mlflow/mlflow (mlflow)
### [`v2.11.3`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2113-2024-03-21)
[Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.11.2...v2.11.3)
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:
- \[Security] Address an LFI exploit related to misuse of url parameters ([#11473](https://togithub.com/mlflow/mlflow/issues/11473), [@daniellok-db](https://togithub.com/daniellok-db))
- \[Databricks] Fix an issue with Databricks Runtime version acquisition when deploying a model using Databricks Docker Container Services ([#11483](https://togithub.com/mlflow/mlflow/issues/11483), [@WeichenXu123](https://togithub.com/WeichenXu123))
- \[Databricks] Correct an issue with credential management within Databricks Model Serving ([#11468](https://togithub.com/mlflow/mlflow/issues/11468), [@victorsun123](https://togithub.com/victorsun123))
- \[Models] Fix an issue with chat request validation for LangChain flavor ([#11478](https://togithub.com/mlflow/mlflow/issues/11478), [@BenWilson2](https://togithub.com/BenWilson2))
- \[Models] Fixes for LangChain models that are logged as code ([#11494](https://togithub.com/mlflow/mlflow/issues/11494), [#11436](https://togithub.com/mlflow/mlflow/issues/11436) [@sunishsheth2009](https://togithub.com/sunishsheth2009))
### [`v2.11.2`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2112-2024-03-19)
[Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.11.1...v2.11.2)
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:
- \[Security] Address LFI exploit ([#11376](https://togithub.com/mlflow/mlflow/issues/11376), [@WeichenXu123](https://togithub.com/WeichenXu123))
- \[Models] Fix transformers models implementation to allow for custom model and component definitions to be loaded properly ([#11412](https://togithub.com/mlflow/mlflow/issues/11412), [#11428](https://togithub.com/mlflow/mlflow/issues/11428) [@daniellok-db](https://togithub.com/daniellok-db))
- \[Models] Fix the LangChain flavor implementation to support defining an MLflow model as code ([#11370](https://togithub.com/mlflow/mlflow/issues/11370), [@sunishsheth2009](https://togithub.com/sunishsheth2009))
- \[Models] Fix LangChain VectorSearch parsing errors ([#11438](https://togithub.com/mlflow/mlflow/issues/11438), [@victorsun123](https://togithub.com/victorsun123))
- \[Models] Fix LangChain import issue with the community package ([#11450](https://togithub.com/mlflow/mlflow/issues/11450), [@sunishsheth2009](https://togithub.com/sunishsheth2009))
- \[Models] Fix serialization errors with RunnableAssign in the LangChain flavor ([#11358](https://togithub.com/mlflow/mlflow/issues/11358), [@serena-ruan](https://togithub.com/serena-ruan))
- \[Models] Address import issues with LangChain community for Databricks models ([#11350](https://togithub.com/mlflow/mlflow/issues/11350), [@liangz1](https://togithub.com/liangz1))
- \[Registry] Fix model metadata sharing within Databricks Unity Catalog ([#11357](https://togithub.com/mlflow/mlflow/issues/11357), [#11392](https://togithub.com/mlflow/mlflow/issues/11392) [@WeichenXu123](https://togithub.com/WeichenXu123))
Small bug fixes and documentation updates:
[#11321](https://togithub.com/mlflow/mlflow/issues/11321), [#11323](https://togithub.com/mlflow/mlflow/issues/11323), [@michael-berk](https://togithub.com/michael-berk); [#11326](https://togithub.com/mlflow/mlflow/issues/11326), [#11455](https://togithub.com/mlflow/mlflow/issues/11455), [@B-Step62](https://togithub.com/B-Step62); [#11333](https://togithub.com/mlflow/mlflow/issues/11333), [@cdancette](https://togithub.com/cdancette); [#11373](https://togithub.com/mlflow/mlflow/issues/11373), [@es94129](https://togithub.com/es94129); [#11429](https://togithub.com/mlflow/mlflow/issues/11429), [@BenWilson2](https://togithub.com/BenWilson2); [#11413](https://togithub.com/mlflow/mlflow/issues/11413), [@GuyAglionby](https://togithub.com/GuyAglionby); [#11338](https://togithub.com/mlflow/mlflow/issues/11338), [#11339](https://togithub.com/mlflow/mlflow/issues/11339), [#11355](https://togithub.com/mlflow/mlflow/issues/11355), [#11432](https://togithub.com/mlflow/mlflow/issues/11432), [#11441](https://togithub.com/mlflow/mlflow/issues/11441), [@daniellok-db](https://togithub.com/daniellok-db); [#11380](https://togithub.com/mlflow/mlflow/issues/11380), [#11381](https://togithub.com/mlflow/mlflow/issues/11381), [#11383](https://togithub.com/mlflow/mlflow/issues/11383), [#11394](https://togithub.com/mlflow/mlflow/issues/11394), [@WeichenXu123](https://togithub.com/WeichenXu123); [#11446](https://togithub.com/mlflow/mlflow/issues/11446), [@harupy](https://togithub.com/harupy);
### [`v2.11.1`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2111-2024-03-06)
[Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.11.0...v2.11.1)
MLflow 2.11.1 is a patch release, containing fixes for some Databricks integrations and other various issues.
Bug fixes:
- \[UI] Add git commit hash back to the run page UI ([#11324](https://togithub.com/mlflow/mlflow/issues/11324), [@daniellok-db](https://togithub.com/daniellok-db))
- \[Databricks Integration] Explicitly import vectorstores and embeddings in databricks_dependencies ([#11334](https://togithub.com/mlflow/mlflow/issues/11334), [@daniellok-db](https://togithub.com/daniellok-db))
- \[Databricks Integration] Modify DBR version parsing logic ([#11328](https://togithub.com/mlflow/mlflow/issues/11328), [@daniellok-db](https://togithub.com/daniellok-db))
Small bug fixes and documentation updates:
[#11336](https://togithub.com/mlflow/mlflow/issues/11336), [#11335](https://togithub.com/mlflow/mlflow/issues/11335), [@harupy](https://togithub.com/harupy); [#11303](https://togithub.com/mlflow/mlflow/issues/11303), [@B-Step62](https://togithub.com/B-Step62); [#11319](https://togithub.com/mlflow/mlflow/issues/11319), [@BenWilson2](https://togithub.com/BenWilson2); [#11306](https://togithub.com/mlflow/mlflow/issues/11306), [@daniellok-db](https://togithub.com/daniellok-db)
### [`v2.11.0`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2110-2024-03-01)
[Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.10.2...v2.11.0)
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](https://www.mlflow.org/docs/latest/llms/transformers/guide/index.html#peft-models-in-mlflow-transformers-flavor) 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](https://togithub.com/mlflow/mlflow/issues/11240), [@B-Step62](https://togithub.com/B-Step62))
- We've added support to both [PyTorch](https://www.mlflow.org/docs/latest/python_api/mlflow.pytorch.html#mlflow.pytorch.autolog) and
[TensorFlow](https://www.mlflow.org/docs/latest/python_api/mlflow.tensorflow.html#mlflow.tensorflow.autolog) 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](https://togithub.com/mlflow/mlflow/issues/11197), [#10935](https://togithub.com/mlflow/mlflow/issues/10935), [@WeichenXu123](https://togithub.com/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](https://togithub.com/mlflow/mlflow/issues/10820), [@daniellok-db](https://togithub.com/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](https://www.mlflow.org/docs/latest/deep-learning/keras/quickstart/quickstart_keras.html)
to start using the updated model flavor today! ([#10830](https://togithub.com/mlflow/mlflow/issues/10830), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
- [Mistral AI](https://mistral.ai/) has been added as a native [provider](https://www.mlflow.org/docs/latest/llms/deployments/index.html#providers) 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](https://togithub.com/mlflow/mlflow/issues/11020), [@thnguyendn](https://togithub.com/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](https://togithub.com/mlflow/mlflow/issues/11123), [@harupy](https://togithub.com/harupy))
Features:
- \[UI] Revamp the MLflow Tracking UI for Deep Learning workflows, offering a more intuitive and efficient user experience ([#11233](https://togithub.com/mlflow/mlflow/issues/11233), [@daniellok-db](https://togithub.com/daniellok-db))
- \[Data] Introduce the ability to log datasets without loading them into memory, optimizing resource usage and processing time ([#11172](https://togithub.com/mlflow/mlflow/issues/11172), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
- \[Models] Introduce logging frequency controls for TensorFlow, aligning it with Keras for consistent performance monitoring ([#11094](https://togithub.com/mlflow/mlflow/issues/11094), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
- \[Models] Add PySpark DataFrame support in `mlflow.pyfunc.predict`, enhancing data compatibility and analysis options for batch inference ([#10939](https://togithub.com/mlflow/mlflow/issues/10939), [@ernestwong-db](https://togithub.com/ernestwong-db))
- \[Models] Introduce new CLI commands for updating model requirements, facilitating easier maintenance, validation and updating of models without having to re-log ([#11061](https://togithub.com/mlflow/mlflow/issues/11061), [@daniellok-db](https://togithub.com/daniellok-db))
- \[Models] Update Embedding API for sentence transformers to ensure compatibility with OpenAI format, broadening model application scopes ([#11019](https://togithub.com/mlflow/mlflow/issues/11019), [@lu-wang-dl](https://togithub.com/lu-wang-dl))
- \[Models] Improve input and signature support for text-generation models, optimizing for Chat and Completions tasks ([#11027](https://togithub.com/mlflow/mlflow/issues/11027), [@es94129](https://togithub.com/es94129))
- \[Models] Enable chat and completions task outputs in the text-generation pipeline, expanding interactive capabilities ([#10872](https://togithub.com/mlflow/mlflow/issues/10872), [@es94129](https://togithub.com/es94129))
- \[Tracking] Add node id to system metrics for enhanced logging in multi-node setups, improving diagnostics and monitoring ([#11021](https://togithub.com/mlflow/mlflow/issues/11021), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
- \[Tracking] Implement `mlflow.config.enable_async_logging` for asynchronous logging, improving log handling and system performance ([#11138](https://togithub.com/mlflow/mlflow/issues/11138), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
- \[Evaluate] Enhance model evaluation with endpoint URL support, streamlining performance assessments and integrations ([#11262](https://togithub.com/mlflow/mlflow/issues/11262), [@B-Step62](https://togithub.com/B-Step62))
- \[Deployments] Implement chat & chat streaming support for Cohere, enhancing interactive model deployment capabilities ([#10976](https://togithub.com/mlflow/mlflow/issues/10976), [@gabrielfu](https://togithub.com/gabrielfu))
- \[Deployments] Enable Cohere streaming support, allowing real-time interaction functionalities for the MLflow Deployments server with the Cohere provider ([#10856](https://togithub.com/mlflow/mlflow/issues/10856), [@gabrielfu](https://togithub.com/gabrielfu))
- \[Docker / Scoring] Optimize Docker images for model serving, ensuring more efficient deployment and scalability ([#10954](https://togithub.com/mlflow/mlflow/issues/10954), [@B-Step62](https://togithub.com/B-Step62))
- \[Scoring] Support completions (`prompt`) and embeddings (`input`) format inputs in the scoring server, increasing model interaction flexibility ([#10958](https://togithub.com/mlflow/mlflow/issues/10958), [@es94129](https://togithub.com/es94129))
Bug Fixes:
- \[Model Registry] Correct the oversight of not utilizing the default credential file in model registry setups ([#11261](https://togithub.com/mlflow/mlflow/issues/11261), [@B-Step62](https://togithub.com/B-Step62))
- \[Model Registry] Address the visibility issue of aliases in the model versions table within the registered model detail page ([#11223](https://togithub.com/mlflow/mlflow/issues/11223), [@smurching](https://togithub.com/smurching))
- \[Models] Ensure `load_context()` is called when enforcing `ChatModel` outputs so that all required external references are included in the model object instance ([#11150](https://togithub.com/mlflow/mlflow/issues/11150), [@daniellok-db](https://togithub.com/daniellok-db))
- \[Models] Rectify the keras output dtype in signature mismatches, ensuring data consistency and accuracy ([#11230](https://togithub.com/mlflow/mlflow/issues/11230), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
- \[Models] Resolve spark model loading failures, enhancing model reliability and accessibility ([#11227](https://togithub.com/mlflow/mlflow/issues/11227), [@WeichenXu123](https://togithub.com/WeichenXu123))
- \[Models] Eliminate false warnings for missing signatures in Databricks, improving the user experience and model validation processes ([#11181](https://togithub.com/mlflow/mlflow/issues/11181), [@B-Step62](https://togithub.com/B-Step62))
- \[Models] Implement a timeout for signature/requirement inference during Transformer model logging, optimizing the logging process and avoiding delays ([#11037](https://togithub.com/mlflow/mlflow/issues/11037), [@B-Step62](https://togithub.com/B-Step62))
- \[Models] Address the missing dtype issue for transformer pipelines, ensuring data integrity and model accuracy ([#10979](https://togithub.com/mlflow/mlflow/issues/10979), [@B-Step62](https://togithub.com/B-Step62))
- \[Models] Correct non-idempotent predictions due to in-place updates to model-config, stabilizing model outputs ([#11014](https://togithub.com/mlflow/mlflow/issues/11014), [@B-Step62](https://togithub.com/B-Step62))
- \[Models] Fix an issue where specifying `torch.dtype` as a string was not being applied correctly to the underlying transformers model ([#11297](https://togithub.com/mlflow/mlflow/issues/11297), [#11295](https://togithub.com/mlflow/mlflow/issues/11295), [@harupy](https://togithub.com/harupy))
- \[Tracking] Fix `mlflow.evaluate` `col_mapping` bug for non-LLM/custom metrics, ensuring accurate evaluation and metric calculation ([#11156](https://togithub.com/mlflow/mlflow/issues/11156), [@sunishsheth2009](https://togithub.com/sunishsheth2009))
- \[Tracking] Resolve the `TensorInfo` TypeError exception message issue, ensuring clarity and accuracy in error reporting for users ([#10953](https://togithub.com/mlflow/mlflow/issues/10953), [@leecs0503](https://togithub.com/leecs0503))
- \[Tracking] Enhance `RestException` objects to be picklable, improving their usability in distributed computing scenarios where serialization is essential ([#10936](https://togithub.com/mlflow/mlflow/issues/10936), [@WeichenXu123](https://togithub.com/WeichenXu123))
- \[Tracking] Address the handling of unrecognized response error codes, ensuring robust error processing and improved user feedback in edge cases ([#10918](https://togithub.com/mlflow/mlflow/issues/10918), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
- \[Spark] Update hardcoded `io.delta:delta-spark_2.12:3.0.0` dependency to the correct scala version, aligning dependencies with project requirements ([#11149](https://togithub.com/mlflow/mlflow/issues/11149), [@WeichenXu123](https://togithub.com/WeichenXu123))
- \[Server-infra] Adapt to newer versions of python by avoiding `importlib.metadata.entry_points().get`, enhancing compatibility and stability ([#10752](https://togithub.com/mlflow/mlflow/issues/10752), [@raphaelauv](https://togithub.com/raphaelauv))
- \[Server-infra / Tracking] Introduce an environment variable to disable mlflow configuring logging on import, improving configurability and user control ([#11137](https://togithub.com/mlflow/mlflow/issues/11137), [@jmahlik](https://togithub.com/jmahlik))
- \[Auth] Enhance auth validation for `mlflow.login()`, streamlining the authentication process and improving security ([#11039](https://togithub.com/mlflow/mlflow/issues/11039), [@chenmoneygithub](https://togithub.com/chenmoneygithub))
Documentation Updates:
- \[Docs] Introduce a comprehensive notebook demonstrating the use of ChatModel with Transformers and Pyfunc, providing users with practical insights and guidelines for leveraging these models ([#11239](https://togithub.com/mlflow/mlflow/issues/11239), [@daniellok-db](https://togithub.com/daniellok-db))
- \[Tracking / Docs] Stabilize the dataset logging APIs, removing the experimental status ([#11229](https://togithub.com/mlflow/mlflow/issues/11229), [@dbczumar](https://togithub.com/dbczumar))
- \[Docs] Revise and update the documentation on authentication database configuration, offering clearer instructions and better support for setting up secure authentication mechanisms ([#11176](https://togithub.com/mlflow/mlflow/issues/11176), [@gabrielfu](https://togithub.com/gabrielfu))
- \[Docs] Publish a new guide and tutorial for MLflow data logging and `log_input`, enriching the documentation with actionable advice and examples for effective data handling ([#10956](https://togithub.com/mlflow/mlflow/issues/10956), [@BenWilson2](https://togithub.com/BenWilson2))
- \[Docs] Upgrade the documentation visuals by replacing low-resolution and poorly dithered GIFs with high-quality HTML5 videos, significantly enhancing the learning experience ([#11051](https://togithub.com/mlflow/mlflow/issues/11051), [@BenWilson2](https://togithub.com/BenWilson2))
- \[Docs / Examples] Correct the compatibility matrix for OpenAI in MLflow Deployments Server documentation, providing users with accurate integration details and supporting smoother deployments ([#11015](https://togithub.com/mlflow/mlflow/issues/11015), [@BenWilson2](https://togithub.com/BenWilson2))
Small bug fixes and documentation updates:
[#11284](https://togithub.com/mlflow/mlflow/issues/11284), [#11096](https://togithub.com/mlflow/mlflow/issues/11096), [#11285](https://togithub.com/mlflow/mlflow/issues/11285), [#11245](https://togithub.com/mlflow/mlflow/issues/11245), [#11254](https://togithub.com/mlflow/mlflow/issues/11254), [#11252](https://togithub.com/mlflow/mlflow/issues/11252), [#11250](https://togithub.com/mlflow/mlflow/issues/11250), [#11249](https://togithub.com/mlflow/mlflow/issues/11249), [#11234](https://togithub.com/mlflow/mlflow/issues/11234), [#11248](https://togithub.com/mlflow/mlflow/issues/11248), [#11242](https://togithub.com/mlflow/mlflow/issues/11242), [#11244](https://togithub.com/mlflow/mlflow/issues/11244), [#11236](https://togithub.com/mlflow/mlflow/issues/11236), [#11208](https://togithub.com/mlflow/mlflow/issues/11208), [#11220](https://togithub.com/mlflow/mlflow/issues/11220), [#11222](https://togithub.com/mlflow/mlflow/issues/11222), [#11221](https://togithub.com/mlflow/mlflow/issues/11221), [#11219](https://togithub.com/mlflow/mlflow/issues/11219), [#11218](https://togithub.com/mlflow/mlflow/issues/11218), [#11210](https://togithub.com/mlflow/mlflow/issues/11210), [#11209](https://togithub.com/mlflow/mlflow/issues/11209), [#11207](https://togithub.com/mlflow/mlflow/issues/11207), [#11196](https://togithub.com/mlflow/mlflow/issues/11196), [#11194](https://togithub.com/mlflow/mlflow/issues/11194), [#11177](https://togithub.com/mlflow/mlflow/issues/11177), [#11205](https://togithub.com/mlflow/mlflow/issues/11205), [#11183](https://togithub.com/mlflow/mlflow/issues/11183), [#11192](https://togithub.com/mlflow/mlflow/issues/11192), [#11179](https://togithub.com/mlflow/mlflow/issues/11179), [#11178](https://togithub.com/mlflow/mlflow/issues/11178), [#11175](https://togithub.com/mlflow/mlflow/issues/11175), [#11174](https://togithub.com/mlflow/mlflow/issues/11174), [#11166](https://togithub.com/mlflow/mlflow/issues/11166), [#11162](https://togithub.com/mlflow/mlflow/issues/11162), [#11151](https://togithub.com/mlflow/mlflow/issues/11151), [#11168](https://togithub.com/mlflow/mlflow/issues/11168), [#11167](https://togithub.com/mlflow/mlflow/issues/11167), [#11153](https://togithub.com/mlflow/mlflow/issues/11153), [#11158](https://togithub.com/mlflow/mlflow/issues/11158), [#11143](https://togithub.com/mlflow/mlflow/issues/11143), [#11141](https://togithub.com/mlflow/mlflow/issues/11141), [#11119](https://togithub.com/mlflow/mlflow/issues/11119), [#11123](https://togithub.com/mlflow/mlflow/issues/11123), [#11124](https://togithub.com/mlflow/mlflow/issues/11124), [#11117](https://togithub.com/mlflow/mlflow/issues/11117), [#11121](https://togithub.com/mlflow/mlflow/issues/11121), [#11078](https://togithub.com/mlflow/mlflow/issues/11078), [#11097](https://togithub.com/mlflow/mlflow/issues/11097), [#11079](https://togithub.com/mlflow/mlflow/issues/11079), [#11095](https://togithub.com/mlflow/mlflow/issues/11095), [#11082](https://togithub.com/mlflow/mlflow/issues/11082), [#11071](https://togithub.com/mlflow/mlflow/issues/11071), [#11076](https://togithub.com/mlflow/mlflow/issues/11076), [#11070](https://togithub.com/mlflow/mlflow/issues/11070), [#11072](https://togithub.com/mlflow/mlflow/issues/11072), [#11073](https://togithub.com/mlflow/mlflow/issues/11073), [#11069](https://togithub.com/mlflow/mlflow/issues/11069), [#11058](https://togithub.com/mlflow/mlflow/issues/11058), [#11034](https://togithub.com/mlflow/mlflow/issues/11034), [#11046](https://togithub.com/mlflow/mlflow/issues/11046), [#10951](https://togithub.com/mlflow/mlflow/issues/10951), [#11055](https://togithub.com/mlflow/mlflow/issues/11055), [#11045](https://togithub.com/mlflow/mlflow/issues/11045), [#11035](https://togithub.com/mlflow/mlflow/issues/11035), [#11044](https://togithub.com/mlflow/mlflow/issues/11044), [#11043](https://togithub.com/mlflow/mlflow/issues/11043), [#11031](https://togithub.com/mlflow/mlflow/issues/11031), [#11030](https://togithub.com/mlflow/mlflow/issues/11030), [#11023](https://togithub.com/mlflow/mlflow/issues/11023), [#10932](https://togithub.com/mlflow/mlflow/issues/10932), [#10986](https://togithub.com/mlflow/mlflow/issues/10986), [#10949](https://togithub.com/mlflow/mlflow/issues/10949), [#10943](https://togithub.com/mlflow/mlflow/issues/10943), [#10928](https://togithub.com/mlflow/mlflow/issues/10928), [#10929](https://togithub.com/mlflow/mlflow/issues/10929), [#10925](https://togithub.com/mlflow/mlflow/issues/10925), [#10924](https://togithub.com/mlflow/mlflow/issues/10924), [#10911](https://togithub.com/mlflow/mlflow/issues/10911), [@harupy](https://togithub.com/harupy); [#11289](https://togithub.com/mlflow/mlflow/issues/11289), [@BenWilson2](https://togithub.com/BenWilson2); [#11290](https://togithub.com/mlflow/mlflow/issues/11290), [#11145](https://togithub.com/mlflow/mlflow/issues/11145), [#11125](https://togithub.com/mlflow/mlflow/issues/11125), [#11098](https://togithub.com/mlflow/mlflow/issues/11098), [#11053](https://togithub.com/mlflow/mlflow/issues/11053), [#11006](https://togithub.com/mlflow/mlflow/issues/11006), [#11001](https://togithub.com/mlflow/mlflow/issues/11001), [#11011](https://togithub.com/mlflow/mlflow/issues/11011), [#11007](https://togithub.com/mlflow/mlflow/issues/11007), [#10985](https://togithub.com/mlflow/mlflow/issues/10985), [#10944](https://togithub.com/mlflow/mlflow/issues/10944), [#11231](https://togithub.com/mlflow/mlflow/issues/11231), [@daniellok-db](https://togithub.com/daniellok-db); [#11276](https://togithub.com/mlflow/mlflow/issues/11276), [#11280](https://togithub.com/mlflow/mlflow/issues/11280), [#11275](https://togithub.com/mlflow/mlflow/issues/11275), [#11263](https://togithub.com/mlflow/mlflow/issues/11263), [#11247](https://togithub.com/mlflow/mlflow/issues/11247), [#11257](https://togithub.com/mlflow/mlflow/issues/11257), [#11258](https://togithub.com/mlflow/mlflow/issues/11258), [#11256](https://togithub.com/mlflow/mlflow/issues/11256), [#11224](https://togithub.com/mlflow/mlflow/issues/11224), [#11211](https://togithub.com/mlflow/mlflow/issues/11211), [#11182](https://togithub.com/mlflow/mlflow/issues/11182), [#11059](https://togithub.com/mlflow/mlflow/issues/11059), [#11056](https://togithub.com/mlflow/mlflow/issues/11056), [#11048](https://togithub.com/mlflow/mlflow/issues/11048), [#11008](https://togithub.com/mlflow/mlflow/issues/11008), [#10923](https://togithub.com/mlflow/mlflow/issues/10923), [@serena-ruan](https://togithub.com/serena-ruan); [#11129](https://togithub.com/mlflow/mlflow/issues/11129), [#11086](https://togithub.com/mlflow/mlflow/issues/11086), [@victorsun123](https://togithub.com/victorsun123); [#11292](https://togithub.com/mlflow/mlflow/issues/11292), [#11004](https://togithub.com/mlflow/mlflow/issues/11004), [#11204](https://togithub.com/mlflow/mlflow/issues/11204), [#11148](https://togithub.com/mlflow/mlflow/issues/11148), [#11165](https://togithub.com/mlflow/mlflow/issues/11165), [#11146](https://togithub.com/mlflow/mlflow/issues/11146), [#11115](https://togithub.com/mlflow/mlflow/issues/11115), [#11099](https://togithub.com/mlflow/mlflow/issues/11099), [#11092](https://togithub.com/mlflow/mlflow/issues/11092), [#11029](https://togithub.com/mlflow/mlflow/issues/11029), [#10983](https://togithub.com/mlflow/mlflow/issues/10983), [@B-Step62](https://togithub.com/B-Step62); [#11189](https://togithub.com/mlflow/mlflow/issues/11189), [#11191](https://togithub.com/mlflow/mlflow/issues/11191), [#11022](https://togithub.com/mlflow/mlflow/issues/11022), [#11160](https://togithub.com/mlflow/mlflow/issues/11160), [#11110](https://togithub.com/mlflow/mlflow/issues/11110), [#11088](https://togithub.com/mlflow/mlflow/issues/11088), [#11042](https://togithub.com/mlflow/mlflow/issues/11042), [#10879](https://togithub.com/mlflow/mlflow/issues/10879), [#10832](https://togithub.com/mlflow/mlflow/issues/10832), [#10831](https://togithub.com/mlflow/mlflow/issues/10831), [#10888](https://togithub.com/mlflow/mlflow/issues/10888), [#10908](https://togithub.com/mlflow/mlflow/issues/10908), [@michael-berk](https://togithub.com/michael-berk); [#10627](https://togithub.com/mlflow/mlflow/issues/10627), [#11217](https://togithub.com/mlflow/mlflow/issues/11217), [#11200](https://togithub.com/mlflow/mlflow/issues/11200), [#10969](https://togithub.com/mlflow/mlflow/issues/10969), [@liangz1](https://togithub.com/liangz1); [#11215](https://togithub.com/mlflow/mlflow/issues/11215), [#11173](https://togithub.com/mlflow/mlflow/issues/11173), [#11000](https://togithub.com/mlflow/mlflow/issues/11000), [#10931](https://togithub.com/mlflow/mlflow/issues/10931), [@edwardfeng-db](https://togithub.com/edwardfeng-db); [#11188](https://togithub.com/mlflow/mlflow/issues/11188), [#10711](https://togithub.com/mlflow/mlflow/issues/10711), [@TomeHirata](https://togithub.com/TomeHirata); [#11186](https://togithub.com/mlflow/mlflow/issues/11186), [@xhochy](https://togithub.com/xhochy); [#10916](https://togithub.com/mlflow/mlflow/issues/10916), [@annzhang-db](https://togithub.com/annzhang-db); [#11131](https://togithub.com/mlflow/mlflow/issues/11131), [#11010](https://togithub.com/mlflow/mlflow/issues/11010), [#11060](https://togithub.com/mlflow/mlflow/issues/11060), [@WeichenXu123](https://togithub.com/WeichenXu123); [#11063](https://togithub.com/mlflow/mlflow/issues/11063), [#10981](https://togithub.com/mlflow/mlflow/issues/10981), [#10889](https://togithub.com/mlflow/mlflow/issues/10889), #[#11269](https://togithub.com/mlflow/mlflow/issues/11269), [@chenmoneygithub](https://togithub.com/chenmoneygithub); [#11054](https://togithub.com/mlflow/mlflow/issues/11054), [#10921](https://togithub.com/mlflow/mlflow/issues/10921), [@smurching](https://togithub.com/smurching); [#11018](https://togithub.com/mlflow/mlflow/issues/11018), [@mingyangge-db](https://togithub.com/mingyangge-db); [#10989](https://togithub.com/mlflow/mlflow/issues/10989), [@minkj1992](https://togithub.com/minkj1992); [#10796](https://togithub.com/mlflow/mlflow/issues/10796), [@kriscon-db](https://togithub.com/kriscon-db); [#10984](https://togithub.com/mlflow/mlflow/issues/10984), [@eltociear](https://togithub.com/eltociear); [#10982](https://togithub.com/mlflow/mlflow/issues/10982), [@holzman](https://togithub.com/holzman); [#10972](https://togithub.com/mlflow/mlflow/issues/10972), [@bmuskalla](https://togithub.com/bmuskalla); [#10959](https://togithub.com/mlflow/mlflow/issues/10959), [@prithvikannan](https://togithub.com/prithvikannan); [#10941](https://togithub.com/mlflow/mlflow/issues/10941), [@mahesh-venkatachalam](https://togithub.com/mahesh-venkatachalam); [#10915](https://togithub.com/mlflow/mlflow/issues/10915), [@Cokral](https://togithub.com/Cokral); [#10904](https://togithub.com/mlflow/mlflow/issues/10904), [@dannyfriar](https://togithub.com/dannyfriar); [#11134](https://togithub.com/mlflow/mlflow/issues/11134), [@WP-LKL](https://togithub.com/WP-LKL); [#11287](https://togithub.com/mlflow/mlflow/issues/11287), [@serkef](https://togithub.com/serkef);
[ ] If you want to rebase/retry this PR, check this box
This PR contains the following updates:
==2.10.2->==2.11.3By merging this PR, the issue #6 will be automatically resolved and closed:
Release Notes
mlflow/mlflow (mlflow)
### [`v2.11.3`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2113-2024-03-21) [Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.11.2...v2.11.3) 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: - \[Security] Address an LFI exploit related to misuse of url parameters ([#11473](https://togithub.com/mlflow/mlflow/issues/11473), [@daniellok-db](https://togithub.com/daniellok-db)) - \[Databricks] Fix an issue with Databricks Runtime version acquisition when deploying a model using Databricks Docker Container Services ([#11483](https://togithub.com/mlflow/mlflow/issues/11483), [@WeichenXu123](https://togithub.com/WeichenXu123)) - \[Databricks] Correct an issue with credential management within Databricks Model Serving ([#11468](https://togithub.com/mlflow/mlflow/issues/11468), [@victorsun123](https://togithub.com/victorsun123)) - \[Models] Fix an issue with chat request validation for LangChain flavor ([#11478](https://togithub.com/mlflow/mlflow/issues/11478), [@BenWilson2](https://togithub.com/BenWilson2)) - \[Models] Fixes for LangChain models that are logged as code ([#11494](https://togithub.com/mlflow/mlflow/issues/11494), [#11436](https://togithub.com/mlflow/mlflow/issues/11436) [@sunishsheth2009](https://togithub.com/sunishsheth2009)) ### [`v2.11.2`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2112-2024-03-19) [Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.11.1...v2.11.2) 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: - \[Security] Address LFI exploit ([#11376](https://togithub.com/mlflow/mlflow/issues/11376), [@WeichenXu123](https://togithub.com/WeichenXu123)) - \[Models] Fix transformers models implementation to allow for custom model and component definitions to be loaded properly ([#11412](https://togithub.com/mlflow/mlflow/issues/11412), [#11428](https://togithub.com/mlflow/mlflow/issues/11428) [@daniellok-db](https://togithub.com/daniellok-db)) - \[Models] Fix the LangChain flavor implementation to support defining an MLflow model as code ([#11370](https://togithub.com/mlflow/mlflow/issues/11370), [@sunishsheth2009](https://togithub.com/sunishsheth2009)) - \[Models] Fix LangChain VectorSearch parsing errors ([#11438](https://togithub.com/mlflow/mlflow/issues/11438), [@victorsun123](https://togithub.com/victorsun123)) - \[Models] Fix LangChain import issue with the community package ([#11450](https://togithub.com/mlflow/mlflow/issues/11450), [@sunishsheth2009](https://togithub.com/sunishsheth2009)) - \[Models] Fix serialization errors with RunnableAssign in the LangChain flavor ([#11358](https://togithub.com/mlflow/mlflow/issues/11358), [@serena-ruan](https://togithub.com/serena-ruan)) - \[Models] Address import issues with LangChain community for Databricks models ([#11350](https://togithub.com/mlflow/mlflow/issues/11350), [@liangz1](https://togithub.com/liangz1)) - \[Registry] Fix model metadata sharing within Databricks Unity Catalog ([#11357](https://togithub.com/mlflow/mlflow/issues/11357), [#11392](https://togithub.com/mlflow/mlflow/issues/11392) [@WeichenXu123](https://togithub.com/WeichenXu123)) Small bug fixes and documentation updates: [#11321](https://togithub.com/mlflow/mlflow/issues/11321), [#11323](https://togithub.com/mlflow/mlflow/issues/11323), [@michael-berk](https://togithub.com/michael-berk); [#11326](https://togithub.com/mlflow/mlflow/issues/11326), [#11455](https://togithub.com/mlflow/mlflow/issues/11455), [@B-Step62](https://togithub.com/B-Step62); [#11333](https://togithub.com/mlflow/mlflow/issues/11333), [@cdancette](https://togithub.com/cdancette); [#11373](https://togithub.com/mlflow/mlflow/issues/11373), [@es94129](https://togithub.com/es94129); [#11429](https://togithub.com/mlflow/mlflow/issues/11429), [@BenWilson2](https://togithub.com/BenWilson2); [#11413](https://togithub.com/mlflow/mlflow/issues/11413), [@GuyAglionby](https://togithub.com/GuyAglionby); [#11338](https://togithub.com/mlflow/mlflow/issues/11338), [#11339](https://togithub.com/mlflow/mlflow/issues/11339), [#11355](https://togithub.com/mlflow/mlflow/issues/11355), [#11432](https://togithub.com/mlflow/mlflow/issues/11432), [#11441](https://togithub.com/mlflow/mlflow/issues/11441), [@daniellok-db](https://togithub.com/daniellok-db); [#11380](https://togithub.com/mlflow/mlflow/issues/11380), [#11381](https://togithub.com/mlflow/mlflow/issues/11381), [#11383](https://togithub.com/mlflow/mlflow/issues/11383), [#11394](https://togithub.com/mlflow/mlflow/issues/11394), [@WeichenXu123](https://togithub.com/WeichenXu123); [#11446](https://togithub.com/mlflow/mlflow/issues/11446), [@harupy](https://togithub.com/harupy); ### [`v2.11.1`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2111-2024-03-06) [Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.11.0...v2.11.1) MLflow 2.11.1 is a patch release, containing fixes for some Databricks integrations and other various issues. Bug fixes: - \[UI] Add git commit hash back to the run page UI ([#11324](https://togithub.com/mlflow/mlflow/issues/11324), [@daniellok-db](https://togithub.com/daniellok-db)) - \[Databricks Integration] Explicitly import vectorstores and embeddings in databricks_dependencies ([#11334](https://togithub.com/mlflow/mlflow/issues/11334), [@daniellok-db](https://togithub.com/daniellok-db)) - \[Databricks Integration] Modify DBR version parsing logic ([#11328](https://togithub.com/mlflow/mlflow/issues/11328), [@daniellok-db](https://togithub.com/daniellok-db)) Small bug fixes and documentation updates: [#11336](https://togithub.com/mlflow/mlflow/issues/11336), [#11335](https://togithub.com/mlflow/mlflow/issues/11335), [@harupy](https://togithub.com/harupy); [#11303](https://togithub.com/mlflow/mlflow/issues/11303), [@B-Step62](https://togithub.com/B-Step62); [#11319](https://togithub.com/mlflow/mlflow/issues/11319), [@BenWilson2](https://togithub.com/BenWilson2); [#11306](https://togithub.com/mlflow/mlflow/issues/11306), [@daniellok-db](https://togithub.com/daniellok-db) ### [`v2.11.0`](https://togithub.com/mlflow/mlflow/blob/HEAD/CHANGELOG.md#2110-2024-03-01) [Compare Source](https://togithub.com/mlflow/mlflow/compare/v2.10.2...v2.11.0) 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](https://www.mlflow.org/docs/latest/llms/transformers/guide/index.html#peft-models-in-mlflow-transformers-flavor) 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](https://togithub.com/mlflow/mlflow/issues/11240), [@B-Step62](https://togithub.com/B-Step62)) - We've added support to both [PyTorch](https://www.mlflow.org/docs/latest/python_api/mlflow.pytorch.html#mlflow.pytorch.autolog) and [TensorFlow](https://www.mlflow.org/docs/latest/python_api/mlflow.tensorflow.html#mlflow.tensorflow.autolog) 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](https://togithub.com/mlflow/mlflow/issues/11197), [#10935](https://togithub.com/mlflow/mlflow/issues/10935), [@WeichenXu123](https://togithub.com/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](https://togithub.com/mlflow/mlflow/issues/10820), [@daniellok-db](https://togithub.com/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](https://www.mlflow.org/docs/latest/deep-learning/keras/quickstart/quickstart_keras.html) to start using the updated model flavor today! ([#10830](https://togithub.com/mlflow/mlflow/issues/10830), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) - [Mistral AI](https://mistral.ai/) has been added as a native [provider](https://www.mlflow.org/docs/latest/llms/deployments/index.html#providers) 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](https://togithub.com/mlflow/mlflow/issues/11020), [@thnguyendn](https://togithub.com/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](https://togithub.com/mlflow/mlflow/issues/11123), [@harupy](https://togithub.com/harupy)) Features: - \[UI] Revamp the MLflow Tracking UI for Deep Learning workflows, offering a more intuitive and efficient user experience ([#11233](https://togithub.com/mlflow/mlflow/issues/11233), [@daniellok-db](https://togithub.com/daniellok-db)) - \[Data] Introduce the ability to log datasets without loading them into memory, optimizing resource usage and processing time ([#11172](https://togithub.com/mlflow/mlflow/issues/11172), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) - \[Models] Introduce logging frequency controls for TensorFlow, aligning it with Keras for consistent performance monitoring ([#11094](https://togithub.com/mlflow/mlflow/issues/11094), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) - \[Models] Add PySpark DataFrame support in `mlflow.pyfunc.predict`, enhancing data compatibility and analysis options for batch inference ([#10939](https://togithub.com/mlflow/mlflow/issues/10939), [@ernestwong-db](https://togithub.com/ernestwong-db)) - \[Models] Introduce new CLI commands for updating model requirements, facilitating easier maintenance, validation and updating of models without having to re-log ([#11061](https://togithub.com/mlflow/mlflow/issues/11061), [@daniellok-db](https://togithub.com/daniellok-db)) - \[Models] Update Embedding API for sentence transformers to ensure compatibility with OpenAI format, broadening model application scopes ([#11019](https://togithub.com/mlflow/mlflow/issues/11019), [@lu-wang-dl](https://togithub.com/lu-wang-dl)) - \[Models] Improve input and signature support for text-generation models, optimizing for Chat and Completions tasks ([#11027](https://togithub.com/mlflow/mlflow/issues/11027), [@es94129](https://togithub.com/es94129)) - \[Models] Enable chat and completions task outputs in the text-generation pipeline, expanding interactive capabilities ([#10872](https://togithub.com/mlflow/mlflow/issues/10872), [@es94129](https://togithub.com/es94129)) - \[Tracking] Add node id to system metrics for enhanced logging in multi-node setups, improving diagnostics and monitoring ([#11021](https://togithub.com/mlflow/mlflow/issues/11021), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) - \[Tracking] Implement `mlflow.config.enable_async_logging` for asynchronous logging, improving log handling and system performance ([#11138](https://togithub.com/mlflow/mlflow/issues/11138), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) - \[Evaluate] Enhance model evaluation with endpoint URL support, streamlining performance assessments and integrations ([#11262](https://togithub.com/mlflow/mlflow/issues/11262), [@B-Step62](https://togithub.com/B-Step62)) - \[Deployments] Implement chat & chat streaming support for Cohere, enhancing interactive model deployment capabilities ([#10976](https://togithub.com/mlflow/mlflow/issues/10976), [@gabrielfu](https://togithub.com/gabrielfu)) - \[Deployments] Enable Cohere streaming support, allowing real-time interaction functionalities for the MLflow Deployments server with the Cohere provider ([#10856](https://togithub.com/mlflow/mlflow/issues/10856), [@gabrielfu](https://togithub.com/gabrielfu)) - \[Docker / Scoring] Optimize Docker images for model serving, ensuring more efficient deployment and scalability ([#10954](https://togithub.com/mlflow/mlflow/issues/10954), [@B-Step62](https://togithub.com/B-Step62)) - \[Scoring] Support completions (`prompt`) and embeddings (`input`) format inputs in the scoring server, increasing model interaction flexibility ([#10958](https://togithub.com/mlflow/mlflow/issues/10958), [@es94129](https://togithub.com/es94129)) Bug Fixes: - \[Model Registry] Correct the oversight of not utilizing the default credential file in model registry setups ([#11261](https://togithub.com/mlflow/mlflow/issues/11261), [@B-Step62](https://togithub.com/B-Step62)) - \[Model Registry] Address the visibility issue of aliases in the model versions table within the registered model detail page ([#11223](https://togithub.com/mlflow/mlflow/issues/11223), [@smurching](https://togithub.com/smurching)) - \[Models] Ensure `load_context()` is called when enforcing `ChatModel` outputs so that all required external references are included in the model object instance ([#11150](https://togithub.com/mlflow/mlflow/issues/11150), [@daniellok-db](https://togithub.com/daniellok-db)) - \[Models] Rectify the keras output dtype in signature mismatches, ensuring data consistency and accuracy ([#11230](https://togithub.com/mlflow/mlflow/issues/11230), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) - \[Models] Resolve spark model loading failures, enhancing model reliability and accessibility ([#11227](https://togithub.com/mlflow/mlflow/issues/11227), [@WeichenXu123](https://togithub.com/WeichenXu123)) - \[Models] Eliminate false warnings for missing signatures in Databricks, improving the user experience and model validation processes ([#11181](https://togithub.com/mlflow/mlflow/issues/11181), [@B-Step62](https://togithub.com/B-Step62)) - \[Models] Implement a timeout for signature/requirement inference during Transformer model logging, optimizing the logging process and avoiding delays ([#11037](https://togithub.com/mlflow/mlflow/issues/11037), [@B-Step62](https://togithub.com/B-Step62)) - \[Models] Address the missing dtype issue for transformer pipelines, ensuring data integrity and model accuracy ([#10979](https://togithub.com/mlflow/mlflow/issues/10979), [@B-Step62](https://togithub.com/B-Step62)) - \[Models] Correct non-idempotent predictions due to in-place updates to model-config, stabilizing model outputs ([#11014](https://togithub.com/mlflow/mlflow/issues/11014), [@B-Step62](https://togithub.com/B-Step62)) - \[Models] Fix an issue where specifying `torch.dtype` as a string was not being applied correctly to the underlying transformers model ([#11297](https://togithub.com/mlflow/mlflow/issues/11297), [#11295](https://togithub.com/mlflow/mlflow/issues/11295), [@harupy](https://togithub.com/harupy)) - \[Tracking] Fix `mlflow.evaluate` `col_mapping` bug for non-LLM/custom metrics, ensuring accurate evaluation and metric calculation ([#11156](https://togithub.com/mlflow/mlflow/issues/11156), [@sunishsheth2009](https://togithub.com/sunishsheth2009)) - \[Tracking] Resolve the `TensorInfo` TypeError exception message issue, ensuring clarity and accuracy in error reporting for users ([#10953](https://togithub.com/mlflow/mlflow/issues/10953), [@leecs0503](https://togithub.com/leecs0503)) - \[Tracking] Enhance `RestException` objects to be picklable, improving their usability in distributed computing scenarios where serialization is essential ([#10936](https://togithub.com/mlflow/mlflow/issues/10936), [@WeichenXu123](https://togithub.com/WeichenXu123)) - \[Tracking] Address the handling of unrecognized response error codes, ensuring robust error processing and improved user feedback in edge cases ([#10918](https://togithub.com/mlflow/mlflow/issues/10918), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) - \[Spark] Update hardcoded `io.delta:delta-spark_2.12:3.0.0` dependency to the correct scala version, aligning dependencies with project requirements ([#11149](https://togithub.com/mlflow/mlflow/issues/11149), [@WeichenXu123](https://togithub.com/WeichenXu123)) - \[Server-infra] Adapt to newer versions of python by avoiding `importlib.metadata.entry_points().get`, enhancing compatibility and stability ([#10752](https://togithub.com/mlflow/mlflow/issues/10752), [@raphaelauv](https://togithub.com/raphaelauv)) - \[Server-infra / Tracking] Introduce an environment variable to disable mlflow configuring logging on import, improving configurability and user control ([#11137](https://togithub.com/mlflow/mlflow/issues/11137), [@jmahlik](https://togithub.com/jmahlik)) - \[Auth] Enhance auth validation for `mlflow.login()`, streamlining the authentication process and improving security ([#11039](https://togithub.com/mlflow/mlflow/issues/11039), [@chenmoneygithub](https://togithub.com/chenmoneygithub)) Documentation Updates: - \[Docs] Introduce a comprehensive notebook demonstrating the use of ChatModel with Transformers and Pyfunc, providing users with practical insights and guidelines for leveraging these models ([#11239](https://togithub.com/mlflow/mlflow/issues/11239), [@daniellok-db](https://togithub.com/daniellok-db)) - \[Tracking / Docs] Stabilize the dataset logging APIs, removing the experimental status ([#11229](https://togithub.com/mlflow/mlflow/issues/11229), [@dbczumar](https://togithub.com/dbczumar)) - \[Docs] Revise and update the documentation on authentication database configuration, offering clearer instructions and better support for setting up secure authentication mechanisms ([#11176](https://togithub.com/mlflow/mlflow/issues/11176), [@gabrielfu](https://togithub.com/gabrielfu)) - \[Docs] Publish a new guide and tutorial for MLflow data logging and `log_input`, enriching the documentation with actionable advice and examples for effective data handling ([#10956](https://togithub.com/mlflow/mlflow/issues/10956), [@BenWilson2](https://togithub.com/BenWilson2)) - \[Docs] Upgrade the documentation visuals by replacing low-resolution and poorly dithered GIFs with high-quality HTML5 videos, significantly enhancing the learning experience ([#11051](https://togithub.com/mlflow/mlflow/issues/11051), [@BenWilson2](https://togithub.com/BenWilson2)) - \[Docs / Examples] Correct the compatibility matrix for OpenAI in MLflow Deployments Server documentation, providing users with accurate integration details and supporting smoother deployments ([#11015](https://togithub.com/mlflow/mlflow/issues/11015), [@BenWilson2](https://togithub.com/BenWilson2)) Small bug fixes and documentation updates: [#11284](https://togithub.com/mlflow/mlflow/issues/11284), [#11096](https://togithub.com/mlflow/mlflow/issues/11096), [#11285](https://togithub.com/mlflow/mlflow/issues/11285), [#11245](https://togithub.com/mlflow/mlflow/issues/11245), [#11254](https://togithub.com/mlflow/mlflow/issues/11254), [#11252](https://togithub.com/mlflow/mlflow/issues/11252), [#11250](https://togithub.com/mlflow/mlflow/issues/11250), [#11249](https://togithub.com/mlflow/mlflow/issues/11249), [#11234](https://togithub.com/mlflow/mlflow/issues/11234), [#11248](https://togithub.com/mlflow/mlflow/issues/11248), [#11242](https://togithub.com/mlflow/mlflow/issues/11242), [#11244](https://togithub.com/mlflow/mlflow/issues/11244), [#11236](https://togithub.com/mlflow/mlflow/issues/11236), [#11208](https://togithub.com/mlflow/mlflow/issues/11208), [#11220](https://togithub.com/mlflow/mlflow/issues/11220), [#11222](https://togithub.com/mlflow/mlflow/issues/11222), [#11221](https://togithub.com/mlflow/mlflow/issues/11221), [#11219](https://togithub.com/mlflow/mlflow/issues/11219), [#11218](https://togithub.com/mlflow/mlflow/issues/11218), [#11210](https://togithub.com/mlflow/mlflow/issues/11210), [#11209](https://togithub.com/mlflow/mlflow/issues/11209), [#11207](https://togithub.com/mlflow/mlflow/issues/11207), [#11196](https://togithub.com/mlflow/mlflow/issues/11196), [#11194](https://togithub.com/mlflow/mlflow/issues/11194), [#11177](https://togithub.com/mlflow/mlflow/issues/11177), [#11205](https://togithub.com/mlflow/mlflow/issues/11205), [#11183](https://togithub.com/mlflow/mlflow/issues/11183), [#11192](https://togithub.com/mlflow/mlflow/issues/11192), [#11179](https://togithub.com/mlflow/mlflow/issues/11179), [#11178](https://togithub.com/mlflow/mlflow/issues/11178), [#11175](https://togithub.com/mlflow/mlflow/issues/11175), [#11174](https://togithub.com/mlflow/mlflow/issues/11174), [#11166](https://togithub.com/mlflow/mlflow/issues/11166), [#11162](https://togithub.com/mlflow/mlflow/issues/11162), [#11151](https://togithub.com/mlflow/mlflow/issues/11151), [#11168](https://togithub.com/mlflow/mlflow/issues/11168), [#11167](https://togithub.com/mlflow/mlflow/issues/11167), [#11153](https://togithub.com/mlflow/mlflow/issues/11153), [#11158](https://togithub.com/mlflow/mlflow/issues/11158), [#11143](https://togithub.com/mlflow/mlflow/issues/11143), [#11141](https://togithub.com/mlflow/mlflow/issues/11141), [#11119](https://togithub.com/mlflow/mlflow/issues/11119), [#11123](https://togithub.com/mlflow/mlflow/issues/11123), [#11124](https://togithub.com/mlflow/mlflow/issues/11124), [#11117](https://togithub.com/mlflow/mlflow/issues/11117), [#11121](https://togithub.com/mlflow/mlflow/issues/11121), [#11078](https://togithub.com/mlflow/mlflow/issues/11078), [#11097](https://togithub.com/mlflow/mlflow/issues/11097), [#11079](https://togithub.com/mlflow/mlflow/issues/11079), [#11095](https://togithub.com/mlflow/mlflow/issues/11095), [#11082](https://togithub.com/mlflow/mlflow/issues/11082), [#11071](https://togithub.com/mlflow/mlflow/issues/11071), [#11076](https://togithub.com/mlflow/mlflow/issues/11076), [#11070](https://togithub.com/mlflow/mlflow/issues/11070), [#11072](https://togithub.com/mlflow/mlflow/issues/11072), [#11073](https://togithub.com/mlflow/mlflow/issues/11073), [#11069](https://togithub.com/mlflow/mlflow/issues/11069), [#11058](https://togithub.com/mlflow/mlflow/issues/11058), [#11034](https://togithub.com/mlflow/mlflow/issues/11034), [#11046](https://togithub.com/mlflow/mlflow/issues/11046), [#10951](https://togithub.com/mlflow/mlflow/issues/10951), [#11055](https://togithub.com/mlflow/mlflow/issues/11055), [#11045](https://togithub.com/mlflow/mlflow/issues/11045), [#11035](https://togithub.com/mlflow/mlflow/issues/11035), [#11044](https://togithub.com/mlflow/mlflow/issues/11044), [#11043](https://togithub.com/mlflow/mlflow/issues/11043), [#11031](https://togithub.com/mlflow/mlflow/issues/11031), [#11030](https://togithub.com/mlflow/mlflow/issues/11030), [#11023](https://togithub.com/mlflow/mlflow/issues/11023), [#10932](https://togithub.com/mlflow/mlflow/issues/10932), [#10986](https://togithub.com/mlflow/mlflow/issues/10986), [#10949](https://togithub.com/mlflow/mlflow/issues/10949), [#10943](https://togithub.com/mlflow/mlflow/issues/10943), [#10928](https://togithub.com/mlflow/mlflow/issues/10928), [#10929](https://togithub.com/mlflow/mlflow/issues/10929), [#10925](https://togithub.com/mlflow/mlflow/issues/10925), [#10924](https://togithub.com/mlflow/mlflow/issues/10924), [#10911](https://togithub.com/mlflow/mlflow/issues/10911), [@harupy](https://togithub.com/harupy); [#11289](https://togithub.com/mlflow/mlflow/issues/11289), [@BenWilson2](https://togithub.com/BenWilson2); [#11290](https://togithub.com/mlflow/mlflow/issues/11290), [#11145](https://togithub.com/mlflow/mlflow/issues/11145), [#11125](https://togithub.com/mlflow/mlflow/issues/11125), [#11098](https://togithub.com/mlflow/mlflow/issues/11098), [#11053](https://togithub.com/mlflow/mlflow/issues/11053), [#11006](https://togithub.com/mlflow/mlflow/issues/11006), [#11001](https://togithub.com/mlflow/mlflow/issues/11001), [#11011](https://togithub.com/mlflow/mlflow/issues/11011), [#11007](https://togithub.com/mlflow/mlflow/issues/11007), [#10985](https://togithub.com/mlflow/mlflow/issues/10985), [#10944](https://togithub.com/mlflow/mlflow/issues/10944), [#11231](https://togithub.com/mlflow/mlflow/issues/11231), [@daniellok-db](https://togithub.com/daniellok-db); [#11276](https://togithub.com/mlflow/mlflow/issues/11276), [#11280](https://togithub.com/mlflow/mlflow/issues/11280), [#11275](https://togithub.com/mlflow/mlflow/issues/11275), [#11263](https://togithub.com/mlflow/mlflow/issues/11263), [#11247](https://togithub.com/mlflow/mlflow/issues/11247), [#11257](https://togithub.com/mlflow/mlflow/issues/11257), [#11258](https://togithub.com/mlflow/mlflow/issues/11258), [#11256](https://togithub.com/mlflow/mlflow/issues/11256), [#11224](https://togithub.com/mlflow/mlflow/issues/11224), [#11211](https://togithub.com/mlflow/mlflow/issues/11211), [#11182](https://togithub.com/mlflow/mlflow/issues/11182), [#11059](https://togithub.com/mlflow/mlflow/issues/11059), [#11056](https://togithub.com/mlflow/mlflow/issues/11056), [#11048](https://togithub.com/mlflow/mlflow/issues/11048), [#11008](https://togithub.com/mlflow/mlflow/issues/11008), [#10923](https://togithub.com/mlflow/mlflow/issues/10923), [@serena-ruan](https://togithub.com/serena-ruan); [#11129](https://togithub.com/mlflow/mlflow/issues/11129), [#11086](https://togithub.com/mlflow/mlflow/issues/11086), [@victorsun123](https://togithub.com/victorsun123); [#11292](https://togithub.com/mlflow/mlflow/issues/11292), [#11004](https://togithub.com/mlflow/mlflow/issues/11004), [#11204](https://togithub.com/mlflow/mlflow/issues/11204), [#11148](https://togithub.com/mlflow/mlflow/issues/11148), [#11165](https://togithub.com/mlflow/mlflow/issues/11165), [#11146](https://togithub.com/mlflow/mlflow/issues/11146), [#11115](https://togithub.com/mlflow/mlflow/issues/11115), [#11099](https://togithub.com/mlflow/mlflow/issues/11099), [#11092](https://togithub.com/mlflow/mlflow/issues/11092), [#11029](https://togithub.com/mlflow/mlflow/issues/11029), [#10983](https://togithub.com/mlflow/mlflow/issues/10983), [@B-Step62](https://togithub.com/B-Step62); [#11189](https://togithub.com/mlflow/mlflow/issues/11189), [#11191](https://togithub.com/mlflow/mlflow/issues/11191), [#11022](https://togithub.com/mlflow/mlflow/issues/11022), [#11160](https://togithub.com/mlflow/mlflow/issues/11160), [#11110](https://togithub.com/mlflow/mlflow/issues/11110), [#11088](https://togithub.com/mlflow/mlflow/issues/11088), [#11042](https://togithub.com/mlflow/mlflow/issues/11042), [#10879](https://togithub.com/mlflow/mlflow/issues/10879), [#10832](https://togithub.com/mlflow/mlflow/issues/10832), [#10831](https://togithub.com/mlflow/mlflow/issues/10831), [#10888](https://togithub.com/mlflow/mlflow/issues/10888), [#10908](https://togithub.com/mlflow/mlflow/issues/10908), [@michael-berk](https://togithub.com/michael-berk); [#10627](https://togithub.com/mlflow/mlflow/issues/10627), [#11217](https://togithub.com/mlflow/mlflow/issues/11217), [#11200](https://togithub.com/mlflow/mlflow/issues/11200), [#10969](https://togithub.com/mlflow/mlflow/issues/10969), [@liangz1](https://togithub.com/liangz1); [#11215](https://togithub.com/mlflow/mlflow/issues/11215), [#11173](https://togithub.com/mlflow/mlflow/issues/11173), [#11000](https://togithub.com/mlflow/mlflow/issues/11000), [#10931](https://togithub.com/mlflow/mlflow/issues/10931), [@edwardfeng-db](https://togithub.com/edwardfeng-db); [#11188](https://togithub.com/mlflow/mlflow/issues/11188), [#10711](https://togithub.com/mlflow/mlflow/issues/10711), [@TomeHirata](https://togithub.com/TomeHirata); [#11186](https://togithub.com/mlflow/mlflow/issues/11186), [@xhochy](https://togithub.com/xhochy); [#10916](https://togithub.com/mlflow/mlflow/issues/10916), [@annzhang-db](https://togithub.com/annzhang-db); [#11131](https://togithub.com/mlflow/mlflow/issues/11131), [#11010](https://togithub.com/mlflow/mlflow/issues/11010), [#11060](https://togithub.com/mlflow/mlflow/issues/11060), [@WeichenXu123](https://togithub.com/WeichenXu123); [#11063](https://togithub.com/mlflow/mlflow/issues/11063), [#10981](https://togithub.com/mlflow/mlflow/issues/10981), [#10889](https://togithub.com/mlflow/mlflow/issues/10889), #[#11269](https://togithub.com/mlflow/mlflow/issues/11269), [@chenmoneygithub](https://togithub.com/chenmoneygithub); [#11054](https://togithub.com/mlflow/mlflow/issues/11054), [#10921](https://togithub.com/mlflow/mlflow/issues/10921), [@smurching](https://togithub.com/smurching); [#11018](https://togithub.com/mlflow/mlflow/issues/11018), [@mingyangge-db](https://togithub.com/mingyangge-db); [#10989](https://togithub.com/mlflow/mlflow/issues/10989), [@minkj1992](https://togithub.com/minkj1992); [#10796](https://togithub.com/mlflow/mlflow/issues/10796), [@kriscon-db](https://togithub.com/kriscon-db); [#10984](https://togithub.com/mlflow/mlflow/issues/10984), [@eltociear](https://togithub.com/eltociear); [#10982](https://togithub.com/mlflow/mlflow/issues/10982), [@holzman](https://togithub.com/holzman); [#10972](https://togithub.com/mlflow/mlflow/issues/10972), [@bmuskalla](https://togithub.com/bmuskalla); [#10959](https://togithub.com/mlflow/mlflow/issues/10959), [@prithvikannan](https://togithub.com/prithvikannan); [#10941](https://togithub.com/mlflow/mlflow/issues/10941), [@mahesh-venkatachalam](https://togithub.com/mahesh-venkatachalam); [#10915](https://togithub.com/mlflow/mlflow/issues/10915), [@Cokral](https://togithub.com/Cokral); [#10904](https://togithub.com/mlflow/mlflow/issues/10904), [@dannyfriar](https://togithub.com/dannyfriar); [#11134](https://togithub.com/mlflow/mlflow/issues/11134), [@WP-LKL](https://togithub.com/WP-LKL); [#11287](https://togithub.com/mlflow/mlflow/issues/11287), [@serkef](https://togithub.com/serkef);