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Do not modify tags dict on start_run() #5191
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@matheusMoreno Thanks for the contribution! The DCO check failed. Please sign off your commits by following the instructions here: https://github.com/mlflow/mlflow/runs/4599678833. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.rst#sign-your-work for more details. |
Signed-off-by: Matheus Moreno <matheus.moreno@hurb.com>
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@matheusMoreno Thanks for the PR! Can we also fix this line?
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Signed-off-by: Matheus Moreno <matheus.moreno@hurb.com>
Signed-off-by: Matheus Moreno <matheus.moreno@hurb.com>
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Fixed on 355fcd4. Did not use deep copy because the function only reads the dict, with no modifications. Also renamed the variable on |
Ah, you're right. I didn't look at the code carefully. |
tags = tags if tags else {} | ||
run_tags = tags if tags else {} |
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Do we need this change?
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At first I thought so, since it could rewrite the parameter, but it does seem unnecessary. Will undo the commit!
This reverts commit 355fcd4. Signed-off-by: Matheus Moreno <matheus.moreno@hurb.com>
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LGTM!
What changes are proposed in this pull request?
On the
start_run()
function, make a deep copy of the tags dictionary and do not redefine the dictionary. Fixes #5190.How is this patch tested?
Installed MLflow from my branch, and ran the sample code in #5190. It did not raise any errors.
Does this PR change the documentation?
Release Notes
Is this a user-facing change?
Fixes bug where dictionary variables for tags were being altered, causing runs to be incorrectly considered nested.
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
: 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