chore(deps): update dependency mlflow to v2.15.1 - autoclosed #60

renovate · 2024-02-25T11:43:24Z

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
mlflow	`==2.1.1` -> `==2.15.1`

Release Notes

mlflow/mlflow (mlflow)

`v2.15.1`

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MLflow 2.15.1 is a patch release that addresses several bug fixes.

Bug fixes:

[Tracking] Fix silent disabling of LangChain autologging for LangChain >= 0.2.10. (#12779, @B-Step62)
[Tracking] Fix mlflow.evaluate crash on binary classification with data subset only contains single class (#12825, @serena-ruan)
[Tracking] Fix incompatibility of MLflow Tracing with LlamaIndex >= 0.10.61 (#12890, @B-Step62)
[Tracking] Record exceptions in OpenAI autolog tracing (#12841, @B-Step62)
[Tracking] Fix url with e2 proxy (#12873, @chenmoneygithub)
[Tracking] Fix regression of connecting to MLflow tracking server on other Databricks workspace (#12861, @WeichenXu123)
[UI] Fix refresh button for model metrics on Experiment and Run pages (#12869, @beomsun0829)

Documentation updates:

[Docs] Update doc for Spark ML vector type (#12827, @WeichenXu123)

Small bug fixes and documentation updates:

#12823, #12860, #12844, #12843, @B-Step62; #12863, #12828, @harupy; #12845, @djliden; #12820, @annzhang-db; #12831, @chenmoneygithub

`v2.15.0`

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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)

Documentation updates:

Add documentation on authentication for Databricks UC Model Registry (#12552, @WeichenXu123)
Adding model-from-code documentation for LangChain and Pyfunc (#12325, #12336, @sunishsheth2009)
Add FAQ entry for viewing trace exceptions (#12309, @BenWilson2)
Add note about fork vs spawn method when using multiprocessing for parallel runs (#12337, @B-Step62)
Add example usage of extract_fields for mlflow.search_traces (#12319, @xq-yin)
Replace GPT-3.5-turbo with GPT-4o-mini (#12740, #12746, @Acksout)

Small bug fixes and documentation updates:

#12727, #12709, #12685, #12667, #12673, #12602, #12601, #12655, #12641, #12635, #12634, #12584, #12428, #12388, #12352, #12298, #12750, #12727, #12757, @daniellok-db; #12726, #12733, #12691, #12622, #12579, #12581, #12285, #12311, #12357, #12339, #12338, #12705, #12797, #12787, #12784, #12771, #12737, @B-Step62; #12715, @hubertzub-db; #12722, #12804, @annzhang-db; #12676, #12680, #12665, #12664, #12671, #12651, #12649, #12647, #12637, #12632, #12603, #12343, #12328, #12286, #12793, #12770, @serena-ruan; #12670, #12613, #12473, #12506, #12485, #12477, #12468, #12464, #12443, #12807, #12800, #10874, #12761, @WeichenXu123; #12690, #12678, #12686, #12545, #12621, #12598, #12583, #12582, #12510, #12580, #12570, #12571, #12559, #12538, #12537, #12519, #12515, #12507, #12508, #12502, #12499, #12497, #12447, #12467, #12426, #12448, #12430, #12420, #12385, #12371, #12359, #12284, #12345, #12316, #12287, #12303, #12291, #12795, #12786, #12796, #12792, #12791, #12778, #12777, #12755, #12751, #12753, #12749, @harupy; #12742, #12702, #12742 @edwardfeng-db; #12605, @alxhslm; #12662, @freemso; #12577, @rafyzg; #12512, @Jaishree2310; #12491, #1274, @BenWilson2; #12549, @besarthoxhaj; #12476, @jessechancy; #12541, @amanjam; #12479, #12472, #12433, #12289, @xq-yin; #12486, #12474, #11406, @jgiannuzzi; #12463, @jsuchome; #12460, @Venki1402; #12449, @yukimori; #12318, @RistoAle97; #12440, @victolee0; #12416, @Dev-98; #11771, @lababidi; #12417, @dannikay; #12663, @cgilviadee; #12410, @julcsii; #12600, @ZTZK; #12803, @hcmturner; #12747, @michael-berk; #12342, @kriscon-db; #12766, @artjen;

`v2.14.3`

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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

`v2.14.2`

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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:

#12311, #12285, #12535, #12543, #12320, #12444, @B-Step62; #12310, #12340, @serena-ruan; #12409, #12432, #12471, #12497, #12499, @harupy; #12555, @nojaf; #12472, #12431, @xq-yin; #12530, #12529, #12528, #12527, #12526, #12524, #12531, #12523, #12525, #12522, @dbczumar; #12483, @jsuchome; #12465, #12441, @BenWilson2; #12450, @StarryZhang-whu

`v2.14.1`

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MLflow 2.14.1 is a patch release that contains several bug fixes and documentation improvements

Bug fixes:

[Models] Fix params and model_config handling for llm/v1/xxx Transformers model (#12401, @B-Step62)
[UI] Fix dark mode user preference (#12386, @daniellok-db)
[Docker] Fix docker image failing to build with install_mlflow=False (#12388, @daniellok-db)

Documentation updates:

[Docs] Add link to langchain autologging page in doc (#12398, @xq-yin)
[Docs] Add documentation for Models from Code (#12381, @BenWilson2)

Small bug fixes and documentation updates:

#12415, #12396, #12394, @harupy; #12403, #12382, @BenWilson2; #12397, @B-Step62

`v2.14.0`

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MLflow 2.14.0 includes several major features and improvements that we're very excited to announce!

Major features:

MLflow Tracing: Tracing is powerful tool designed to enhance your ability to monitor, analyze, and debug GenAI applications by allowing you to inspect the intermediate outputs generated as your application handles a request. This update comes with an automatic LangChain integration to make it as easy as possible to get started, but we've also implemented high-level fluent APIs, and low-level client APIs for users who want more control over their trace instrumentation. For more information, check out the guide in our docs!
Unity Catalog Integration: The MLflow Deployments server now has an integration with Unity Catalog, allowing you to leverage registered functions as tools for enhancing your chat application. For more information, check out this guide!
OpenAI Autologging: Autologging support has now been added for the OpenAI model flavor. With this feature, MLflow will automatically log a model upon calling the OpenAI API. Each time a request is made, the inputs and outputs will be logged as artifacts. Check out the guide for more information!

Other Notable Features:

[Models] Support input images encoded with b64.encodebytes (#12087, @MadhuM02)
[Tracking] Support async logging per X seconds (#12324, @chenmoneygithub)
[Tracking] Provide a way to set urllib's connection number and max size (#12227, @chenmoneygithub)
[Projects] Make MLflow project runner supporting submit spark job to databricks runtime >= 13 (#12139, @WeichenXu123)
[UI] Add the "description" column to the runs table (#11996, @zhouyou9505)

Bug fixes:

[Model Registry] Handle no headers presigned url (#12349, @artjen)
[Models] Fix docstring order for ChatResponse class and make object field immutable (#12305, @xq-yin)
[Databricks] Fix root user checking in get_databricks_nfs_temp_dir and get_databricks_local_temp_dir (#12186, @WeichenXu123)
[Tracking] fix _init_server process terminate hang (#12076, @zhouyou9505)
[Scoring] Fix MLflow model container and slow test CI failure (#12042, @WeichenXu123)

Documentation updates:

[Docs] Enhance documentation for autologging supported libraries (#12356, @xq-yin)
[Tracking, Docs] Adding Langchain as a code example and doc string (#12325, @sunishsheth2009)
[Tracking, Docs] Adding Pyfunc as a code example and doc string (#12336, @sunishsheth2009)
[Docs] Add FAQ entry for viewing trace exceptions in Docs (#12309, @BenWilson2)
[Docs] Add note about 'fork' vs 'spawn' method when using multiprocessing for parallel runs (#12337, @B-Step62)
[Docs] Fix type error in tracing example for function wrapping (#12338, @B-Step62)
[Docs] Add example usage of "extract_fields" for mlflow.search_traces in documentation (#12319, @xq-yin)
[Docs] Update LangChain Autologging docs (#12306, @B-Step62)
[Docs] Add Tracing documentation (#12191, @BenWilson2)

Small bug fixes and documentation updates:

#12359, #12308, #12350, #12284, #12345, #12316, #12287, #12303, #12291, #12288, #12265, #12170, #12248, #12263, #12249, #12251, #12239, #12241, #12240, #12235, #12242, #12172, #12215, #12228, #12216, #12164, #12225, #12203, #12181, #12198, #12195, #12192, #12146, #12171, #12163, #12166, #12124, #12106, #12113, #12112, #12074, #12077, #12058, @harupy; #12355, #12326, #12114, #12343, #12328, #12327, #12340, #12286, #12310, #12200, #12209, #12189, #12194, #12201, #12196, #12174, #12107, @serena-ruan; #12364, #12352, #12354, #12353, #12351, #12298, #12297, #12220, #12155, @daniellok-db; #12311, #12357, #12346, #12312, #12339, #12281, #12283, #12282, #12268, #12236, #12247, #12199, #12232, #12233, #12221, #12229, #12207, #12212, #12193, #12167, #12137, #12147, #12148, #12138, #12127, #12065, @B-Step62; #12289, #12253, #12330 @xq-yin; #11771, @lababidi; #12280, #12275, @BenWilson2; #12246, #12244, #12211, #12066, #12061, @WeichenXu123; #12278, @sunishsheth2009; #12136, @kriscon-db; #11911, @jessechancy; #12169, @hubertzub-db

`v2.13.2`

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MLflow 2.13.2 is a patch release that includes several bug fixes and integration improvements to existing
features.

Features:

[Tracking] Provide a way to set urllib's connection number and max size (#12227, @chenmoneygithub)
[Tracking] Support UC directory as MLflow MetaDataset (#12224, @chenmoneygithub)

Bug fixes:

[Models] Fix inferring mlflow[gateway] as dependency when using mlflow.deployment module (#12264, @B-Step62)
[Tracking] Flatten the model_config with / before logging as params (#12190, @sunishsheth2009)

Small bug fixes and documentation updates:

#12268, #12210, @B-Step62; #12214, @harupy; #12223, #12226, @annzhang-db; #12260, #12237, @prithvikannan; #12261, @BenWilson2; #12231, @serena-ruan; #12238, @sunishsheth2009

`v2.13.1`

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MLflow 2.13.1 is a patch release that includes several bug fixes and integration improvements to existing features. New features that are introduced in this patch release are intended to provide a foundation to further major features that will be released in the next release.

Features:

[MLflow] Add mlflow[langchain] extra that installs recommended versions of langchain with MLflow (#12182, @sunishsheth2009)
[Tracking] Adding the ability to override the model_config in langchain flavor if loaded as pyfunc (#12085, @sunishsheth2009)
[Model Registry] Automatically detect if Presigned URLs are required for Unity Catalog (#12177, @artjen)

Bug fixes:

[Tracking] Use getUserLocalTempDir and getUserNFSTempDir to replace getReplLocalTempDir and getReplNFSTempDir in databricks runtime (#12105, @WeichenXu123)
[Model] Updating chat model to take default input_example and predict to accept json during inference (#12115, @sunishsheth2009)
[Tracking] Automatically call load_context when inferring signature in pyfunc (#12099, @sunishsheth2009)

Small bug fixes and documentation updates:

#12180, #12152, #12128, #12126, #12100, #12086, #12084, #12079, #12071, #12067, #12062, @serena-ruan; #12175, #12167, #12137, #12134, #12127, #12123, #12111, #12109, #12078, #12080, #12064, @B-Step62; #12142, @2maz; #12171, #12168, #12159, #12153, #12144, #12104, #12095, #12083, @harupy; #12160, @aravind-segu; #11990, @kriscon-db; #12178, #12176, #12090, #12036, @sunishsheth2009; #12162, #12110, #12088, #11937, #12075, @daniellok-db; #12133, #12131, @prithvikannan; #12132, #12035, @annzhang-db; #12121, #12120, @liangz1; #12122, #12094, @dbczumar; #12098, #12055, @mparkhe

`v2.13.0`

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MLflow 2.13.0 includes several major features and improvements

With this release, we're happy to introduce several features that enhance the usability of MLflow broadly across a range of use cases.

Major Features and Improvements:

Streamable Python Models: The newly introduced predict_stream API for Python Models allows for custom model implementations that support the return of a generator object, permitting full customization for GenAI applications.
Enhanced Code Dependency Inference: A new feature for automatically inferrring code dependencies based on detected dependencies within a model's implementation. As a supplement to the code_paths parameter, the introduced infer_model_code_paths option when logging a model will determine which additional code modules are needed in order to ensure that your models can be loaded in isolation, deployed, and reliably stored.
Standardization of MLflow Deployment Server: Outputs from the Deployment Server's endpoints now conform to OpenAI's interfaces to provide a simpler integration with commonly used services.

Features:

[Deployments] Update the MLflow Deployment Server interfaces to be OpenAI compatible (#12003, @harupy)
[Deployments] Add Togetherai as a supported provider for the MLflow Deployments Server (#11557, @FotiosBistas)
[Models] Add predict_stream API support for Python Models (#11791, @WeichenXu123)
[Models] Enhance the capabilities of logging code dependencies for MLflow models (#11806, @WeichenXu123)
[Models] Add support for RunnableBinding models in LangChain (#11980, @serena-ruan)
[Model Registry / Databricks] Add support for renaming models registered to Unity Catalog (#11988, @artjen)
[Model Registry / Databricks] Improve the handling of searching for invalid components from Unity Catalog registered models (#11961, @artjen)
[Model Registry] Enhance retry logic and credential refresh to mitigate cloud provider token expiration failures when uploading or downloading artifacts (#11614, @artjen)
[Artifacts / Databricks] Add enhanced lineage tracking for models loaded from Unity Catalog ([#11305](https://togithub.com/mlflow/mlflow/issues/1

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chore(deps): update dependency mlflow to v2.15.1

a4669a4

renovate bot force-pushed the renovate-mlflow-2.x branch from bfb72bf to a4669a4 Compare August 15, 2024 11:17

renovate bot changed the title ~~chore(deps): update dependency mlflow to v2.15.1~~ chore(deps): update dependency mlflow to v2.15.1 - autoclosed Aug 23, 2024

renovate bot closed this Aug 23, 2024

renovate bot deleted the renovate-mlflow-2.x branch August 23, 2024 10:20

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chore(deps): update dependency mlflow to v2.15.1 - autoclosed #60

chore(deps): update dependency mlflow to v2.15.1 - autoclosed #60

renovate bot commented Feb 25, 2024 •

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chore(deps): update dependency mlflow to v2.15.1 - autoclosed #60

chore(deps): update dependency mlflow to v2.15.1 - autoclosed #60

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renovate bot commented Feb 25, 2024 • edited Loading

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renovate bot commented Feb 25, 2024 •

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