Bump mlflow from 2.7.1 to 2.10.2 in /runtimes/mlflow

[dependabot]

Bumps mlflow from 2.7.1 to 2.10.2.

Release notes

Sourced from mlflow's releases.

MLflow 2.10.2 is a patch release.

Small bug fixes and documentation updates:

#11065, @​WeichenXu123

MLflow 2.10.1 is a patch release, containing fixes for various bugs in the transformers and langchain flavors, the MLflow UI, and the S3 artifact store. More details can be found in the patch notes below.

Bug fixes:

• [UI] Fixed a bug that prevented datasets from showing up in the MLflow UI (#10992, @​daniellok-db)
• [Artifact Store] Fixed directory bucket region name retrieval (#10967, @​kriscon-db)
• Bug fixes for Transformers flavor
  • [Models] Fix an issue with transformer pipelines not inheriting the torch dtype specified on the model, causing pipeline inference to consume more resources than expected. (#10979, @​B-Step62)
  • [Models] Fix non-idempotent prediction due to in-place update to model-config (#11014, @​B-Step62)
  • [Models] Fixed a bug affecting prompt templating with Text2TextGeneration pipelines. Previously, calling predict() on a pyfunc-loaded Text2TextGeneration pipeline would fail for string and List[string] inputs. (#10960, @​B-Step62)
• Bug fixes for Langchain flavor
  • Fixed errors that occur when logging inputs and outputs with different lengths (#10952, @​serena-ruan)

Documentation updates:

• [Docs] Add indications of DL UI capabilities to the DL landing page (#10991, @​BenWilson2)
• [Docs] Fix incorrect logo on LLMs landing page (#11017, @​BenWilson2)

Small bug fixes and documentation updates:

#10930, #11005, @​serena-ruan; #10927, @​harupy

MLflow 2.10.0

In MLflow 2.10, we're introducing a number of significant new features that are preparing the way for current and future enhanced support for Deep Learning use cases, new features to support a broadened support for GenAI applications, and some quality of life improvements for the MLflow Deployments Server (formerly the AI Gateway).

New MLflow Website

We have a new home. The new site landing page is fresh, modern, and contains more content than ever. We're adding new content and blogs all of the time.

Model Signature Supports Objects and Arrays (#9936, @​serena-ruan)

Objects and Arrays are now available as configurable input and output schema elements. These new types are particularly useful for GenAI-focused flavors that can have complex input and output types. See the new Signature and Input Example documentation to learn more about how to use these new signature types.

Langchain Autologging (#10801, @​serena-ruan)

LangChain has autologging support now! When you invoke a chain, with autologging enabled, we will automatically log most chain implementations, recording and storing your configured LLM application for you. See the new Langchain documentation to learn more about how to use this feature.

Prompt Templating for Transformers Models (#10791, @​daniellok-db)

The MLflow transformers flavor now supports prompt templates. You can now specify an application-specific set of instructions to submit to your GenAI pipeline in order to simplify, streamline, and integrate sets of system prompts to be supplied with each input request. Check out the updated guide to transformers to learn more and see examples!

MLflow Deployments Server Enhancement (#10765, @​gabrielfu; #10779, @​TomeHirata)

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.10.2 (2024-02-09)

MLflow 2.10.2 includes several major features and improvements

Small bug fixes and documentation updates:

#11065, @​WeichenXu123

2.10.1 (2024-02-06)

MLflow 2.10.1 is a patch release, containing fixes for various bugs in the transformers and langchain flavors, the MLflow UI, and the S3 artifact store. More details can be found in the patch notes below.

Bug fixes:

• [UI] Fixed a bug that prevented datasets from showing up in the MLflow UI (#10992, @​daniellok-db)
• [Artifact Store] Fixed directory bucket region name retrieval (#10967, @​kriscon-db)
• Bug fixes for Transformers flavor
  • [Models] Fix an issue with transformer pipelines not inheriting the torch dtype specified on the model, causing pipeline inference to consume more resources than expected. (#10979, @​B-Step62)
  • [Models] Fix non-idempotent prediction due to in-place update to model-config (#11014, @​B-Step62)
  • [Models] Fixed a bug affecting prompt templating with Text2TextGeneration pipelines. Previously, calling predict() on a pyfunc-loaded Text2TextGeneration pipeline would fail for string and List[string] inputs. (#10960, @​B-Step62)
• Bug fixes for Langchain flavor
  • Fixed errors that occur when logging inputs and outputs with different lengths (#10952, @​serena-ruan)

Documentation updates:

• [Docs] Add indications of DL UI capabilities to the DL landing page (#10991, @​BenWilson2)
• [Docs] Fix incorrect logo on LLMs landing page (#11017, @​BenWilson2)

Small bug fixes and documentation updates:

#10930, #11005, @​serena-ruan; #10927, @​harupy

2.10.0 (2024-01-26)

MLflow 2.10.0 includes several major features and improvements

In MLflow 2.10, we're introducing a number of significant new features that are preparing the way for current and future enhanced support for Deep Learning use cases, new features to support a broadened support for GenAI applications, and some quality of life improvements for the MLflow Deployments Server (formerly the AI Gateway).

Our biggest features this release are:

• We have a new home. The new site landing page is fresh, modern, and contains more content than ever. We're adding new content and blogs all of the time.

• Objects and Arrays are now available as configurable input and output schema elements. These new types are particularly useful for GenAI-focused flavors that can have complex input and output types. See the new Signature and Input Example documentation to learn more about how to use these new signature types.

• LangChain has autologging support now! When you invoke a chain, with autologging enabled, we will automatically log most chain implementations, recording and storing your configured LLM application for you. See the new Langchain documentation to learn more about how to use this feature.

• The MLflow transformers flavor now supports prompt templates. You can now specify an application-specific set of instructions to submit to your GenAI pipeline in order to simplify, streamline, and integrate sets of system prompts to be supplied with each input request. Check out the updated guide to transformers to learn more and see examples!

• The MLflow Deployments Server now supports two new requested features: (1) OpenAI endpoints that support streaming responses. You can now configure an endpoint to return realtime responses for Chat and Completions instead of waiting for the entire text contents to be completed. (2) Rate limits can now be set per endpoint in order to help control cost overrun when using SaaS models.

... (truncated)

Commits

• d77cc7a Run python3 dev/update_mlflow_versions.py pre-release ... (#11067)
• 64fc04c Cherry pick commits of removing legacy databricks-cli dependency (#11065)
• e5b4c50 Run python3 dev/update_mlflow_versions.py pre-release ...
• 4ca03d2 Improve langchain autologging doc (#10930)
• 6dbf334 Fix test_client_can_be_serialized_with_pickle (#10927)
• 1c7841e Fix inputs & outputs log for langchain autologging (#10952)
• e08a0b3 Fix missing dtype issue for transformer pipeline (#10979)
• 693a5a5 Add indications of DL UI capabilities to the DL landing page (#10991)
• fad7578 Fix incorrect logo on LLMs landing page (#11017)
• b851b55 Update rag notebook (#11005)
• Additional commits viewable in compare view

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[dependabot]

Looks like mlflow is up-to-date now, so this is no longer needed.