search

Azure-Samples / azure-search-power-skills

A collection of useful functions to be deployed as custom skills for Azure Cognitive Search
MIT License
269 stars 163 forks source link

Bump mlflow from 2.6.0 to 2.8.1 in /Template/PythonFastAPI #149

Closed dependabot[bot] closed 7 months ago

dependabot[bot] commented 7 months ago

Bumps mlflow from 2.6.0 to 2.8.1.

Release notes

Sourced from mlflow's releases.

MLflow 2.8.1 is a patch release, containing some critical bug fixes and an update to our continued work on reworking our docs.

Notable details:

  • The API mlflow.llm.log_predictions is being marked as deprecated, as its functionality has been incorporated into mlflow.log_table. This API will be removed in the 2.9.0 release. (#10414, @​dbczumar)

Bug fixes:

  • [Artifacts] Fix a regression in 2.8.0 where downloading a single file from a registered model would fail (#10362, @​BenWilson2)
  • [Evaluate] Fix the Azure OpenAI integration for mlflow.evaluate when using LLM judge metrics (#10291, @​prithvikannan)
  • [Evaluate] Change Examples to optional for the make_genai_metric API (#10353, @​prithvikannan)
  • [Evaluate] Remove the fastapi dependency when using mlflow.evaluate for LLM results (#10354, @​prithvikannan)
  • [Evaluate] Fix syntax issues and improve the formatting for generated prompt templates (#10402, @​annzhang-db)
  • [Gateway] Fix the Gateway configuration validator pre-check for OpenAI to perform instance type validation (#10379, @​BenWilson2)
  • [Tracking] Fix an intermittent issue with hanging threads when using asynchronous logging (#10374, @​chenmoneygithub)
  • [Tracking] Add a timeout for the mlflow.login() API to catch invalid hostname configuration input errors (#10239, @​chenmoneygithub)
  • [Tracking] Add a flush operation at the conclusion of logging system metrics (#10320, @​chenmoneygithub)
  • [Models] Correct the prompt template generation logic within the Prompt Engineering UI so that the prompts can be used in the Python API (#10341, @​daniellok-db)
  • [Models] Fix an issue in the SHAP model explainability functionality within mlflow.shap.log_explanation so that duplicate or conflicting dependencies are not registered when logging (#10305, @​BenWilson2)

Documentation updates:

Small bug fixes and documentation updates:

#10367, #10359, #10358, #10340, #10310, #10276, #10277, #10247, #10260, #10220, #10263, #10259, #10219, @​harupy; #10313, #10303, #10213, #10272, #10282, #10283, #10231, #10256, #10242, #10237, #10238, #10233, #10229, #10211, #10231, #10256, #10242, #10238, #10237, #10229, #10233, #10211, @​BenWilson2; #10375, @​serena-ruan; #10330, @​Haxatron; #10342, #10249, #10249, @​B-Step62; #10355, #10301, #10286, #10257, #10236, #10270, #10236, @​prithvikannan; #10321, #10258, @​jerrylian-db; #10245, @​jessechancy; #10278, @​daniellok-db; #10244, @​gabrielfu; #10226, @​milinddethe15; #10390, @​bbqiu; #10232, @​sunishsheth2009

MLflow 2.8.0 includes several notable new features and improvements

  • The MLflow Evaluate API has had extensive feature development in this release to support LLM workflows and multiple new evaluation modalities. See the new documentation, guides, and tutorials for MLflow LLM Evaluate to learn more.
  • The MLflow Docs modernization effort has started. You will see a very different look and feel to the docs when visiting them, along with a batch of new tutorials and guides. More changes will be coming soon to the docs!
  • 4 new LLM providers have been added! Google PaLM 2, AWS Bedrock, AI21 Labs, and HuggingFace TGI can now be configured and used within the AI Gateway. Learn more in the new AI Gateway docs!

Features:

  • [Gateway] Add support for AWS Bedrock as a provider in the AI Gateway (#9598, @​andrew-christianson)
  • [Gateway] Add support for Huggingface Text Generation Inference as a provider in the AI Gateway (#10072, @​SDonkelaarGDD)
  • [Gateway] Add support for Google PaLM 2 as a provider in the AI Gateway (#9797, @​arpitjasa-db)
  • [Gateway] Add support for AI21labs as a provider in the AI Gateway (#9828, #10168, @​zhe-db)
  • [Gateway] Introduce a simplified method for setting the configuration file location for the AI Gateway via environment variable (#9822, @​danilopeixoto)
  • [Evaluate] Introduce default provided LLM evaluation metrics for MLflow evaluate (#9913, @​prithvikannan)
  • [Evaluate] Add support for evaluating inference datasets in MLflow evaluate (#9830, @​liangz1)
  • [Evaluate] Add support for evaluating single argument functions in MLflow evaluate (#9718, @​liangz1)
  • [Evaluate] Add support for Retriever LLM model type evaluation within MLflow evaluate (#10079, @​liangz1)
  • [Models] Add configurable parameter for external model saving in the ONNX flavor to address a regression (#10152, @​daniellok-db)
  • [Models] Add support for saving inference parameters in a logged model's input example (#9655, @​serena-ruan)

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.8.1 (2023-11-14)

MLflow 2.8.1 is a patch release, containing some critical bug fixes and an update to our continued work on reworking our docs.

Notable details:

  • The API mlflow.llm.log_predictions is being marked as deprecated, as its functionality has been incorporated into mlflow.log_table. This API will be removed in the 2.9.0 release. (#10414, @​dbczumar)

Bug fixes:

  • [Artifacts] Fix a regression in 2.8.0 where downloading a single file from a registered model would fail (#10362, @​BenWilson2)
  • [Evaluate] Fix the Azure OpenAI integration for mlflow.evaluate when using LLM judge metrics (#10291, @​prithvikannan)
  • [Evaluate] Change Examples to optional for the make_genai_metric API (#10353, @​prithvikannan)
  • [Evaluate] Remove the fastapi dependency when using mlflow.evaluate for LLM results (#10354, @​prithvikannan)
  • [Evaluate] Fix syntax issues and improve the formatting for generated prompt templates (#10402, @​annzhang-db)
  • [Gateway] Fix the Gateway configuration validator pre-check for OpenAI to perform instance type validation (#10379, @​BenWilson2)
  • [Tracking] Fix an intermittent issue with hanging threads when using asynchronous logging (#10374, @​chenmoneygithub)
  • [Tracking] Add a timeout for the mlflow.login() API to catch invalid hostname configuration input errors (#10239, @​chenmoneygithub)
  • [Tracking] Add a flush operation at the conclusion of logging system metrics (#10320, @​chenmoneygithub)
  • [Models] Correct the prompt template generation logic within the Prompt Engineering UI so that the prompts can be used in the Python API (#10341, @​daniellok-db)
  • [Models] Fix an issue in the SHAP model explainability functionality within mlflow.shap.log_explanation so that duplicate or conflicting dependencies are not registered when logging (#10305, @​BenWilson2)

Documentation updates:

Small bug fixes and documentation updates:

#10367, #10359, #10358, #10340, #10310, #10276, #10277, #10247, #10260, #10220, #10263, #10259, #10219, @​harupy; #10313, #10303, #10213, #10272, #10282, #10283, #10231, #10256, #10242, #10237, #10238, #10233, #10229, #10211, #10231, #10256, #10242, #10238, #10237, #10229, #10233, #10211, @​BenWilson2; #10375, @​serena-ruan; #10330, @​Haxatron; #10342, #10249, #10249, @​B-Step62; #10355, #10301, #10286, #10257, #10236, #10270, #10236, @​prithvikannan; #10321, #10258, @​jerrylian-db; #10245, @​jessechancy; #10278, @​daniellok-db; #10244, @​gabrielfu; #10226, @​milinddethe15; #10390, @​bbqiu; #10232, @​sunishsheth2009

2.8.0 (2023-10-28)

MLflow 2.8.0 includes several notable new features and improvements

  • The MLflow Evaluate API has had extensive feature development in this release to support LLM workflows and multiple new evaluation modalities. See the new documentation, guides, and tutorials for MLflow LLM Evaluate to learn more.
  • The MLflow Docs modernization effort has started. You will see a very different look and feel to the docs when visiting them, along with a batch of new tutorials and guides. More changes will be coming soon to the docs!
  • 4 new LLM providers have been added! Google PaLM 2, AWS Bedrock, AI21 Labs, and HuggingFace TGI can now be configured and used within the AI Gateway. Learn more in the new AI Gateway docs!

Features:

  • [Gateway] Add support for AWS Bedrock as a provider in the AI Gateway (#9598, @​andrew-christianson)
  • [Gateway] Add support for Huggingface Text Generation Inference as a provider in the AI Gateway (#10072, @​SDonkelaarGDD)
  • [Gateway] Add support for Google PaLM 2 as a provider in the AI Gateway (#9797, @​arpitjasa-db)
  • [Gateway] Add support for AI21labs as a provider in the AI Gateway (#9828, #10168, @​zhe-db)
  • [Gateway] Introduce a simplified method for setting the configuration file location for the AI Gateway via environment variable (#9822, @​danilopeixoto)
  • [Evaluate] Introduce default provided LLM evaluation metrics for MLflow evaluate (#9913, @​prithvikannan)
  • [Evaluate] Add support for evaluating inference datasets in MLflow evaluate (#9830, @​liangz1)

... (truncated)

Commits


Dependabot compatibility score

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/Azure-Samples/azure-search-power-skills/network/alerts).