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Bump mlflow from 2.8.0 to 2.9.2 #86

Open dependabot[bot] opened 11 months ago

dependabot[bot] commented 11 months ago

Bumps mlflow from 2.8.0 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:

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:

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)

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