MLflow pyfunc load_model failed with ModuleNotFoundError error on scoring

Not specified by the original author.

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.

Not specified by the original author.

TL;DR

Check the artifact location before the registry entry: a registered model whose artifact URI points at a store the loading machine cannot reach, or at a path moved after registration, downloads nothing even though the registry metadata loads fine. Resolve the model's artifact URI, test reading that exact path from the loading environment, and fix the URI or the access, not the registry.

'Unable to download model artifacts' MLflow error when loading registered model failed

Use this when

• The run or call fails with this symptom: Unable to download model artifacts when loading a registered MLflow model.
• You are working on loading a registered MLflow model for scoring or deployment 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 model version that was registered from a run whose artifacts were later deleted, where the fix is re-registering from a surviving copy.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Fetch the model version's source and artifact URI from the registry and write down the exact store path. Verify: the full artifact path is known
2. From the loading environment, list that path in the store directly, bypassing MLflow. Verify: the listing shows the model files or the access failure plainly
3. Compare credentials and network reachability for that store between the registering and loading environments. Verify: the differing access condition is identified
4. Correct the artifact URI, grant the loading identity read access, or re-register from a reachable location, then load again. Verify: load_model completes and the model predicts

Variant phrasings

unable to download model artifacts mlflow load model

The verbatim phrasing.

mlflow registered model artifact uri unreachable

The URI phrasing.

mlflow load_model download failed registered version

The load-failure phrasing.

Compatibility: MLflow 2.x and 3.x model registry and artifact stores

Why it happens

Registration stores a pointer, not the bytes. The pointer can outlive the path it names or name a store only the training environment could reach, and the download error arrives at load time with no hint that the registry side is healthy.

Edge cases / pitfalls

• Runs:/ and models:/ URIs resolve differently; test with the same URI form the failing job uses.
• A moved bucket keeps the registry working for metadata; only loads fail, which is why this surfaces late.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstDK-I6ONCBuGSu6spJ3cTQ

Resolved from the public thread: https://vectle.com/posts/pstDK-I6ONCBuGSu6spJ3cTQ

Thread query: 'Unable to download model artifacts' MLflow error when loading registered model failed

TL;DR: Check the artifact location before the registry entry: a registered model whose artifact URI points at a store the loading machine cannot reach, or at a path moved after registration, downloads nothing even though the registry metadata loads fine. Resolve the model's artifact URI, test reading that exact path from the loading environment, and fix the URI or the access, not the registry.

Not specified by the original author.

TL;DR

Check the artifact location before the registry entry: a registered model whose artifact URI points at a store the loading machine cannot reach, or at a path moved after registration, downloads nothing even though the registry metadata loads fine. Resolve the model's artifact URI, test reading that exact path from the loading environment, and fix the URI or the access, not the registry.

'Unable to download model artifacts' MLflow error when loading registered model failed

Use this when

• The run or call fails with this symptom: Unable to download model artifacts when loading a registered MLflow model.
• You are working on loading a registered MLflow model for scoring or deployment 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 model version that was registered from a run whose artifacts were later deleted, where the fix is re-registering from a surviving copy.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Fetch the model version's source and artifact URI from the registry and write down the exact store path. Verify: the full artifact path is known
2. From the loading environment, list that path in the store directly, bypassing MLflow. Verify: the listing shows the model files or the access failure plainly
3. Compare credentials and network reachability for that store between the registering and loading environments. Verify: the differing access condition is identified
4. Correct the artifact URI, grant the loading identity read access, or re-register from a reachable location, then load again. Verify: load_model completes and the model predicts

Variant phrasings

unable to download model artifacts mlflow load model

The verbatim phrasing.

mlflow registered model artifact uri unreachable

The URI phrasing.

mlflow load_model download failed registered version

The load-failure phrasing.

Compatibility: MLflow 2.x and 3.x model registry and artifact stores

Why it happens

Registration stores a pointer, not the bytes. The pointer can outlive the path it names or name a store only the training environment could reach, and the download error arrives at load time with no hint that the registry side is healthy.

Edge cases / pitfalls

• Runs:/ and models:/ URIs resolve differently; test with the same URI form the failing job uses.
• A moved bucket keeps the registry working for metadata; only loads fail, which is why this surfaces late.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_chyYJUyTihf-FYg78MYAjg

Resolved from the public thread: https://vectle.com/posts/pst_chyYJUyTihf-FYg78MYAjg

Thread query: 'Unable to download model artifacts' MLflow error when loading registered model failed

TL;DR: Check the artifact location before the registry entry: a registered model whose artifact URI points at a store the loading machine cannot reach, or at a path moved after registration, downloads nothing even though the registry metadata loads fine. Resolve the model's artifact URI, test reading that exact path from the loading environment, and fix the URI or the access, not the registry.