Update dependency mlflow to v2.20.3 #20
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This PR contains the following updates:
==2.16.2->==2.20.3Release Notes
mlflow/mlflow (mlflow)
v2.20.3Compare Source
MLflow 2.20.3 is a patch release includes several major features and improvements
Features:
Bug fixes:
astream_eventAPI (#14598, @B-Step62)Small bug fixes and documentation updates:
#14640, #14574, #14593, @serena-ruan; #14338, #14693, #14664, #14663, #14377, @B-Step62; #14680, @JulesLandrySimard; #14388, #14685, @harupy; #14704, @brilee; #14698, #14658, @bbqiu; #14660, #14659, #14632, #14616, #14594, @TomeHirata; #14535, @njbrake
v2.20.2Compare Source
MLflow 2.20.2 is a patch release includes several bug fixes and features
Features:
Bug fixes:
Documentation updates:
Small bug fixes and documentation updates:
#14410, #14569, #14440, @harupy; #14510, #14544, #14491, #14488, @bbqiu; #14518, @serena-ruan; #14517, #14500, #14461, #14478, @TomeHirata; #14512, @shaikmoeed; #14496, #14473, #14475, @B-Step62; #14467, @seal07; #14022, #14453, #14539, @daniellok-db; #14450, @BenWilson2; #14449, @SaiMadhavanG
v2.20.1Compare Source
MLflow 2.20.1 is a patch release includes several bug fixes and features:
Features:
Bug fixes:
Other small updates:
#14337, #14382, @B-Step62; #14356, @daniellok-db, #14354, @artjen, #14360, @TomuHirata,
v2.20.0Compare Source
We are excited to announce the release of MLflow 2.20.0! This release includes a number of significant features, enhancements, and bug fixes.
Major New Features
💡Type Hint-Based Model Signature: Define your model's signature in the most Pythonic way. MLflow now supports defining a model signature based on the type hints in your
PythonModel'spredictfunction, and validating input data payloads against it. (#14182, #14168, #14130, #14100, #14099, @serena-ruan)🧠 Bedrock / Groq Tracing Support: MLflow Tracing now offers a one-line auto-tracing experience for Amazon Bedrock and Groq LLMs. Track LLM invocation within your model by simply adding
mlflow.bedrock.tracingormlflow.groq.tracingcall to the code. (#14018, @B-Step62, #14006, @anumita0203)🗒️ Inline Trace Rendering in Jupyter Notebook: MLflow now supports rendering a trace UI within the notebook where you are running models. This eliminates the need to frequently switch between the notebook and browser, creating a seamless local model debugging experience. Check out this blog post for a quick demo! (#13955, @daniellok-db)
⚡️Faster Model Validation with
uvPackage Manager: MLflow has adopted uv, a new Rust-based, super-fast Python package manager. This release adds support for the new package manager in the mlflow.models.predict API, enabling faster model environment validation. Stay tuned for more updates! (#13824, @serena-ruan)🖥️ New Chat Panel in Trace UI: THe MLflow Trace UI now shows a unified
chatpanel for LLM invocations. The update allows you to view chat messages and function calls in a rich and consistent UI across LLM providers, as well as inspect the raw input and output payloads. (#14211, @TomuHirata)Other Features:
ChatAgentbase class for defining custom python agent (#13797, @bbqiu)contextparameter optional for callingPythonModelinstance (#14059, @serena-ruan)ChatModel(#14068, @stevenchen-db)Bug fixes:
log_image(#14281, @TomeHirata)0584bdc(#14146, @daniellok-db)loaded_modelvariable (#14109, @yang-chengg)DatabricksSDKModelsArtifactRepository.list_artifactsis called on a file (#14027, @shichengzhou-db)Documentation updates:
Small bug fixes and documentation updates:
#14294, #14252, #14233, #14205, #14217, #14172, #14188, #14167, #14166, #14163, #14162, #14161, #13971, @TomeHirata; #14299, #14280, #14279, #14278, #14272, #14270, #14268, #14269, #14263, #14258, #14222, #14248, #14128, #14112, #14111, #14093, #14096, #14095, #14090, #14089, #14085, #14078, #14074, #14070, #14053, #14060, #14035, #14014, #14002, #14000, #13997, #13996, #13995, @harupy; #14298, #14286, #14249, #14276, #14259, #14242, #14254, #14232, #14207, #14206, #14185, #14196, #14193, #14173, #14164, #14159, #14165, #14152, #14151, #14126, #14069, #13987, @B-Step62; #14295, #14265, #14271, #14262, #14235, #14239, #14234, #14228, #14227, #14229, #14218, #14216, #14213, #14208, #14204, #14198, #14187, #14181, #14177, #14176, #14156, #14169, #14099, #14086, #13983, @serena-ruan; #14155, #14067, #14140, #14132, #14072, @daniellok-db; #14178, @emmanuel-ferdman; #14247, @dbczumar; #13789, #14108, @dsuhinin; #14212, @aravind-segu; #14223, #14191, #14084, @dsmilkov; #13804, @kriscon-db; #14158, @Lodewic; #14148, #14147, #14115, #14079, #14116, @WeichenXu123; #14135, @brilee; #14133, @manos02; #14121, @LeahKorol; #14025, @nojaf; #13948, @benglewis; #13942, @justsomerandomdude264; #14003, @Ajay-Satish-01; #13982, @prithvikannan; #13638, @MaxwellSalmon
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](https:/
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