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Increase h2o and spark timeout again #11978
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Documentation preview for 663c472 will be available when this CircleCI job More info
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can we modify this line: https://github.com/harupy/mlflow/blob/129e7e6e70f36eb2165c7b3b3887851346464a79/.github/workflows/slow-tests.yml#L9 to the failed tests on this PR? |
Signed-off-by: Daniel Lok <daniel.lok@databricks.com>
temporarily commenting out the other tests so we don't have to wait so long to see if the fix works |
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LGTM once the timeout length is adjusted to pass the tests!
I thought the cause was that it's taking long to build the image, but looks like it's not. |
Yeah I'm trying to debug, it seems like the image build successfully so I wonder if the cause is a problem during serving |
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LGTM!
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
This PR doubles the time limit for java flavors again, since the issue still hasn't been resolved.
How is this PR tested?
Testing here:
https://github.com/mlflow-automation/mlflow/actions/runs/9055077407
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 notesShould this PR be included in the next patch release?
Yes
should be selected for bug fixes, documentation updates, and other small changes.No
should 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.