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[WIP] Updated how we parse json input in the scoring server.
This is in preparation for the updates to the scoring protocol in mlflow 2.0.
Signed-off-by: tomasatdatabricks [email protected]
Related Issues/PRs
#xxx
What changes are proposed in this pull request?
(Please fill in changes proposed in this fix)
How is this patch tested?
- [ ] I have written tests (not required for typo or doc fix) and confirmed the proposed feature/bug-fix/change works.
Does this PR change the documentation?
- [x ] No. You can skip the rest of this section.
- [ ] Yes. Make sure the changed pages / sections render correctly by following the steps below.
- Check the status of the
ci/circleci: build_doc
check. If it's successful, proceed to the next step, otherwise fix it. - Click
Details
on the right to open the job page of CircleCI. - Click the
Artifacts
tab. - Click
docs/build/html/index.html
. - Find the changed pages / sections and make sure they render correctly.
Release Notes
Is this a user-facing change?
- [ x] No. You can skip the rest of this section.
- [ ] Yes. Give a description of this change to be included in the release notes for MLflow users.
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)
What component(s), interfaces, languages, and integrations does this PR affect?
Components
- [ ]
area/artifacts
: Artifact stores and artifact logging - [ ]
area/build
: Build and test infrastructure for MLflow - [ ]
area/docs
: MLflow documentation pages - [ ]
area/examples
: Example code - [ ]
area/model-registry
: Model Registry service, APIs, and the fluent client calls for Model Registry - [ ]
area/models
: MLmodel format, model serialization/deserialization, flavors - [ ]
area/pipelines
: Pipelines, Pipeline APIs, Pipeline configs, Pipeline Templates - [ ]
area/projects
: MLproject format, project running backends - [ x]
area/scoring
: MLflow Model server, model deployment tools, Spark UDFs - [ ]
area/server-infra
: MLflow Tracking server backend - [ ]
area/tracking
: Tracking Service, tracking client APIs, autologging
Interface
- [ ]
area/uiux
: Front-end, user experience, plotting, JavaScript, JavaScript dev server - [ ]
area/docker
: Docker use across MLflow's components, such as MLflow Projects and MLflow Models - [ ]
area/sqlalchemy
: Use of SQLAlchemy in the Tracking Service or Model Registry - [ ]
area/windows
: Windows support
Language
- [ ]
language/r
: R APIs and clients - [ ]
language/java
: Java APIs and clients - [ ]
language/new
: Proposals for new client languages
Integrations
- [ ]
integrations/azure
: Azure and Azure ML integrations - [ ]
integrations/sagemaker
: SageMaker integrations - [ ]
integrations/databricks
: Databricks integrations
How should the PR be classified in the release notes? Choose one:
- [ ]
rn/breaking-change
- The PR will be mentioned in the "Breaking Changes" section - [x]
rn/none
- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section - [ ]
rn/feature
- A new user-facing feature worth mentioning in the release notes - [ ]
rn/bug-fix
- A user-facing bug fix worth mentioning in the release notes - [ ]
rn/documentation
- A user-facing documentation change worth mentioning in the release notes