Iris Example with Model Signature #46
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Signed-off-by: Shrinath Suresh <shrinath@ideas2it.com>
Signed-off-by: Shrinath Suresh <shrinath@ideas2it.com>
Signed-off-by: Shrinath Suresh <shrinath@ideas2it.com>
shrinath-suresh
changed the title
| if self.mlmodel_file_path: | ||
| mlmodel = Model.load(self.mlmodel_file_path) | ||
| if not hasattr(mlmodel, "signature"): | ||
| raise Exception("Model Signature not found") | ||
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| input_schema = mlmodel.get_input_schema() | ||
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| from mlflow.pyfunc import _enforce_schema | ||
| _enforce_schema(data, input_schema) | ||
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@shrinath-suresh
Do we really need this change? Have you tried using mlflow.pyfunc.load_model("<model_uri>")?
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@shrinath-suresh
Do we really need this change? Have you tried usingmlflow.pyfunc.load_model("<model_uri>")?
As of now, mlflow.pytorch library only allows to log the signature. But, it doesnt have the mechanism to validate the signature.
Is it the case, that pytorch users can log the model signature using mlflow.pytorch and validate it using mlflow.pyfunc ? If that is the case, we don't need this change.
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that pytorch users can log the model signature using mlflow.pytorch and validate it using mlflow.pyfunc
I think yes because that's the only way to enforce the schema now.
| import pandas as pd | ||
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| class IrisClassification(pl.LightningModule): |
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I think we have a similar class in other pytorch examples. Can we reuse it?
| # Uncomment this block to check invalid data type enforcement | ||
| # for column in df.columns: | ||
| # df[column] = df[column].astype("str") | ||
| # | ||
| # print("Result with invalid datatype: ", model.predict(df)) |
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Can we uncomment this block and wrap it with try-catch and print out the error message in the except clause?
try:
model.predict(df)
except Exception as e:
print(e)
| mlflow.pytorch.save_model(trainer.get_model(), "model", signature=signature) | ||
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| model = _load_pyfunc(path="model/data", validate_signature=True) | ||
| df = pd.read_json("sample.json") |
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Do we need really sample.json? Can we just hard-code its content in pd.DataFrame?
df = pd.DataFrame({"sepal length (cm)": ...})
| input_schema = Schema( | ||
| [ | ||
| ColSpec("double", "sepal length (cm)"), | ||
| ColSpec("double", "sepal width (cm)"), | ||
| ColSpec("double", "petal length (cm)"), | ||
| ColSpec("double", "petal width (cm)"), | ||
| ] | ||
| ) | ||
| output_schema = Schema([ColSpec("long")]) |
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Can we infer the schema from the iris dataframe?
Signed-off-by: Shrinath Suresh shrinath@ideas2it.com
What changes are proposed in this pull request?
Logging model signature and validating model signature for iris classification example.
Reused the model signature enforcements from pyfunc.
How is this patch tested?
Existing Unit Tests
Release Notes
Is this a user-facing change?
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesarea/examples: Example codearea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/projects: MLproject format, project running backendsarea/scoring: Local serving, model deployment tools, spark UDFsarea/server-infra: MLflow server, JavaScript dev serverarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, JavaScript, plottingarea/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows: Windows supportLanguage
language/r: R APIs and clientslanguage/java: Java APIs and clientslanguage/new: Proposals for new client languagesIntegrations
integrations/azure: Azure and Azure ML integrationsintegrations/sagemaker: SageMaker integrationsintegrations/databricks: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notes