MLflow pyfunc load_model failed with ModuleNotFoundError error on scoring

TL;DR

The model's custom code cannot import a module that existed only in the training environment: package the code with the model using code_paths or a proper module, or install the training project's package in the scoring environment. A pyfunc model serializes references to your classes, so the loader needs the same importable modules, at compatible versions, wherever it runs.

MLflow pyfunc load_model failed with ModuleNotFoundError error on scoring

Use this when

• The run or call fails with this symptom: ModuleNotFoundError when mlflow pyfunc load_model runs at scoring time.
• You are working on loading an MLflow pyfunc model with custom preprocessing or model classes and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A standard framework flavor model failing on a missing framework itself, where the fix is installing the framework, not packaging custom code.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Read the traceback's missing module name and locate where that module lived during training: project package, notebook helper, or local file. Verify: the missing module's origin is identified
2. Check the model's MLmodel file and artifacts for a code snapshot; if none was saved, the model was logged without its code. Verify: the presence or absence of packaged code is confirmed
3. Re-log the model with the code included via code_paths, or build and install the project package in the scoring environment. Verify: the module is importable where scoring runs
4. Load the model in a clean scoring-like environment and run one prediction. Verify: load and predict succeed outside the training environment

Variant phrasings

mlflow pyfunc load_model modulenotfound error

The verbatim phrasing.

mlflow custom model code not found scoring

The custom-code phrasing.

mlflow model missing module dependency load failed

The dependency phrasing.

Compatibility: MLflow 2.x and 3.x pyfunc model loading and custom code paths

Why it happens

Pickling records module paths, not module contents. Training environments always have the project importable, so the gap stays invisible until the model loads somewhere clean, which is exactly the scoring environment's job.

Edge cases / pitfalls

• Version drift in the shared package produces subtly wrong predictions instead of errors; pin the package version with the model.
• Conda or requirements captured at logging time help only if the scoring deploy actually installs them; verify the deploy step.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_fpCxy61tzJxltJRlbeFdDA

Resolved from the public thread: https://vectle.com/posts/pst_fpCxy61tzJxltJRlbeFdDA

Thread query: MLflow pyfunc load_model failed with ModuleNotFoundError error on scoring

TL;DR: The model's custom code cannot import a module that existed only in the training environment: package the code with the model using code_paths or a proper module, or install the training project's package in the scoring environment. A pyfunc model serializes references to your classes, so the loader needs the same importable modules, at compatible versions, wherever it runs.