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

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

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)

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 ?

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

If you use this one for example, will the component have pandas as part of the requirement
None

def step_two(...):
    import pandas as pd
    # do stuff

If so (and it should), what's the difference, where is "internal.repo " different from pandas ?

Hi @<1523701083040387072:profile|UnevenDolphin73>

How can I ensure tasks in a pipeline have the same environment as the pipeline itself?
...
but the tasks (executed remotely) do not use that same environment?

Just verifying, we are talking about pipeline decorators?

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

You can specify the docker on the decorator itself:
None
Regrading capturing the packages, if you import them inside the decorated package, they will be captured based on what is installed in the local (i.e. initial) environment. The idea is that the components are Not the same as the logic, basically the logic of the pipeline should not have any real package requirement, only the components (actually doing something), should. What am I missing ?

It is installed on the pipeline creating the machine.
I have no idea why it did not automatically detect it 😞

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?

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)

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+

And is this repo installed on the pipeline creating machine ?
Basically I'm asking how come it did not automatically detect it?

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 🤔

• 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

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 ?

PricklyRaven28 That would be my fallback, it would make development much slower (having to build containers with every small change)

For example:

my-repo @ git+

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.

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… 🤔

not sure about this, we really like being in control of reproducibility and not depend on the invoking machine… maybe that’s not what you intend

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

Hi UnevenDolphin73 , when you say pipeline itself you mean the controller? The controller is only in charge of handling the components. Lets say you have a pipeline with many parts. If you have a global environment then it will force a lot of redundant installations through the pipeline. What is your use case?

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

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

Pinging about this still, unresolved 🤔

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

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

Yes, for example.

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

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 :(