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Fix inputs & outputs log for langchain autologging #10952
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Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
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Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
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LGTM
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Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
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Signed-off-by: Serena Ruan <serena.rxy@gmail.com> Signed-off-by: lu-wang-dl <lu.wang@databricks.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Fix errors when input and output has different length.
The improvement in this PR is to flatten the input & output such that if the data is a dictionary, each key will correspond to a column in the pandas dataframe. Then we log the combined result into inference_inputs_outputs.json file.
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing 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/deployments
: MLflow Deployments client APIs, server, and third-party Deployments integrationsarea/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/recipes
: Recipes, Recipe APIs, Recipe configs, Recipe 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/none
- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/breaking-change
- The PR will be mentioned in the "Breaking Changes" 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