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set_signature
API to set the signature for model artifacts
#8476
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Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
set_signature
API to set the signature for model artifactsset_signature
API to set the signature for model artifacts
Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
mlflow/models/signature.py
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assert isinstance( | ||
signature, ModelSignature | ||
), "The signature argument must be a ModelSignature object" | ||
assert not ModelsArtifactRepository.is_models_uri( | ||
model_uri | ||
), "Model URIs with the `models:/` scheme are not supported." |
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Can we turn these into MlflowException
with error code INVALID_PARAMETER_VALUE
?
mlflow/models/signature.py
Outdated
assert isinstance( | ||
signature, ModelSignature | ||
), "The signature argument must be a ModelSignature object" | ||
assert not ModelsArtifactRepository.is_models_uri( | ||
model_uri | ||
), "Model URIs with the `models:/` scheme are not supported." |
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Can we update the error message to recommend that users create a new model version? It's always nice when I see an error message that tells me what to do next :D
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In a follow-up PR, I'll link to documentation that describes how to do this.
mlflow/models/signature.py
Outdated
fail. Furthermore, as model registry artifacts are read-only, model artifacts located in the | ||
model registry and represented by ``models:/`` URI schemes are not compatible with this API. |
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Can we recommend that users create a new model version in order to update the signature? Might be nice to include a link to some docs about how to do that (we may have to add those docs as part of this PR too, or feel free to inline as a Python code example)
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I'll add a new doc section in the Model Signatures doc in a follow up PR.
out.write(artifact_text) | ||
|
||
with mlflow.start_run() as run: | ||
mlflow.log_metric("coolness", 1) |
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Coolness, indeed :D
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LGTM with nitty docs comments. Impl looks wonderful! Thanks @jerrylian-db !
Documentation preview for 092bfb5 will be available here when this CircleCI job completes successfully. More info
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Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
Signed-off-by: Jerry Liang <jerry.liang@databricks.com>
Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
This PR introduces a
set_signature
API that makes it possible to set signatures for model artifacts. These model artifacts can be locally saved models, logged run model artifacts, or artifacts in any of the supported artifact stores. The function respects the immutability of model registry artifacts by disallowing model artifacts specified with amodel:/
scheme URI.How is this patch tested?
I tested the
set_signature
API in a Databricks notebook. Here are the steps I took:%pip install git+https://github.com/mlflow/mlflow@add_signature
Does this PR change the documentation?
Release Notes
Is this a user-facing change?
Introduces a
mlflow.models.set_signature()
API to set signatures for model artifacts.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/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/breaking-change
- The PR will be mentioned in the "Breaking Changes" sectionrn/none
- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" 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