Rename df arg in deploy predict abstract method#6681
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BenWilson2 merged 5 commits intobranch-2.0from Sep 2, 2022
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Signed-off-by: Ben Wilson <[email protected]>
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Signed-off-by: Ben Wilson <[email protected]>
WeichenXu123
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Sep 2, 2022
Signed-off-by: Ben Wilson <[email protected]>
WeichenXu123
approved these changes
Sep 2, 2022
dbczumar
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Sep 2, 2022
mlflow/deployments/base.py
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| :param deployment_name: Name of deployment to predict against | ||
| :param df: Pandas DataFrame to use for inference | ||
| :param inputs: Input data (or arguments) used to generate inference from a model endpoint |
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| :param inputs: Input data (or arguments) used to generate inference from a model endpoint | |
| :param inputs: Input data (or arguments) to pass to the deployment or model endpoint for inference. |
dbczumar
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Sep 2, 2022
mlflow/deployments/base.py
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| Note that the input/output types of this method matches that of `mlflow pyfunc predict` | ||
| (we accept a pandas DataFrame as input and return either a pandas DataFrame, | ||
| pandas Series, or numpy array as output). | ||
| Compute predictions on input data using the specified deployment. |
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| Compute predictions on input data using the specified deployment. | |
| Compute predictions on inputs using the specified deployment or model endpoint. |
dbczumar
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Sep 2, 2022
mlflow/deployments/base.py
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| (we accept a pandas DataFrame as input and return either a pandas DataFrame, | ||
| pandas Series, or numpy array as output). | ||
| Compute predictions on input data using the specified deployment. | ||
| Note that the input/output types of this method matches that of `mlflow pyfunc predict`. |
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| Note that the input/output types of this method matches that of `mlflow pyfunc predict`. | |
| Note that the input/output types of this method match those of `mlflow pyfunc predict`. |
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LGTM with two nitty docs comments. Thanks @BenWilson2 !
Signed-off-by: Ben Wilson <[email protected]>
Signed-off-by: Ben Wilson <[email protected]>
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Related Issues/PRs
#xxx
What changes are proposed in this pull request?
rename the argument from
dftodatato reflect the broad allowable input types for pyfunc deployment serving in the .predict() abstract method.How is this patch tested?
Existing tests
Does this PR change the documentation?
Detailslink on thePreview docscheck.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/pipelines: Pipelines, Pipeline APIs, Pipeline configs, Pipeline Templatesarea/projects: MLproject format, project running backendsarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/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