Update dependency mlflow to v2.19.0 #20
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This PR contains the following updates:
==2.16.2->==2.19.0Release Notes
mlflow/mlflow (mlflow)
v2.19.0Compare Source
We are excited to announce the release of MLflow 2.19.0! This release includes a number of significant features, enhancements, and bug fixes.
Major New Features
ChatModel enhancements - ChatModel now adopts
ChatCompletionRequestandChatCompletionResponseas its new schema. Thepredict_streaminterface usesChatCompletionChunkto deliver true streaming responses. Additionally, thecustom_inputsandcustom_outputsfields in ChatModel now utilizeAnyType, enabling support for a wider variety of data types. Note: In a future version of MLflow,ChatParams(and by extension,ChatCompletionRequest) will have the default values forn,temperature, andstreamremoved. (#13782, #13857, @stevenchen-db)Tracing improvements - MLflow Tracing now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @B-Step62)
New Tracing Integrations - MLflow Tracing now supports CrewAI and Anthropic, enabling a one-line, fully automated tracing experience. (#13903, @TomeHirata, #13851, @gabrielfu)
Any Type in model signature - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @serena-ruan)
Other Features:
update_current_traceAPI for adding tags to an active trace. (#13828, @B-Step62)trace.search_spans()method for searching spans within traces (#13984, @B-Step62)Bug fixes:
mlflow.end_runinside a MLflow run context manager (#13888, @WeichenXu123)Documentation updates:
Small bug fixes and documentation updates:
#13972, #13968, #13917, #13912, #13906, #13846, @serena-ruan; #13969, #13959, #13957, #13958, #13925, #13882, #13879, #13881, #13869, #13870, #13868, #13854, #13849, #13847, #13836, #13823, #13811, #13820, #13775, #13768, #13764, @harupy; #13960, #13914, #13862, #13892, #13916, #13918, #13915, #13878, #13891, #13863, #13859, #13850, #13844, #13835, #13818, #13762, @B-Step62; #13913, #13848, #13774, @TomeHirata; #13936, #13954, #13883, @daniellok-db; #13947, @AHB102; #13929, #13922, @Ajay-Satish-01; #13857, @stevenchen-db; #13773, @BenWilson2; #13705, @williamjamir; #13745, #13743, @WeichenXu123; #13895, @chenmoneygithub; #14023, @theBeginner86
v2.18.0Compare Source
We are excited to announce the release of MLflow 2.18.0! This release includes a number of significant features, enhancements, and bug fixes.
Python Version Update
Python 3.8 is now at an end-of-life point. With official support being dropped for this legacy version, MLflow now requires Python 3.9
as a minimum supported version.
Major New Features
🦺 Fluent API Thread/Process Safety - MLflow's fluent APIs for tracking and the model registry have been overhauled to add support for both thread and multi-process safety. You are now no longer forced to use the Client APIs for managing experiments, runs, and logging from within multiprocessing and threaded applications. (#13456, #13419, @WeichenXu123)
🧩 DSPy flavor - MLflow now supports logging, loading, and tracing of
DSPymodels, broadening the support for advanced GenAI authoring within MLflow. Check out the MLflow DSPy Flavor documentation to get started! (#13131, #13279, #13369, #13345, @chenmoneygithub, #13543, #13800, #13807, @B-Step62, #13289, @michael-berk)🖥️ Enhanced Trace UI - MLflow Tracing's UI has undergone
a significant overhaul to bring usability and quality of life updates to the experience of auditing and investigating the contents of GenAI traces, from enhanced span content rendering using markdown to a standardized span component structure, (#13685, #13357, #13242, @daniellok-db)
🚄 New Tracing Integrations - MLflow Tracing now supports DSPy, LiteLLM, and Google Gemini, enabling a one-line, fully automated tracing experience. These integrations unlock enhanced observability across a broader range of industry tools. Stay tuned for upcoming integrations and updates! (#13801, @TomeHirata, #13585, @B-Step62)
📊 Expanded LLM-as-a-Judge Support - MLflow now enhances its evaluation capabilities with support for additional providers, including
Anthropic,Bedrock,Mistral, andTogetherAI, alongside existing providers likeOpenAI. Users can now also configure proxy endpoints or self-hosted LLMs that follow the provider API specs by using the newproxy_urlandextra_headersoptions. Visit the LLM-as-a-Judge documentation for more details! (#13715, #13717, @B-Step62)⏰ Environment Variable Detection - As a helpful reminder for when you are deploying models, MLflow now detects and reminds users of environment variables set during model logging, ensuring they are configured for deployment. In addition to this, the
mlflow.models.predictutility has also been updated to include these variables in serving simulations, improving pre-deployment validation. (#13584, @serena-ruan)Breaking Changes to ChatModel Interface
ChatModel Interface Updates - As part of a broader unification effort within MLflow and services that rely on or deeply integrate
with MLflow's GenAI features, we are working on a phased approach to making a consistent and standard interface for custom GenAI
application development and usage. In the first phase (planned for release in the next few releases of MLflow), we are marking
several interfaces as deprecated, as they will be changing. These changes will be:
ChatRequest→ChatCompletionRequestto provide disambiguation for future planned request interfaces.ChatResponse→ChatCompletionResponsefor the same reason as the input interface.metadatafields withinChatRequestandChatResponse→custom_inputsandcustom_outputs, respectively.predict_streamwill be updated to enable true streaming for custom GenAI applications. Currently, it returns a generator with synchronous outputs from predict. In a future release, it will return a generator ofChatCompletionChunks, enabling asynchronous streaming. While the API call structure will remain the same, the returned data payload will change significantly, aligning with LangChain’s implementation.mlflow.models.rag_signatureswill be deprecated, merging into unifiedChatCompletionRequest,ChatCompletionResponse, andChatCompletionChunks.Other Features:
spark_udfwhen running on Databricks Serverless runtime, Databricks connect, and prebuilt python environments (#13276, #13496, @WeichenXu123)model_configparameter forpyfunc.spark_udffor customization of batch inference payload submission (#13517, @WeichenXu123)Documents (#13242, @daniellok-db)resourcesdefinitions forLangchainmodel logging (#13315, @sunishsheth2009)dependenciesfor Agent definitions (#13246, @sunishsheth2009)Bug fixes:
gccommand when deleting experiments with logged datasets (#13741, @daniellok-db)Langchain'spyfuncpredict input conversion (#13652, @serena-ruan)Optionaldataclasses that define a model's signature (#13440, @bbqiu)LangChain's autologging thread-safety behavior (#13672, @B-Step62)roleandindexas required for chat schema (#13279, @chenmoneygithub)Langchainmodels (#13610, @WeichenXu123)Documentation updates:
model_configwhen logging models as code (#13631, @sunishsheth2009)code_pathsmodel logging feature (#13702, @TomeHirata)SparkMLlog_modeldocumentation with guidance on how return probabilities from classification models (#13684, @WeichenXu123)Small bug fixes and documentation updates:
#13775, #13768, #13764, #13744, #13699, #13742, #13703, #13669, #13682, #13569, #13563, #13562, #13539, #13537, #13533, #13408, #13295, @serena-ruan; #13768, #13764, #13761, #13738, #13737, #13735, #13734, #13723, #13726, #13662, #13692, #13689, #13688, #13680, #13674, #13666, #13661, #13625, #13460, #13626, #13546, #13621, #13623, #13603, #13617, #13614, #13606, #13600, #13583, #13601, #13602, #13604, #13598, #13596, #13597, #13531, #13594, #13589, #13581, #13112, #13587, #13582, #13579, #13578, #13545, #13572, #13571, #13564, #13559, #13565, #13558, #13541, #13560, #13556, #13534, #13386, #13532, #13385, #13384, #13383, #13507, #13523, #13518, #13492, #13493, #13487, #13490, #13488, #13449, #13471, #13417, #13445, #13430, #13448, #13443, #13429, #13418, #13412, #13382, #13402, #13381, #13364, #13356, #13309, #13313, #13334, #13331, #13273, #13322, #13319, #13308, #13302, #13268, #13298, #13296, @harupy; #13705, @williamjamir; #13632, @shichengzhou-db; #13755, #13712, #13260, @BenWilson2; #13745, #13743, #13697, #13548, #13549, #13577, #13349, #13351, #13350, #13342, #13341, @WeichenXu123; #13807, #13798, #13787, #13786, #13762, #13749, #13733, #13678, #13721, #13611, #13528, #13444, #13450, #13360, #13416, #13415, #13336, #13305, #13271, @B-Step62; #13808, #13708, @smurching; #13739, @fedorkobak; #13728, #13719, #13695, #13677, @TomeHirata; #13776, #13736, #13649, #13285, #13292, #13282, #13283, #13267, @daniellok-db; #13711, @bhavya2109sharma; #13693, #13658, @aravind-segu; #13553, @dsuhinin; #13663, @gitlijian; #13657, #13629, @parag-shendye; #13630, @JohannesJungbluth; #13613, @itepifanio; #13480, @agjendem; #13627, @ilyaresh; #13592, #13410, #13358, #13233, @nojaf; #13660, #13505, @sunishsheth2009; #13414, @lmoros-DB; #13399, @Abubakar17; #13390, @KekmaTime; #13291, @michael-berk; #12511, @jgiannuzzi; #13265, @Ahar28; #13785, @Rick-McCoy; #13676, @hyolim-e; #13718, @annzhang-db; #13705, @williamjamir
v2.17.2Compare Source
MLflow 2.17.2 includes several major features and improvements
Features:
Bug fixes:
Documentation updates:
Small bug fixes and documentation updates:
#13569, @serena-ruan; #13595, @BenWilson2; #13593, @mnijhuis-dnb;
v2.17.1Compare Source
MLflow 2.17.1 includes several major features and improvements
Features:
Bug fixes:
Documentation updates:
Small bug fixes and documentation updates:
#13293, #13510, #13501, #13506, #13446, @harupy; #13341, #13342, @WeichenXu123; #13396, @dvorst; #13535, @chenmoneygithub; #13503, #13469, #13416, @B-Step62; #13519, #13516, @serena-ruan; #13504, @sunishsheth2009; #13508, @KamilStachera; #13397, @kriscon-db
v2.17.0Compare Source
We are excited to announce the release of MLflow 2.17.0! This release includes several enhancements to extend the
functionality of MLflow's ChatModel interface to further extend its versatility for handling custom GenAI application use cases.
Additionally, we've improved the interface within the tracing UI to provide a structured output for retrieved documents,
enhancing the ability to read the contents of those documents within the UI.
We're also starting the work on improving both the utility and the versatility of MLflow's evaluate functionality for GenAI,
initially with support for callable GenAI evaluation metrics.
Major Features and notifications:
ChatModel enhancements - As the GenAI-focused 'cousin' of
PythonModel,ChatModelis getting some sizable functionalityextensions. From native support for tool calling (a requirement for creating a custom agent), simpler conversions to the
internal dataclass constructs needed to interface with
ChatModelvia the introduction offrom_dictmethods to all data structures,the addition of a
metadatafield to allow for full input payload customization, handling of the newrefusalresponse type, to theinclusion of the interface type to the response structure to allow for greater integration compatibility.
(#13191, #13180, #13143, @daniellok-db, #13102, #13071, @BenWilson2)
Callable GenAI Evaluation Metrics - As the intial step in a much broader expansion of the functionalities of
mlflow.evaluateforGenAI use cases, we've converted the GenAI evaluation metrics to be callable. This allows you to use them directly in packages that support
callable GenAI evaluation metrics, as well as making it simpler to debug individual responses when prototyping solutions. (#13144, @serena-ruan)
Audio file support in the MLflow UI - You can now directly 'view' audio files that have been logged and listen to them from within the MLflow UI's
artifact viewer pane.
MLflow AI Gateway is no longer deprecated - We've decided to revert our deprecation for the AI Gateway feature. We had renamed it to the
MLflow Deployments Server, but have reconsidered and reverted the naming and namespace back to the original configuration.
Features:
Workflowsobjects to be serialized when callinglog_model()(#13277, #13305, #13336, @B-Step62)from_dict()function to ChatModel dataclasses (#13180, @daniellok-db)langchain.log_model()(#13315, @sunishsheth2009)set_retriever_schema(#13246, @sunishsheth2009)Bug fixes:
presigned_url_artifactrequests being in the wrong format (#13366, @WeichenXu123)langchain-databrickspartner package. (#13266, @B-Step62)Documentation updates:
run_idparameter within thesearch_traceAPI (#13251, @B-Step62)Small bug fixes and documentation updates:
#13372, #13271, #13243, #13226, #13190, #13230, #13208, #13130, #13045, #13094, @B-Step62; #13302, #13238, #13234, #13205, #13200, #13196, #13198, #13193, #13192, #13194, #13189, #13184, #13182, #13161, #13179, #13178, #13110, #13162, #13173, #13171, #13169, #13168, #13167, #13156, #13127, #13133, #13089, #13073, #13057, #13058, #13067, #13062, #13061, #13052, @harupy; #13295, #13219, #13038, @serena-ruan; #13176, #13164, @WeichenXu123; #13163, @gabrielfu; #13186, @varshinimuthukumar1; #13128, #13115, @nojaf; #13120, @levscaut; #13152, #13075, @BenWilson2; #13138, @tanguylefloch-veesion; #13087, [@SeanAverS](https://redirect.github
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