Show images in the metric tab #15034
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Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
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| // The variables retrieved here are not reliable on OSS due to the usage of "%" as the separator. | ||
| // Need to switch to a different separator on the backend to fully resolve the issue. | ||
| const [serializedImageKey, stepLabel, stepString, timestampLabel, timestampString, ..._] = fileKey.split(delimiter); | ||
| const isCompressed = fileKey.includes('compressed'); |
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Is there any additional rule to limit the false positive here? E.g. what if the original users' image name is pic_compressed.png. Maybe we can limit the match target here to the ..._ part not the entire fileKey?
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Images logged through log_image(artifact_path="...") is not relevant to this change as this logic is for images with key and step. To handle the case where "key" includes "compressed" keyword, I changed "includes" to "endWith"
Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
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Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
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Signed-off-by: Tomu Hirata <tomu.hirata@gmail.com>
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Related Issues/PRs
Resolve #15013
What changes are proposed in this pull request?
There is an issue that images are not displayed correctly on the run metrics tab. This is caused by incorrectly decoding the file name that uses "%" as the separator. The full resolution needs replacing the separator, but this PR fixes the immediate issue.
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
Show images in the run metrics tab.
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 notesShould this PR be included in the next patch release?
Yesshould be selected for bug fixes, documentation updates, and other small changes.Noshould be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.What is a minor/patch release?
Bug fixes, doc updates and new features usually go into minor releases.
Bug fixes and doc updates usually go into patch releases.