why it fails detecting it, let me check that

And it’s failing on typing hints for functions passed in

pipe.add_function_step(…, helper_function=[…])

… I guess those aren’t being removed like the wrapped function step?

Can you provide the log? I think I'm missing what exactly was added into the decorator that somehow fails the Task creation

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

We’d be happy if ClearML captures that (since it uses e.g. pip, then we have the git + commit hash for reproducibility), as it claims it would 😅

Any thoughts CostlyOstrich36 ?

We can change the project name’s of course, if there’s a suggestion/guide that will make them see past the namespace…

Using the PipelineController with add_function_step

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?

For example:

my-repo @ git+

what format should I specify it

requirements.txt format e.g. ["package >= 1.2.3"]

Would this enforce that package on various components

This is a per component control, so you can have different packages / containers based on the componnent

Would it then no longer capture import statements?

This is replacing the auto detected packages, but obviously this fails to detect your internal repo package, which is the main issue here.
How is "internal package" installed, in other words can you send the pip freeze of th machine creating the pipeline ? because this is where the packages are detected (if packages are not installed you cannot infer the actual package name nor the version just from the import statement)

it does

not

include the “internal.repo” as a package dependency, so it crashes.

understood

And for the time being we have not used the decorators,

So how are you building the pipeline component ?

There's no decorator, just e.g.

def helper(foo: Optional[Any] = None):
    return foo

def step_one(...):
    # stuff

Then the type hints are not removed from helper and the code immediately crashes when being run

Well the individual tasks do not seem to have the expected environment.

There's code that strips the type hints from the component function, just think it should be applied to the helper functions too :)

We also wanted this, we preferred to create a docker image with all we need, and let the pipeline steps use that docker image

That way you don’t rely on clearml capturing the local env, and you can control what exists in the env

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 not include the “internal.repo” as a package dependency, so it crashes.

We have an internal mono-repo and some of the packages are required - they’re all available correctly for the controller, only some are required for the individual tasks, but the “magic” doesn’t happen 😞
That is, the controller does not identify them as a requirement, so they’re not installed in the tasks environment.

How or why is this the issue?

The main issue is a missing requirement on the Task component, and this is why it is failing.
You can however manually specify package (and I'm assuming this will solve the issue), but it should have autodetected, no?

Still; anyone? 🥹 @<1523701070390366208:profile|CostlyOstrich36> @<1523701205467926528:profile|AgitatedDove14>

Yes. Though again, just highlighting the naming of foo-mod is arbitrary. The actual module simply has a folder structured with an implicit namespace:

foo/
  mod/
    __init__.py
    # stuff

FWIW, for the time being I’m just setting the packages to all the packages the pipeline tasks sees with:

    packages = get_installed_pkgs_detail()
    packages = [f"{name}=={version}" if version else name for name, version in packages.values()]
    packages = task.data.script.requirements.get('pip', task.data.script.requirements.get('poetry')) or packages
    print(f"Task requirements:\n{packages}")
    tmp_requirements_file = "tmp_reqs.txt"
    with open(tmp_requirements_file, "w") as f:
        f.write("\n".join(packages) if isinstance(packages, list) else packages)
    
    # ...
    
    pipe.add_function_step(..., packages=tmp_requirements_file)

• This then looks for a module called foo , even though it’s just a namespaceI think this is the issue, are you using python package name spaces ?
  (this is a PEP feature that is really rarely used, and I have seen break too many times)
  Assuming you have from from foo.mod import what are you seeing in pip freeze ? I'd like to see if we can fix this, and better support namespaces

So from foo.mod import "translates" to foo-mod @ git+ None .. ?

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

I think this is the main issue, is this reproducible ? How can we test that?

It’s just that for the packages argument, ClearML says:

If not provided, packages are automatically added based on the imports used inside the wrapped function.

So… 🤔

is this repo installed on the machine creating the pipeline ?
You can also manually add it here `packages={"link_to_internal_python_package",]
None

The only thing I could think of is that the output of pip freeze would be a URL?

So a missing bit of information that I see I forgot to mention, is that we named our packages as foo-mod in pyproject.toml . That hyphen then get’s rewritten as foo_mod.x.y.z-distinfo .

foo-mod @ git+

None
This example ?