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Bug fix: TraceStatus hydration from proto #12044
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@mparkhe 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. |
Signed-off-by: Mani Parkhe <mani@databricks.com>
mlflow/entities/trace_info.py
Outdated
@@ -83,7 +83,7 @@ def to_dict(self): | |||
Update status field to the string value for serialization. | |||
""" | |||
trace_info_dict = asdict(self) | |||
trace_info_dict["status"] = self.status.value |
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Following up from our thread offline, the defined type of self.status
in TraceInfo
is TraceStatus
, so the preexisting logic in to_dict()
should in theory be correct. My guess is that TraceInfo
is being constructed with a string type for TraceStatus
(which is the incorrect type) when a trace is read from the backend.
The problem seems to lie in
mlflow/mlflow/store/tracking/rest_store.py
Line 338 in f54297d
return TraceInfo.from_proto(response_proto.trace_info) |
mlflow/mlflow/entities/trace_info.py
Line 75 in f54297d
status=TraceStatus.from_proto(proto.status), |
mlflow/mlflow/entities/trace_status.py
Line 24 in f54297d
return ProtoTraceStatus.Name(proto_status) |
here, we see that TraceStatus.from_proto()
doesn't return a TraceStatus
like it should. Instead, it returns a string. We need to fix the implementation of TraceStatus.from_proto()
and then add test coverage verifying that a TraceStatus
constructed from a proto can be converted to dict / JSON and back.
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Yeah! I realized that the fix was superficial and hence incorrect. Fixed the from_proto hydration and added a bunch of tests
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Following from https://github.com/mlflow/mlflow/pull/12044/files#r1605945236, I think the fix lies in TraceStatus.from_proto()
Signed-off-by: Mani Parkhe <mani@databricks.com>
Signed-off-by: Mani Parkhe <mani@databricks.com>
@@ -1,11 +1,14 @@ | |||
import pytest | |||
|
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Can we add a test case where we do TraceInfo.from_dict(TraceInfo.from_proto(...).to_dict())
to make sure that loading a trace from proto and converting to dict & back works properly? This will prevent recurrence of the issue.
def test_to_dict(trace_info): | ||
trace_as_dict = trace_info.to_dict() | ||
assert trace_as_dict == { | ||
"request_id": "request_id", | ||
"experiment_id": "test_experiment", | ||
"timestamp_ms": 0, | ||
"execution_time_ms": 1, | ||
"status": "OK", | ||
"request_metadata": { | ||
"foo": "bar", | ||
"k" * 1000: "v" * 1000, | ||
}, | ||
"tags": { | ||
"baz": "qux", | ||
"k" * 2000: "v" * 2000, | ||
}, | ||
} |
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Awesome that we're adding this. I think this still passes on master
because the status
field of TraceInfo
is being set to a TraceStatus
object in https://github.com/mlflow/mlflow/pull/12044/files#diff-5aee524692b8b8837b083f08c50a7feb4ebc2d192b97548527ad6978173bc99dR16.
If we also add https://github.com/mlflow/mlflow/pull/12044/files#r1605960450, then we will prevent recurrence.
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doh!
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LGTM once https://github.com/mlflow/mlflow/pull/12044/files#r1605960450 is addressed. Thanks @mparkhe ! :D
Signed-off-by: Mani Parkhe <mani@databricks.com>
Signed-off-by: Mani Parkhe <mani@databricks.com>
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What changes are proposed in this pull request?
Fixing a bug in trace info deserialization
How is this PR tested?
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.