Open source platform for the machine learning lifecycle
MLflow 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.
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_paths parameter 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)
Native MLflow Image support in the log_image API: Added support for optimized image logging, including step-based iterative logging for images generated as part of a training run. This feature enables the ability to track your image generation, classification, segmentation, enhancement and object detection deep learning models effortlessly. (#11243, #11404, @jessechancy)
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_stream API 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)
Features:
predict_stream API for streamable output for Langchain models and the DatabricksDeploymentClient (#11490, #11580 @WeichenXu123)code_paths alias for code_path in pyfunc to be standardized to other flavor implementations (#11688, @BenWilson2)sentence-transformers flavor (#11635, @B-Step62)MapType support within model signatures when used with Spark udf inference (#11265, @WeichenXu123)ChatOpenAI and AzureChatOpenAI LLM interfaces within the LangChain flavor (#11644, @B-Step62)Image object for handling the logging and optimized compression of images (#11404, @jessechancy)UCVolumeDatasetSource (#11301, @chenmoneygithub)mlflow.Image files within tables (#11535, @jessechancy)chat & chat streaming for 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)LangChain loading functions to handle uncorrectable pickle-related exceptions that are thrown when loading a model in certain versions (#11582, @B-Step62)sklearn flavor to reintroduce support for custom prediction methods (#11577, @B-Step62)langchain flavor (#11485, @WeichenXu123)transformers models that contain custom code (#11412, @daniellok-db)transformers flavor that generates an inconsistent input example display within the MLflow UI (#11508, @B-Step62)keras autologging training dataset generator (#11383, @WeichenXu123)GetSampledHistoryBulkInterval API to produce more consistent results when displayed within the UI (#11475, @daniellok-db)langchain and lanchain_community within langchain models when logging (#11450, @sunishsheth2009)Documentation updates:
code_paths docstrings in API documentation (#11675, @BenWilson2)sentence-transformers OpenAI-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
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:
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.
New features: [Models] Copy Model Metadata into the "metadata" subdirectory within artifact store (#11357 @WeichenXu123)
Bug fixes:
[Security] Address LFI exploit (#11376, @WeichenXu123) [Models] Fix transformers models implementation to allow for custom model and component definitions to be loaded properly (#11412, #11428 @daniellok-db) [Models] Fix the LangChain flavor implementation to support defining an MLflow model as code (#11370, @sunishsheth2009) [Models] Fix LangChain VectorSearch parsing errors (#11438, @victorsun123) [Models] Fix LangChain import issue with the community package (#11450, @sunishsheth2009) [Models] Fix serialization errors with RunnableAssign in the LangChain flavor (#11358, @serena-ruan) [Models] Address import issues with LangChain community for Databricks models (#11350, @liangz1) [Registry] Fix model metadata sharing within Databricks Unity Catalog (#11392 @WeichenXu123) 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;
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
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.transformers flavor. In addition to PEFT support, we've removed the restrictions on Pipeline types
that 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 ChatModel interface 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_logging for 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 enforcing ChatModel outputs so that all required external references are included in the model object instance (#11150, @daniellok-db)torch.dtype as a string was not being applied correctly to the underlying transformers model (#11297, #11295, @harupy)mlflow.evaluate col_mapping bug for non-LLM/custom metrics, ensuring accurate evaluation and metric calculation (#11156, @sunishsheth2009)TensorInfo TypeError exception message issue, ensuring clarity and accuracy in error reporting for users (#10953, @leecs0503)RestException objects to be picklable, improving their usability in distributed computing scenarios where serialization is essential (#10936, @WeichenXu123)io.delta:delta-spark_2.12:3.0.0 dependency 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;
MLflow 2.10.2 is a patch release.
Small bug fixes and documentation updates:
#11065, @WeichenXu123
MLflow 2.10.1 is a patch release, containing fixes for various bugs in the transformers and langchain flavors, the MLflow UI, and the S3 artifact store. More details can be found in the patch notes below.
Bug fixes:
predict() on a pyfunc-loaded Text2TextGeneration pipeline would fail for string and List[string] inputs. (#10960, @B-Step62)Documentation updates:
Small bug fixes and documentation updates:
#10930, #11005, @serena-ruan; #10927, @harupy
In MLflow 2.10, we're introducing a number of significant new features that are preparing the way for current and future enhanced support for Deep Learning use cases, new features to support a broadened support for GenAI applications, and some quality of life improvements for the MLflow Deployments Server (formerly the AI Gateway).
We have a new home. The new site landing page is fresh, modern, and contains more content than ever. We're adding new content and blogs all of the time.
Objects and Arrays are now available as configurable input and output schema elements. These new types are particularly useful for GenAI-focused flavors that can have complex input and output types. See the new Signature and Input Example documentation to learn more about how to use these new signature types.
LangChain has autologging support now! When you invoke a chain, with autologging enabled, we will automatically log most chain implementations, recording and storing your configured LLM application for you. See the new Langchain documentation to learn more about how to use this feature.
The MLflow transformers flavor now supports prompt templates. You can now specify an application-specific set of instructions to submit to your GenAI pipeline in order to simplify, streamline, and integrate sets of system prompts to be supplied with each input request. Check out the updated guide to transformers to learn more and see examples!
The MLflow Deployments Server now supports two new requested features: (1) OpenAI endpoints that support streaming responses. You can now configure an endpoint to return realtime responses for Chat and Completions instead of waiting for the entire text contents to be completed. (2) Rate limits can now be set per endpoint in order to help control cost overrun when using SaaS models.
Continued the push for enhanced documentation, guides, tutorials, and examples by expanding on core MLflow functionality (Deployments, Signatures, and Model Dependency management), as well as entirely new pages for GenAI flavors. Check them out today!
predict API to serve as a pre-logging validator of environment compatibility. (#10759, @B-Step62)pyfunc predict (#10758, @dbczumar)Futures objects (#10715, @chenmoneygithub)login() API (#10623, @henxing)dict inputs with the messages key (#10742, @daniellok-db, @B-Step62)mlflowdbfs mounts for JohnSnowLabs flavor due to flakiness (#9872, @C-K-Loan)KeyError: 'loss' bug for the Quickstart guideline (#10886, @yanmxa)#10538, #10901, #10903, #10876, #10833, #10859, #10867, #10843, #10857, #10834, #10814, #10805, #10764, #10771, #10733, #10724, #10703, #10710, #10696, #10691, #10692, @B-Step62; #10882, #10854, #10395, #10725, #10695, #10712, #10707, #10667, #10665, #10654, #10638, #10628, @harupy; #10881, #10875, #10835, #10845, #10844, #10651, #10806, #10786, #10785, #10781, #10741, #10772, #10727, @serena-ruan; #10873, #10755, #10750, #10749, #10619, @WeichenXu123; #10877, @amueller; #10852, @QuentinAmbard; #10822, #10858, @gabrielfu; #10862, @jerrylian-db; #10840, @ernestwong-db; #10841, #10795, #10792, #10774, #10776, #10672, @BenWilson2; #10827, #10826, #10825, #10732, #10481, @michael-berk; #10828, #10680, #10629, @daniellok-db; #10799, #10800, #10578, #10782, #10783, #10723, #10464, @annzhang-db; #10803, #10731, #10708, @kriscon-db; #10797, @dbczumar; #10756, #10751, @Ankit8848; #10784, @AveshCSingh; #10769, #10763, #10717, @chenmoneygithub; #10698, @rmalani-db; #10767, @liangz1; #10682, @cdreetz; #10659, @prithvikannan; #10639, #10609, @TomeHirata
MLflow 2.9.2 is a patch release, containing several critical security fixes and configuration updates to support extremely large model artifacts.
Features:
mlflow.deployments.openai API to simplify direct access to OpenAI services through the deployments API (#10473, @prithvikannan)Security fixes:
.. path traversal queries (#10653, @B-Step62)HTTPDatasetSource (#10647, @BenWilson2)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:
Small bug fixes and documentation updates:
#10625, @BenWilson2