Added start_time and duration to csv export #4851
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Signed-off-by: Marijn Valk <marijncv@hotmail.com>
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Signed-off-by: Marijn Valk <marijncv@hotmail.com>
dbczumar
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Sep 29, 2021
| @@ -191,7 +191,7 @@ class Utils { | |||
| * @param endTime in milliseconds | |||
| */ | |||
| static getDuration(startTime, endTime) { | |||
| return startTime && endTime ? this.formatDuration(endTime - startTime) : null; | |||
| return startTime && endTime ? this.formatDuration(endTime - startTime) : ''; | |||
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I think returning null here is semantically clearer than returning the empty string. Can we maintain the null return type and handle the mapping from null to empty string in the CSV export routine?
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Agreed. Reverted back to returning null here.
For consistency, should this PR refactor the other getter functions in Utils (getRunName, getSourceName etc.) to return null instead of empty strings? Let me know!
Signed-off-by: Marijn Valk <marijncv@hotmail.com>
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Signed-off-by: Marijn Valk marijncv@hotmail.com
What changes are proposed in this pull request?
This PR adds the start time and duration of each run to the csv export. closes #4850
How is this patch tested?
Use the csv export button and see the Start time and duration added. For runs in progress, the duration cell is empty
Release Notes
Is this a user-facing change?
Start time and duration have been added to the csv export in the experiment UI
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