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Bump mlflow from 1.10.0 to 2.13.2 in /tutorials/uv-metrics in the pip group across 1 directory #131

Open dependabot[bot] opened 5 months ago

dependabot[bot] commented 5 months ago

Bumps the pip group with 1 update in the /tutorials/uv-metrics directory: mlflow.

Updates mlflow from 1.10.0 to 2.13.2

Release notes

Sourced from mlflow's releases.

v2.13.2

MLflow 2.13.2 is a patch release that includes several bug fixes and integration improvements to existing features.

Features:

Bug fixes:

Small bug fixes and documentation updates:

#12268, #12210, @​B-Step62; #12214, @​harupy; #12223, #12226, @​annzhang-db; #12260, #12237, @​prithvikannan; #12261, @​BenWilson2; #12231, @​serena-ruan; #12238, @​sunishsheth2009

v2.13.1

MLflow 2.13.1 is a patch release that includes several bug fixes and integration improvements to existing features. New features that are introduced in this patch release are intended to provide a foundation to further major features that will be released in the next release.

Features:

  • [MLflow] Add mlflow[langchain] extra that installs recommended versions of langchain with MLflow (#12182, @​sunishsheth2009)
  • [Tracking] Adding the ability to override the model_config in langchain flavor if loaded as pyfunc (#12085, @​sunishsheth2009)
  • [Model Registry] Automatically detect if Presigned URLs are required for Unity Catalog (#12177, @​artjen)

Bug fixes:

  • [Tracking] Use getUserLocalTempDir and getUserNFSTempDir to replace getReplLocalTempDir and getReplNFSTempDir in databricks runtime (#12105, @​WeichenXu123)
  • [Model] Updating chat model to take default input_example and predict to accept json during inference (#12115, @​sunishsheth2009)
  • [Tracking] Automatically call load_context when inferring signature in pyfunc (#12099, @​sunishsheth2009)

v2.13.0

MLflow 2.13.0 includes several major features and improvements

With this release, we're happy to introduce several features that enhance the usability of MLflow broadly across a range of use cases.

Major Features and Improvements:

  • Streamable Python Models: The newly introduced predict_stream API for Python Models allows for custom model implementations that support the return of a generator object, permitting full customization for GenAI applications.

  • Enhanced Code Dependency Inference: A new feature for automatically inferrring code dependencies based on detected dependencies within a model's implementation. As a supplement to the code_paths parameter, the introduced infer_model_code_paths option when logging a model will determine which additional code modules are needed in order to ensure that your models can be loaded in isolation, deployed, and reliably stored.

  • Standardization of MLflow Deployment Server: Outputs from the Deployment Server's endpoints now conform to OpenAI's interfaces to provide a simpler integration with commonly used services.

Features:

  • [Deployments] Update the MLflow Deployment Server interfaces to be OpenAI compatible (#12003, @​harupy)
  • [Deployments] Add Togetherai as a supported provider for the MLflow Deployments Server (#11557, @​FotiosBistas)
  • [Models] Add predict_stream API support for Python Models (#11791, @​WeichenXu123)

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.13.2 (2024-06-06)

MLflow 2.13.2 is a patch release that includes several bug fixes and integration improvements to existing features.

Features:

Bug fixes:

Small bug fixes and documentation updates:

#12268, #12210, @​B-Step62; #12214, @​harupy; #12223, #12226, @​annzhang-db; #12260, #12237, @​prithvikannan; #12261, @​BenWilson2; #12231, @​serena-ruan; #12238, @​sunishsheth2009

2.13.1 (2024-05-30)

MLflow 2.13.1 is a patch release that includes several bug fixes and integration improvements to existing features. New features that are introduced in this patch release are intended to provide a foundation to further major features that will be released in the next release.

Features:

  • [MLflow] Add mlflow[langchain] extra that installs recommended versions of langchain with MLflow (#12182, @​sunishsheth2009)
  • [Tracking] Adding the ability to override the model_config in langchain flavor if loaded as pyfunc (#12085, @​sunishsheth2009)
  • [Model Registry] Automatically detect if Presigned URLs are required for Unity Catalog (#12177, @​artjen)

Bug fixes:

  • [Tracking] Use getUserLocalTempDir and getUserNFSTempDir to replace getReplLocalTempDir and getReplNFSTempDir in databricks runtime (#12105, @​WeichenXu123)
  • [Model] Updating chat model to take default input_example and predict to accept json during inference (#12115, @​sunishsheth2009)
  • [Tracking] Automatically call load_context when inferring signature in pyfunc (#12099, @​sunishsheth2009)

Small bug fixes and documentation updates:

#12180, #12152, #12128, #12126, #12100, #12086, #12084, #12079, #12071, #12067, #12062, @​serena-ruan; #12175, #12167, #12137, #12134, #12127, #12123, #12111, #12109, #12078, #12080, #12064, @​B-Step62; #12142, @​2maz; #12171, #12168, #12159, #12153, #12144, #12104, #12095, #12083, @​harupy; #12160, @​aravind-segu; #11990, @​kriscon-db; #12178, #12176, #12090, #12036, @​sunishsheth2009; #12162, #12110, #12088, #11937, #12075, @​daniellok-db; #12133, #12131, @​prithvikannan; #12132, #12035, @​annzhang-db; #12121, #12120, @​liangz1; #12122, #12094, @​dbczumar; #12098, #12055, @​mparkhe

2.13.0 (2024-05-20)

MLflow 2.13.0 includes several major features and improvements

With this release, we're happy to introduce several features that enhance the usability of MLflow broadly across a range of use cases.

Major Features and Improvements:

  • Streamable Python Models: The newly introduced predict_stream API for Python Models allows for custom model implementations that support the return of a generator object, permitting full customization for GenAI applications.

  • Enhanced Code Dependency Inference: A new feature for automatically inferrring code dependencies based on detected dependencies within a model's implementation. As a supplement to the code_paths parameter, the introduced infer_model_code_paths option when logging a model will determine which additional code modules are needed in order to ensure that your models can be loaded in isolation, deployed, and reliably stored.

... (truncated)

Commits
  • de95337 Run python3 dev/update_mlflow_versions.py pre-release ... (#12270)
  • e14c7cf Suppress trace display while loading model-as-code langchain model that inclu...
  • 96e51a7 Avoid importing mlflow.gateway at the top level of mlflow.deployment modu...
  • 80c0b6e Silence traces when logging langchain models (#12210)
  • 231740f Call _flatten_nested_params only when model_config is truthy (#12214)
  • 0e4c4f5 Use unique temp dir for model code (#12223)
  • f097aff Rename environment variables to not include "DATABRICKS" (#12260)
  • b79c425 Add alternative package names for RAG to requirements exclusion validation (#...
  • 68db950 Mock dbutils when loading model code path (#12226)
  • fd8fb05 Ignore databricks_rag_studio in package mismatch (#12231)
  • Additional commits viewable in compare view


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