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bugfix: Update documentation to fix KeyError in demo code in quickstart #10540
bugfix: Update documentation to fix KeyError in demo code in quickstart #10540
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@wllgrnt Thank you for the contribution! Could you fix the following issue(s)? ⚠ DCO checkThe DCO check failed. Please sign off your commit(s) by following the instructions here. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.md#sign-your-work for more details. |
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@wllgrnt LGTM! Thank you so much for catching this issue and proactively making the fix! Will merge once all CI tests passed.
fyi: it seems some jobs were stuck, closed and re-opend to trigger them |
@wllgrnt Very weird but some jobs are still not executing. Could you run followings to force triggering them again? While it seems to be an intermittent issue in Github, I can investigate if that happens again. Thanks!
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Signed-off-by: William Grant <william.grant@aprioriinvestments.com>
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Thanks for the PR!
The force-push above doesn't seem to have triggered the jobs, let me know if there's something else I should try |
@wllgrnt Thanks for trying it out, that's very weird it didn't work. It seems the same issue happens for some other PRs so I will investigate further. Meanwhile, we overrode the checks and pushed as there shouldn't be a big concern for this PR. Thank you so much for your contribution!! |
🛠 DevTools 🛠
Install mlflow from this PR
Checkout with GitHub CLI
Related Issues/PRs
Resolve #10539
What changes are proposed in this pull request?
Updates the documentation so that the demo code here doesn't throw a KeyError when run.
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
This one-liner change updates the hyperopt usage example code in the quickstart documentaion so that the correct variable is referenced when logging the best tensorflow model.
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/gateway
: AI Gateway service, Gateway client APIs, third-party Gateway integrationsarea/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