[TS #1] Initial package skeleton#16298
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Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
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| # CLAUDE.md | |||
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Q: Do we need to commit this file?
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Yea this is like cursor-rule for claude code and useful asset for whoever develop with claude code. Basically whenever you use claude code in the repository, it will read this md file to understand the context and particular coding instructions.
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| "name": "mlflow-tracing", | |||
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So did we decide to go with mlflow-tracing?
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Not yet, I sent a message to the mlflow npm package owner. This one is placeholder and we can change it later.
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Can you check the prettier failure? |
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| "name": "mlflow-tracing", | ||
| "version": "0.1.0", | ||
| "description": "TypeScript implementation of MLflow Tracing SDK for LLM observability", |
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I don't have a preference but it seems we only use LLM observability in our repo now, I'll keep it for consistency:p
BenWilson2
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layout looks good and the general placement of this in the repo LGTM
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| **Complete REST API Implementation**: | ||
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| - Authentication via Bearer token from `.databrickscfg` |
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do we need to specify that this is only if databricks is the tracking server?
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Yea will instruct claude once we implement OSS server - currently I only implemented DBX export.
🛠 DevTools 🛠
Install mlflow from this PR
For Databricks, use the following command:
What changes are proposed in this pull request?
Initial commit for the basic package structure (e.g. package.json, linter, etc).
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/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?
Yesshould be selected for bug fixes, documentation updates, and other small changes.Noshould 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.