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)
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!
[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)
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)
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!
[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)
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Bumps mlflow from 2.3.2 to 2.8.1.
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Commits
98269ed
Deprecate mlflow.llm.log_predictions() (#10414)b0fabca
unindent gen ai metrics (#10390)646e557
Fix prompt template (#10402)b3831aa
Add reference to LLM evaluation page in Prompt Engineering UI docs (#10279)052ffa2
Test pyarrow 14 (#10359)d6eebe8
Runpython3 dev/update_mlflow_versions.py pre-release ...
(#10389)ccdf66f
Installpandoc
indevcontainer
(#10367)b3a71b2
Install GitHub CLI indevcontainer
(#10358)1156352
Fix spark jobs (#10340)a401fa0
Fix langchain test (#10313)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