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Draft prompt registry doc #14936
Draft prompt registry doc #14936
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TODO: Add guidance of using "tags" vs "version_metadata", and how to use search box. |
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<div class="flex-column"> | ||
```python | ||
# Set a production alias for a specific version | ||
mlflow.set_prompt_alias("summarization-prompt", alias="production", version=2) |
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note: This line doesn't work due to a bug in model registry. Being fixed in #14980
```python | ||
import pandas as pd | ||
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eval_data = pd.DataFrame( |
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hope we can support list[inputs] directly in the future :)
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LGTM!
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Signed-off-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
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Signed-off-by: B-Step62 <yuki.watanabe@databricks.com> Signed-off-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com> Signed-off-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
🛠 DevTools 🛠
Install mlflow from this PR
For Databricks, use the following command:
What changes are proposed in this pull request?
Draft doc for prompt registry
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
Add doc for the new Prompt Registry.
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.