Update dependency mlflow to v2.12.1 #44
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This PR contains the following updates:
==2.10.2->==2.12.1Release Notes
mlflow/mlflow (mlflow)
v2.12.1MLflow 2.12.1 includes several major features and improvements
With this release, we're pleased to introduce several major new features that are focused on enhanced GenAI support, Deep Learning workflows involving images, expanded table logging functionality, and general usability enhancements within the UI and external integrations.
Major Features and Improvements:
PromptFlow: Introducing the new PromptFlow flavor, designed to enrich the GenAI landscape within MLflow. This feature simplifies the creation and management of dynamic prompts, enhancing user interaction with AI models and streamlining prompt engineering processes. (#11311, #11385 @brynn-code)
Enhanced Metadata Sharing for Unity Catalog: MLflow now supports the ability to share metadata (and not model weights) within Databricks Unity Catalog. When logging a model, this functionality enables the automatic duplication of metadata into a dedicated subdirectory, distinct from the model’s actual storage location, allowing for different sharing permissions and access control limits. (#11357, #11720 @WeichenXu123)
Code Paths Unification and Standardization: We have unified and standardized the
code_pathsparameter across all MLflow flavors to ensure a cohesive and streamlined user experience. This change promotes consistency and reduces complexity in the model deployment lifecycle. (#11688, @BenWilson2)ChatOpenAI and AzureChatOpenAI Support: Support for the ChatOpenAI and AzureChatOpenAI interfaces has been integrated into the LangChain flavor, facilitating seamless deployment of conversational AI models. This development opens new doors for building sophisticated and responsive chat applications leveraging cutting-edge language models. (#11644, @B-Step62)
Custom Models in Sentence-Transformers: The sentence-transformers flavor now supports custom models, allowing for a greater flexibility in deploying tailored NLP solutions. (#11635, @B-Step62)
Image Support for Log Table: With the addition of image support in
log_table, MLflow enhances its capabilities in handling rich media. This functionality allows for direct logging and visualization of images within the platform, improving the interpretability and analysis of visual data. (#11535, @jessechancy)Streaming Support for LangChain: The newly introduced
predict_streamAPI for LangChain models supports streaming outputs, enabling real-time output for chain invocation via pyfunc. This feature is pivotal for applications requiring continuous data processing and instant feedback. (#11490, #11580 @WeichenXu123)Security Fixes:
Features:
predict_streamAPI for streamable output for Langchain models and theDatabricksDeploymentClient(#11490, #11580 @WeichenXu123)code_pathsalias forcode_pathinpyfuncto be standardized to other flavor implementations (#11688, @BenWilson2)sentence-transformersflavor (#11635, @B-Step62)MapTypesupport within model signatures when used with Spark udf inference (#11265, @WeichenXu123)ChatOpenAIandAzureChatOpenAILLM interfaces within the LangChain flavor (#11644, @B-Step62)Imageobject for handling the logging and optimized compression of images (#11404, @jessechancy)UCVolumeDatasetSource(#11301, @chenmoneygithub)mlflow.Imagefiles within tables (#11535, @jessechancy)chat&chat streamingfor Anthropic within the MLflow deployments server (#11195, @gabrielfu)Security fixes:
Bug fixes:
%in model names to prevent URL mangling within the UI (#11474, @daniellok-db)LangChainloading functions to handle uncorrectable pickle-related exceptions that are thrown when loading a model in certain versions (#11582, @B-Step62)sklearnflavor to reintroduce support for custom prediction methods (#11577, @B-Step62)langchainflavor (#11485, @WeichenXu123)transformersmodels that contain custom code (#11412, @daniellok-db)transformersflavor that generates an inconsistent input example display within the MLflow UI (#11508, @B-Step62)kerasautologging training dataset generator (#11383, @WeichenXu123)GetSampledHistoryBulkIntervalAPI to produce more consistent results when displayed within the UI (#11475, @daniellok-db)langchainandlanchain_communitywithinlangchainmodels when logging (#11450, @sunishsheth2009)Documentation updates:
code_pathsdocstrings in API documentation (#11675, @BenWilson2)sentence-transformersOpenAI-compatible API interfaces (#11373, @es94129)Small bug fixes and documentation updates:
#11723, @freemin7; #11722, #11721, #11690, #11717, #11685, #11689, #11607, #11581, #11516, #11511, #11358, @serena-ruan; #11718, #11673, #11676, #11680, #11671, #11662, #11659, #11654, #11633, #11628, #11620, #11610, #11605, #11604, #11600, #11603, #11598, #11572, #11576, #11555, #11563, #11539, #11532, #11528, #11525, #11514, #11513, #11509, #11457, #11501, #11500, #11459, #11446, #11443, #11442, #11433, #11430, #11420, #11419, #11416, #11418, #11417, #11415, #11408, #11325, #11327, #11313, @harupy; #11707, #11527, #11663, #11529, #11517, #11510, #11489, #11455, #11427, #11389, #11378, #11326, @B-Step62; #11715, #11714, #11665, #11626, #11619, #11437, #11429, @BenWilson2; #11699, #11692, @annzhang-db; #11693, #11533, #11396, #11392, #11386, #11380, #11381, #11343, @WeichenXu123; #11696, #11687, #11683, @chilir; #11387, #11625, #11574, #11441, #11432, #11428, #11355, #11354, #11351, #11349, #11339, #11338, #11307, @daniellok-db; #11653, #11369, #11270, @chenmoneygithub; #11666, #11588, @jessechancy; #11661, @jmjeon94; #11640, @tunjan; #11639, @minkj1992; #11589, @tlm365; #11566, #11410, @brynn-code; #11570, @lababidi; #11542, #11375, #11345, @edwardfeng-db; #11463, @taranarmo; #11506, @ernestwong-db; #11502, @fzyzcjy; #11470, @clemenskol; #11452, @jkfran; #11413, @GuyAglionby; #11438, @victorsun123; #11350, @liangz1; #11370, @sunishsheth2009; #11379, #11304, @zhouyou9505; #11321, #11323, #11322, @michael-berk; #11333, @cdancette; #11228, @TomeHirata
v2.12.0MLflow 2.12.0 has been yanked from PyPI due to an issue with packaging required JS components. MLflow 2.12.1 is its replacement.
v2.11.3Compare Source
MLflow 2.11.3 is a patch release that addresses a security exploit with the Open Source MLflow tracking server and miscellaneous Databricks integration fixes
Bug fixes:
v2.11.2Compare Source
MLflow 2.11.2 is a patch release that introduces corrections for the support of custom transformer models, resolves LangChain integration problems, and includes several fixes to enhance stability.
Bug fixes:
Small bug fixes and documentation updates:
#11321, #11323, @michael-berk; #11326, #11455, @B-Step62; #11333, @cdancette; #11373, @es94129; #11429, @BenWilson2; #11413, @GuyAglionby; #11338, #11339, #11355, #11432, #11441, @daniellok-db; #11380, #11381, #11383, #11394, @WeichenXu123; #11446, @harupy;
v2.11.1Compare Source
MLflow 2.11.1 is a patch release, containing fixes for some Databricks integrations and other various issues.
Bug fixes:
Small bug fixes and documentation updates:
#11336, #11335, @harupy; #11303, @B-Step62; #11319, @BenWilson2; #11306, @daniellok-db
v2.11.0Compare Source
MLflow 2.11.0 includes several major features and improvements
With the MLflow 2.11.0 release, we're excited to bring a series of large and impactful features that span both GenAI and Deep Learning use cases.
The MLflow Tracking UI got an overhaul to better support the review and comparison of training runs for Deep Learning workloads. From grouping to large-scale metric plotting throughout
the iterations of a DL model's training cycle, there are a large number of quality of life improvements to enhance your Deep Learning MLOps workflow.
Support for the popular PEFT library from HuggingFace is now available
in the
mlflow.transformersflavor. In addition to PEFT support, we've removed the restrictions on Pipeline typesthat can be logged to MLflow, as well as the ability to, when developing and testing models, log a transformers pipeline without copying foundational model weights. These
enhancements strive to make the transformers flavor more useful for cutting-edge GenAI models, new pipeline types, and to simplify the development process of prompt engineering, fine-tuning,
and to make iterative development faster and cheaper. Give the updated flavor a try today! (#11240, @B-Step62)
We've added support to both PyTorch and
TensorFlow for automatic model weights checkpointing (including resumption from a
previous state) for the auto logging implementations within both flavors. This highly requested feature allows you to automatically configure long-running Deep Learning training
runs to keep a safe storage of your best epoch, eliminating the risk of a failure late in training from losing the state of the model optimization. (#11197, #10935, @WeichenXu123)
We've added a new interface to Pyfunc for GenAI workloads. The new
ChatModelinterface allows for interacting with a deployed GenAI chat model as you would with any other provider.The simplified interface (no longer requiring conformance to a Pandas DataFrame input type) strives to unify the API interface experience. (#10820, @daniellok-db)
We now support Keras 3. This large overhaul of the Keras library introduced new fundamental changes to how Keras integrates with different DL frameworks, bringing with it
a host of new functionality and interoperability. To learn more, see the Keras 3.0 Tutorial
to start using the updated model flavor today! (#10830, @chenmoneygithub)
Mistral AI has been added as a native provider for the MLflow Deployments Server. You can
now create proxied connections to the Mistral AI services for completions and embeddings with their powerful GenAI models. (#11020, @thnguyendn)
We've added compatibility support for the OpenAI 1.x SDK. Whether you're using an OpenAI LLM for model evaluation or calling OpenAI within a LangChain model, you'll now be able to
utilize the 1.x family of the OpenAI SDK without having to point to deprecated legacy APIs. (#11123, @harupy)
Features:
mlflow.pyfunc.predict, enhancing data compatibility and analysis options for batch inference (#10939, @ernestwong-db)mlflow.config.enable_async_loggingfor asynchronous logging, improving log handling and system performance (#11138, @chenmoneygithub)prompt) and embeddings (input) format inputs in the scoring server, increasing model interaction flexibility (#10958, @es94129)Bug Fixes:
load_context()is called when enforcingChatModeloutputs so that all required external references are included in the model object instance (#11150, @daniellok-db)torch.dtypeas a string was not being applied correctly to the underlying transformers model (#11297, #11295, @harupy)mlflow.evaluatecol_mappingbug for non-LLM/custom metrics, ensuring accurate evaluation and metric calculation (#11156, @sunishsheth2009)TensorInfoTypeError exception message issue, ensuring clarity and accuracy in error reporting for users (#10953, @leecs0503)RestExceptionobjects to be picklable, improving their usability in distributed computing scenarios where serialization is essential (#10936, @WeichenXu123)io.delta:delta-spark_2.12:3.0.0dependency to the correct scala version, aligning dependencies with project requirements (#11149, @WeichenXu123)importlib.metadata.entry_points().get, enhancing compatibility and stability (#10752, @raphaelauv)mlflow.login(), streamlining the authentication process and improving security (#11039, @chenmoneygithub)Documentation Updates:
log_input, enriching the documentation with actionable advice and examples for effective data handling (#10956, @BenWilson2)Small bug fixes and documentation updates:
#11284, #11096, #11285, #11245, #11254, #11252, #11250, #11249, #11234, #11248, #11242, #11244, #11236, #11208, #11220, #11222, #11221, #11219, #11218, #11210, #11209, #11207, #11196, #11194, #11177, #11205, #11183, #11192, #11179, #11178, #11175, #11174, #11166, #11162, #11151, #11168, #11167, #11153, #11158, #11143, #11141, #11119, #11123, #11124, #11117, #11121, #11078, #11097, #11079, #11095, #11082, #11071, #11076, #11070, #11072, #11073, #11069, #11058, #11034, #11046, #10951, #11055, #11045, #11035, #11044, #11043, #11031, #11030, #11023, #10932, #10986, #10949, #10943, #10928, #10929, #10925, #10924, #10911, @harupy; #11289, @BenWilson2; #11290, #11145, #11125, #11098, #11053, #11006, #11001, #11011, #11007, #10985, #10944, #11231, @daniellok-db; #11276, #11280, #11275, #11263, #11247, #11257, #11258, #11256, #11224, #11211, #11182, #11059, #11056, #11048, #11008, #10923, @serena-ruan; #11129, #11086, @victorsun123; #11292, #11004, #11204, #11148, #11165, #11146, #11115, #11099, #11092, #11029, #10983, @B-Step62; #11189, #11191, #11022, #11160, #11110, #11088, #11042, #10879, #10832, #10831, #10888, #10908, @michael-berk; #10627, #11217, #11200, #10969, @liangz1; #11215, #11173, #11000, #10931, @edwardfeng-db; #11188, #10711, @TomeHirata; #11186, @xhochy; #10916, @annzhang-db; #11131, #11010, #11060, @WeichenXu123; #11063, #10981, #10889, ##11269, @chenmoneygithub; #11054, #10921, @smurching; #11018, @mingyangge-db; #10989, @minkj1992; #10796, @kriscon-db; #10984, @eltociear; #10982, @holzman; #10972, @bmuskalla; #10959, @prithvikannan; #10941, @mahesh-venkatachalam; #10915, @Cokral; #10904, @dannyfriar; #11134, @WP-LKL; #11287, @serkef;
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