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chore(deps): update dependency mlflow to v2.14.2 #14
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This PR contains the following updates:
==2.6.0
->==2.14.2
Warning
Some dependencies could not be looked up. Check the Dependency Dashboard for more information.
Release Notes
mlflow/mlflow (mlflow)
v2.14.2
Compare Source
MLflow 2.14.2 is a patch release that includes several important bug fixes and documentation enhancements.
Bug fixes:
llm/v1/xxx
task definitions. (#12551, @B-Step62)log_model
introduced in MLflow 2.13.0 that causes Databricks DLT service to crash in some situations (#12514, @WeichenXu123)predict_stream
implementation for LangChain AgentExecutor and other non-Runnable chains (#12518, @B-Step62)predict_proba
inference method in thesklearn
flavor when loading an sklearn pipeline object aspyfunc
(#12554, @WeichenXu123)Documentation updates:
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
Compare Source
MLflow 2.14.1 is a patch release that contains several bug fixes and documentation improvements
Bug fixes:
install_mlflow=False
(#12388, @daniellok-db)Documentation updates:
Small bug fixes and documentation updates:
#12415, #12396, #12394, @harupy; #12403, #12382, @BenWilson2; #12397, @B-Step62
v2.14.0
Compare Source
MLflow 2.14.0 includes several major features and improvements that we're very excited to announce!
Major features:
Other Notable Features:
Bug fixes:
Documentation updates:
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
Compare Source
MLflow 2.13.2 is a patch release that includes several bug fixes and integration improvements to existing
features.
Features:
urllib
's connection number and max size (#12227, @chenmoneygithub)Bug fixes:
mlflow[gateway]
as dependency when usingmlflow.deployment
module (#12264, @B-Step62)/
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
Compare Source
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[langchain]
extra that installs recommended versions of langchain with MLflow (#12182, @sunishsheth2009)Bug fixes:
getUserLocalTempDir
andgetUserNFSTempDir
to replacegetReplLocalTempDir
andgetReplNFSTempDir
in databricks runtime (#12105, @WeichenXu123)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
Compare Source
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 introducedinfer_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:
Togetherai
as a supported provider for the MLflow Deployments Server (#11557, @FotiosBistas)predict_stream
API support for Python Models (#11791, @WeichenXu123)Bug fixes:
hasattr
references inAttrDict
usages (#11999, @BenWilson2)Documentation updates:
predict_stream
API (#11976, @BenWilson2)JFrog
MLflow Plugin (#11426, @yonarbel)Small bug fixes and documentation updates:
#12052, #12053, #12022, #12029, #12024, #11992, #12004, #11958, #11957, #11850, #11938, #11924, #11922, #11920, #11820, #11822, #11798, @serena-ruan; #12054, #12051, #12045, #12043, #11987, #11888, #11876, #11913, #11868, @sunishsheth2009; #12049, #12046, #12037, #11831, @dbczumar; #12047, #12038, #12020, #12021, #11970, #11968, #11967, #11965, #11963, #11941, #11956, #11953, #11934, #11921, #11454, #11836, #11826, #11793, #11790, #11776, #11765, #11763, #11746, #11748, #11740, #11735, @harupy; #12025, #12034, #12027, #11914, #11899, #11866, @BenWilson2; #12026, #11991, #11979, #11964, #11939, #11894, @daniellok-db; #11951, #11974, #11916, @annzhang-db; #12015, #11931, #11627, @jessechancy; #12014, #11917, @prithvikannan; #12012, @AveshCSingh; #12001, @yunpark93; #11984, #11983, #11977, #11977, #11949, @edwardfeng-db; #11973, @bbqiu; #11902, #11835, #11775, @B-Step62; #11845, @lababidi
v2.12.2
Compare Source
MLflow 2.12.2 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 2 minor releases.
Features:
llm/v1/embeddings
task in the Transformers flavor to unify the input and output structures for embedding models (#11795, @B-Step62)predict_stream()
for custompyfunc
models capable of returning a stream response (#11791, #11895, @WeichenXu123)mlflow.evaluate
for GenAI models (#11912, @apurva-koti)pyfunc
models (#11832, #11825, #11804, @sunishsheth2009)LangChain
and custompyfunc
models as code (#11855, #11842, @sunishsheth2009)Bug fixes:
params
are specified (#11838, @WeichenXu123)spark_udf
for inference fails due to a configuration issue (#11752, @WeichenXu123)Documentation updates:
Small bug fixes and documentation updates:
#11928, @apurva-koti; #11910, #11915, #11864, #11893, #11875, #11744, @BenWilson2; #11913, #11918, #11869, #11873, #11867, @sunishsheth2009; #11916, #11879, #11877, #11860, #11843, #11844, #11817, #11841, @annzhang-db; #11822, #11861, @serena-ruan; #11890, #11819, #11794, #11774, @B-Step62; #11880, @prithvikannan; #11833, #11818, #11954, @harupy; #11831, @dbczumar; #11812, #11816, #11800, @daniellok-db; #11788, @smurching; #11756, @IgorMilavec; #11627, @jessechancy
v2.12.1
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.
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_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)
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)Security Fixes:
Features:
predict_stream
API for streamable output for Langchain models and theDatabricksDeploymentClient
(#11490, #11580 @WeichenXu123)code_paths
alias forcode_path
inpyfunc
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
andAzureChatOpenAI
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](https://togithub.com/mlflow/Configuration
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