Add documentation and examples for transformers flavor #8236
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Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
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jinzhang21
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Apr 15, 2023
harupy
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Apr 15, 2023
docs/source/models.rst
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| ZeroShot Classification* Dict[str, Union[List[str]|str]]* str or List[str] | ||
| Table Question Answering** Dict[str, Union[List[str]|str]]** str or List[str] |
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Suggested change
| ZeroShot Classification* Dict[str, Union[List[str]|str]]* str or List[str] | |
| Table Question Answering** Dict[str, Union[List[str]|str]]** str or List[str] | |
| ZeroShot Classification* Dict[str, List[str] | str]* str or List[str] | |
| Table Question Answering** Dict[str, List[str] | str]** str or List[str] |
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For ZeroShot Classification, is {"<key1>": "<val1>", <key1>: ["<val2>"]} a valid input? Dict[str, List[str] | str] implies it is. If it's not, Dict[str, str], Dict[str, List[str]] is more precise and less confusing.
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I like this better. Easier to read :)
harupy
reviewed
Apr 17, 2023
| mlflow.transformers.generate_signature_output(conversational_pipeline, "Hi there, chatbot!"), | ||
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| model_info = mlflow.transformers.log_model( |
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| model_info = mlflow.transformers.log_model( | |
| with mlflow.start_run(): | |
| model_info = mlflow.transformers.log_model( |
harupy
reviewed
Apr 17, 2023
| tokenizer=transformers.T5TokenizerFast.from_pretrained("t5-small", model_max_length=100), | ||
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| model_info = mlflow.transformers.log_model( |
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| model_info = mlflow.transformers.log_model( | |
| with mlflow.start_run(): | |
| model_info = mlflow.transformers.log_model( |
harupy
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Apr 17, 2023
docs/source/models.rst
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| # Log the pipeline | ||
| model_info = mlflow.transformers.log_model( |
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| model_info = mlflow.transformers.log_model( | |
| with mlflow.start_run(): | |
| model_info = mlflow.transformers.log_model( |
harupy
reviewed
Apr 17, 2023
examples/transformers/simple.py
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| with mlflow.start_run() as run: | ||
| mlflow.transformers.log_model( |
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| mlflow.transformers.log_model( | |
| model_info = mlflow.transformers.log_model( |
harupy
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Apr 17, 2023
examples/transformers/simple.py
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| model_uri = f"runs:/{run.info.run_id}/sentence_builder" | ||
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| sentence_generator = mlflow.pyfunc.load_model(model_uri) |
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| model_uri = f"runs:/{run.info.run_id}/sentence_builder" | |
| sentence_generator = mlflow.pyfunc.load_model(model_uri) | |
| sentence_generator = mlflow.pyfunc.load_model(model_info.model_uri) |
harupy
approved these changes
Apr 17, 2023
Signed-off-by: Ben Wilson <benjamin.wilson@databricks.com>
Signed-off-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Add docs and examples for the transformers flavor.
How is this patch tested?
Manual validation of docs rendering
Does this PR change the documentation?
Release Notes
Is this a user-facing change?
Added examples for the transformers flavor and documentation guides to the flavor.
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