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Intel® AI Reference Models: contains Intel optimizations for running deep learning workloads on Intel® Xeon® Scalable processors and Intel® Data Center GPUs
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Bump mlflow from 2.6.0 to 2.8.1 in /datasets/cloud_data_connector/samples/azure #157

Closed dependabot[bot] closed 11 months ago

dependabot[bot] commented 12 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)

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dependabot[bot] commented 11 months ago

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