Bump mlflow from 2.10.0 to 2.15.0

Open dependabot[bot] opened this issue 1 year ago • 0 comments

Bumps mlflow from 2.10.0 to 2.15.0.

Release notes

MLflow 2.15.0rc0 is a release candidate to provide users with an early preview of the major features and changes coming in the stable release. We encourage you to try out these new features, and if you encounter any issues or have suggestions, please open an issue on our GitHub repository!

Try It Out

To install the release candidate, run the following command:
pip install mlflow==2.15.0rc0
Please note that the release candidate may contain bugs or incomplete features. We encourage you to test it in a non-production environment and provide feedback on any issues you encounter.

MLflow 2.14.3 is a patch release that addresses bug fixes and additional documentation for released features

Features:

[Model Registry] Add support for server-side encryption when uploading files to AWS S3 (#12495, @artjen)

Bug fixes:

[Models] Fix stream trace logging with the OpenAI autologging implementation to record the correct chunk structure (#12629, @BenWilson2)

[Models] Fix batch inference behavior for Whisper-based translation models to allow for multiple audio file inputs (#12575, @B-Step62)

Documentation updates:

[Docs] Add documentation for OpenAI autologging (#12608, @BenWilson2)

Small bug fixes and documentation updates:

#12556, #12628, @B-Step62; #12582, #12560, @harupy; #12553, @nojaf

MLflow 2.14.2 is a patch release that includes several important bug fixes and documentation enhancements.

Bug fixes:

[Models] Fix an issue with requirements inference error handling when disabling the default warning-only behavior (#12547, @B-Step62)

[Models] Fix dependency inference issues with Transformers models saved with the unified API llm/v1/xxx task definitions. (#12551, @B-Step62)

[Models / Databricks] Fix an issue with MLlfow log_model introduced in MLflow 2.13.0 that causes Databricks DLT service to crash in some situations (#12514, @WeichenXu123)

[Models] Fix an output data structure issue with the predict_stream implementation for LangChain AgentExecutor and other non-Runnable chains (#12518, @B-Step62)

[Tracking] Fix an issue with the predict_proba inference method in the sklearn flavor when loading an sklearn pipeline object as pyfunc (#12554, @WeichenXu123)

[Tracking] Fix an issue with the Tracing implementation where other services usage of OpenTelemetry would activate MLflow tracing and cause errors (#12457, @B-Step62)

[Tracking / Databricks] Correct an issue when running dependency inference in Databricks that can cause duplicate dependency entries to be logged (#12493, @sunishsheth2009)

Documentation updates:

[Docs] Add documentation and guides for the MLflow tracing schema (#12521, @BenWilson2)

Small bug fixes and documentation updates:

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.15.0 (2024-07-29)

We are excited to announce the release candidate for MLflow 2.15.0. This release includes many major features and improvements!

Major features:

LlamaIndex Flavor🦙 - MLflow now offers a native integration with LlamaIndex, one of the most popular libraries for building GenAI apps centered around custom data. This integration allows you to log LlamaIndex indices within MLflow, allowing for the loading and deployment of your indexed data for inference tasks with different engine types. MLflow also provides comprehensive tracing support for LlamaIndex operations, offering unprecedented transparency into complex queries. Check out the MLflow LlamaIndex documentation to get started! (#12633, @michael-berk, @B-Step62)

OpenAI Tracing🔍 - We've enhanced our OpenAI integration with a new tracing feature that works seamlessly with MLflow OpenAI autologging. You can now enable tracing of their OpenAI API usage with a single mlflow.openai.autolog() call, thereby MLflow will automatically log valuable metadata such as token usage and a history of your interactions, providing deeper insights into your OpenAI-powered applications. To start exploring this new capability, please check out the tracing documentation! (#12267, @gabrielfu)

Enhanced Model Deployment with New Validation Feature✅ - To improve the reliability of model deployments, MLflow has added a new method to validate your model before deploying it to an inference endpoint. This feature helps to eliminate typical errors in input and output handling, streamlining the process of model deployment and increasing confidence in your deployed models. By catching potential issues early, you can ensure a smoother transition from development to production. (#12710, @serena-ruan)

Custom Metrics Definition Recording for Evaluations📊 - We've strengthened the flexibility of defining custom metrics for model evaluation by automatically logging and versioning metrics definitions, including models used as judges and prompt templates. With this new capability, you can ensure reproducibility of evaluations across different runs and easily reuse evaluation setups for consistency, facilitating more meaningful comparisons between different models or versions. (#12487, #12509, @xq-yin)

Databricks SDK Integration🔐 - MLflow's interaction with Databricks endpoints has been fully migrated to use the Databricks SDK. This change brings more robust and reliable connections between MLflow and Databricks, and access to the latest Databricks features and capabilities. We mark the legacy databricks-cli support as deprecated and will remove in the future release. (#12313, @WeichenXu123)

Spark VectorUDT Support💥 - MLflow's Model Signature framework now supports Spark Vector UDT (User Defined Type), enabling logging and deployment of models using Spark VectorUDT with robust type validation. (#12758, @WeichenXu123)

Other Notable Changes

Features:

[Tracking] Add parent_id as a parameter to the start_run fluent API for alternative control flows (#12721, @Flametaa)

[Tracking] Add U2M authentication support for connecting to Databricks from MLflow (#12713, @WeichenXu123)

[Tracking] Support deleting remote artifacts with mlflow gc (#12451, @M4nouel)

[Tracing] Traces can now be deleted conveniently via UI from the Traces tab in the experiments page (#12641, @daniellok-db)

[Models] Introduce additional parameters for the ChatModel interface for GenAI flavors (#12612, @WeichenXu123)

[Models] [Transformers] Support input images encoded with b64.encodebytes (#12087, @MadhuM02)

[Models Registry] Add support for AWS KMS encryption for the Unity Catalog model registry integration (#12495, @artjen)

[Models] Fix MLflow Dataset hashing logic for Pandas dataframe to use iloc for accessing rows (#12410, @julcsii)

[Models Registry] Support presigned urls without headers for artifact location (#12349, @artjen)

[UI] The experiments page in the MLflow UI has an updated look, and comes with some performance optimizations for line charts (#12641, @hubertzub-db)

[UI] Line charts can now be configured to ignore outliers in the data (#12641, @daniellok-db)

[UI] Creating compatibility with Kubeflow Dashboard UI (#12663, @cgilviadee)

[UI] Add a new section to the artifact page in the Tracking UI, which shows code snippet to validate model input format before deployment (#12729, @serena-ruan)

Bug fixes:

[Tracking] Fix the model construction bug in MLflow SHAP evaluation for scikit-learn model (#12599, @serena-ruan)

[Tracking] File store get_experiment_by_name returns all stage experiments (#12788, @serena-ruan)

[Tracking] Fix Langchain callback injection logic for async/streaming request (#12773, @B-Step62)

[Tracing] [OpenAI] Fix stream tracing for OpenAI to record the correct chunk structure (#12629, @BenWilson2)

[Tracing] [LangChain] Fix LangChain tracing bug for .batch call due to thread unsafety (#12701, @B-Step62)

[Tracing] [LangChain] Fix nested trace issue in LangChain tracing. (#12705, @B-Step62)

[Tracing] Prevent intervention between MLflow Tracing and other OpenTelemetry-based libraries (#12457, @B-Step62)

[Models] Fix log_model issue in MLflow >= 2.13 that causes databricks DLT py4j service crashing (#12514, @WeichenXu123)

[Models] [Transformers] Fix batch inference issue for Transformers Whisper model (#12575, @B-Step62)

[Models] [LangChain] Fix the empty generator issue in predict_stream for AgentExecutor and other non-Runnable chains (#12518, @B-Step62)

[Scoring] Fix Spark UDF permission denied issue in Databricks runtime (#12774, @WeichenXu123)

... (truncated)

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Jul 29 '24 11:07 dependabot[bot]