[BUG] `_check_requirement_satisfied()` ignores environment markers

[ishaan-mehta]

Issues Policy acknowledgement

• [X] I have read and agree to submit bug reports in accordance with the issues policy

Where did you encounter this bug?

Local machine

Willingness to contribute

Yes. I can contribute a fix for this bug independently.

MLflow version

• Client: 2.17.0

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS Sonoma 14.7
• Python version: 3.12.0

Describe the problem

_check_requirement_satisfied() does not check the Requirement object to see if it actually needs to be installed in the current environment. Requirements can have environment markers like so:

colorama==0.4.6 ; python_version >= "3.12" and python_version < "3.13" and (platform_system == "Windows" or sys_platform == "win32")

This is very common when using requirements,txt files that are exported from poetry, for example.

You can evaluate whether the requirement is valid for the current detected environment by running req.marker.evaluate().

Tracking information

N/A

Code to reproduce issue

from langchain_core.runnables import RunnableLambda
import mlflow
from packaging.requirements import Requirement
import sys

toy_chain = RunnableLambda(lambda x: x)

req = "langchain==9.9.9 ; sys_platform == 'fake_platform'"

# confirm we are not on a platform called "fake_platform"
assert sys.platform != "fake_platform"

# confirm the environment marker indicates the requirement is not valid for this environment
assert not Requirement(req).marker.evaluate()

with mlflow.start_run():
    mlflow.langchain.log_model(
        lc_model=toy_chain,
        artifact_path="chain",
        registered_model_name="my_toy_model",
        pip_requirements=[
            req,
        ],
    )

Stack trace

N/A

Other info / logs

The relevant warning from the minimal reproducible example I shared:

2024/10/17 14:43:06 WARNING mlflow.utils.requirements_utils: Detected one or more mismatches between the model's dependencies and the current Python environment:
 - langchain (current: 0.1.20, required: langchain==9.9.9; sys_platform == "fake_platform")
To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file.

What component(s) does this bug affect?

• [ ] area/artifacts: Artifact stores and artifact logging
• [ ] area/build: Build and test infrastructure for MLflow
• [ ] area/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrations
• [ ] area/docs: MLflow documentation pages
• [ ] area/examples: Example code
• [ ] area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• [X] area/models: MLmodel format, model serialization/deserialization, flavors
• [ ] area/recipes: Recipes, Recipe APIs, Recipe configs, Recipe Templates
• [ ] area/projects: MLproject format, project running backends
• [ ] area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• [ ] area/server-infra: MLflow Tracking server backend
• [ ] area/tracking: Tracking Service, tracking client APIs, autologging

What interface(s) does this bug affect?

• [ ] area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• [ ] area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• [ ] area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• [ ] area/windows: Windows support

What language(s) does this bug affect?

• [ ] language/r: R APIs and clients
• [ ] language/java: Java APIs and clients
• [ ] language/new: Proposals for new client languages

What integration(s) does this bug affect?

• [ ] integrations/azure: Azure and Azure ML integrations
• [ ] integrations/sagemaker: SageMaker integrations
• [ ] integrations/databricks: Databricks integrations