Bump mlflow from 2.7.1 to 2.9.2

[dependabot]

Bumps mlflow from 2.7.1 to 2.9.2.

Release notes

Sourced from mlflow's releases.

MLflow 2.9.2 is a patch release, containing several critical security fixes and configuration updates to support extremely large model artifacts.

Features:

• [Deployments] Add the mlflow.deployments.openai API to simplify direct access to OpenAI services through the deployments API (#10473, @​prithvikannan)
• [Server-infra] Add a new environment variable that permits disabling http redirects within the Tracking Server for enhanced security in publicly accessible tracking server deployments (#10673, @​daniellok-db)
• [Artifacts] Add environment variable configurations for both Multi-part upload and Multi-part download that permits modifying the per-chunk size to support extremely large model artifacts (#10648, @​harupy)

Security fixes:

• [Server-infra] Disable the ability to inject malicious code via manipulated YAML files by forcing YAML rendering to be performed in a secure Sandboxed mode (#10676, @​BenWilson2, #10640, @​harupy)
• [Artifacts] Prevent path traversal attacks when querying artifact URI locations by disallowing .. path traversal queries (#10653, @​B-Step62)
• [Data] Prevent a mechanism for conducting a malicious file traversal attack on Windows when using tracking APIs that interface with HTTPDatasetSource (#10647, @​BenWilson2)
• [Artifacts] Prevent a potential path traversal attack vector via encoded url traversal paths by decoding paths prior to evaluation (#10650, @​B-Step62)
• [Artifacts] Prevent the ability to conduct path traversal attacks by enforcing the use of sanitized paths with the tracking server (#10666, @​harupy)
• [Artifacts] Prevent path traversal attacks when using an FTP server as a backend store by enforcing base path declarations prior to accessing user-supplied paths (#10657, @​harupy)

Documentation updates:

• [Docs] Add an end-to-end tutorial for RAG creation and evaluation (#10661, @​AbeOmor)
• [Docs] Add Tensorflow landing page (#10646, @​chenmoneygithub)
• [Deployments / Tracking] Add endpoints to LLM evaluation docs (#10660, @​prithvikannan)
• [Examples] Add retriever evaluation tutorial for LangChain and improve the Question Generation tutorial notebook (#10419, @​liangz1)

Small bug fixes and documentation updates:

#10677, #10636, @​serena-ruan; #10652, #10649, #10641, @​harupy; #10643, #10632, @​BenWilson2

MLflow 2.9.1 is a patch release, containing a critical bug fix related to loading pyfunc models that were saved in previous versions of MLflow.

Bug fixes:

• [Models] Revert Changes to PythonModel that introduced loading issues for models saved in earlier versions of MLflow (#10626, @​BenWilson2)

Small bug fixes and documentation updates:

#10625, @​BenWilson2

MLflow 2.9.0 includes several major features and improvements.

MLflow AI Gateway deprecation (#10420, @​harupy)

The feature previously known as MLflow AI Gateway has been moved to utilize the MLflow deployments API. For guidance on migrating from the AI Gateway to the new deployments API, please see the MLflow AI Gateway Migration Guide.

MLflow Tracking docs overhaul (#10471, @​B-Step62)

The MLflow tracking docs have been overhauled. We'd like your feedback on the new tracking docs!

Security fixes

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.9.2 (2023-12-14)

MLflow 2.9.2 is a patch release, containing several critical security fixes and configuration updates to support extremely large model artifacts.

Features:

• [Deployments] Add the mlflow.deployments.openai API to simplify direct access to OpenAI services through the deployments API (#10473, @​prithvikannan)
• [Server-infra] Add a new environment variable that permits disabling http redirects within the Tracking Server for enhanced security in publicly accessible tracking server deployments (#10673, @​daniellok-db)
• [Artifacts] Add environment variable configurations for both Multi-part upload and Multi-part download that permits modifying the per-chunk size to support extremely large model artifacts (#10648, @​harupy)

Security fixes:

• [Server-infra] Disable the ability to inject malicious code via manipulated YAML files by forcing YAML rendering to be performed in a secure Sandboxed mode (#10676, @​BenWilson2, #10640, @​harupy)
• [Artifacts] Prevent path traversal attacks when querying artifact URI locations by disallowing .. path traversal queries (#10653, @​B-Step62)
• [Data] Prevent a mechanism for conducting a malicious file traversal attack on Windows when using tracking APIs that interface with HTTPDatasetSource (#10647, @​BenWilson2)
• [Artifacts] Prevent a potential path traversal attack vector via encoded url traversal paths by decoding paths prior to evaluation (#10650, @​B-Step62)
• [Artifacts] Prevent the ability to conduct path traversal attacks by enforcing the use of sanitized paths with the tracking server (#10666, @​harupy)
• [Artifacts] Prevent path traversal attacks when using an FTP server as a backend store by enforcing base path declarations prior to accessing user-supplied paths (#10657, @​harupy)

Documentation updates:

• [Docs] Add an end-to-end tutorial for RAG creation and evaluation (#10661, @​AbeOmor)
• [Docs] Add Tensorflow landing page (#10646, @​chenmoneygithub)
• [Deployments / Tracking] Add endpoints to LLM evaluation docs (#10660, @​prithvikannan)
• [Examples] Add retriever evaluation tutorial for LangChain and improve the Question Generation tutorial notebook (#10419, @​liangz1)

Small bug fixes and documentation updates:

#10677, #10636, @​serena-ruan; #10652, #10649, #10641, @​harupy; #10643, #10632, @​BenWilson2

2.9.1 (2023-12-07)

MLflow 2.9.1 is a patch release, containing a critical bug fix related to loading pyfunc models that were saved in previous versions of MLflow.

Bug fixes:

• [Models] Revert Changes to PythonModel that introduced loading issues for models saved in earlier versions of MLflow (#10626, @​BenWilson2)

Small bug fixes and documentation updates:

#10625, @​BenWilson2

2.9.0 (2023-12-05)

MLflow 2.9.0 includes several major features and improvements.

MLflow AI Gateway deprecation (#10420, @​harupy):

The feature previously known as MLflow AI Gateway has been moved to utilize the MLflow deployments API. For guidance on migrating from the AI Gateway to the new deployments API, please see the [MLflow AI Gateway Migration Guide](https://mlflow.org/docs/latest/llms/gateway/migration.html.

... (truncated)

Commits

• 6ca7246 Run python3 dev/update_mlflow_versions.py pre-release ... (#10679)
• 27a3b24 Create mlflow.deployments.openai (#10473)
• 7f2a686 Fix ImportError in new langchain version (#10677)
• 60e209e Add env var to disable redirects again (#10673)
• 0cd7b26 Adding Notebook for RAG Blog (#10661)
• 8ef0659 Use validated path after path validation (#10666)
• 8029113 Add sandboxed jinja2 loader for yaml rendering (#10676)
• 81deca5 Add Tensorflow landing page (#10646)
• 46910c2 Make MPU / MPD chunk size configurable via environment variables (#10648)
• f00bd70 Apply os.path.basename before . or .. check in FTPArtifactRepository ...
• Additional commits viewable in compare view

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 <dependency name> 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.