fix(predict-diabetes): update dependency mlflow to v2.11.1 #15074
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
![renovate[bot]](https://avatars.githubusercontent.com/in/2740?s=60&v=4){kind=small}
|
![mergify[bot]](https://avatars.githubusercontent.com/in/10562?s=60&v=4){kind=small}
|
🎉 This PR is included in version 1.107.1 🎉 The release is available on GitHub release Your semantic-release bot 📦🚀 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR contains the following updates:
2.10.2->2.11.1Warning
Some dependencies could not be looked up. Check the Dependency Dashboard for more information.
Release Notes
mlflow/mlflow (mlflow)
v2.11.1Compare Source
MLflow 2.11.1 is a patch release, containing fixes for some Databricks integrations and other various issues.
Bug fixes:
Small bug fixes and documentation updates:
#11336, #11335, @harupy; #11303, @B-Step62; #11319, @BenWilson2; #11306, @daniellok-db
v2.11.0Compare Source
MLflow 2.11.0 includes several major features and improvements
With the MLflow 2.11.0 release, we're excited to bring a series of large and impactful features that span both GenAI and Deep Learning use cases.
The MLflow Tracking UI got an overhaul to better support the review and comparison of training runs for Deep Learning workloads. From grouping to large-scale metric plotting throughout
the iterations of a DL model's training cycle, there are a large number of quality of life improvements to enhance your Deep Learning MLOps workflow.
Support for the popular PEFT library from HuggingFace is now available
in the
mlflow.transformersflavor. In addition to PEFT support, we've removed the restrictions on Pipeline typesthat can be logged to MLflow, as well as the ability to, when developing and testing models, log a transformers pipeline without copying foundational model weights. These
enhancements strive to make the transformers flavor more useful for cutting-edge GenAI models, new pipeline types, and to simplify the development process of prompt engineering, fine-tuning,
and to make iterative development faster and cheaper. Give the updated flavor a try today! (#11240, @B-Step62)
We've added support to both PyTorch and
TensorFlow for automatic model weights checkpointing (including resumption from a
previous state) for the auto logging implementations within both flavors. This highly requested feature allows you to automatically configure long-running Deep Learning training
runs to keep a safe storage of your best epoch, eliminating the risk of a failure late in training from losing the state of the model optimization. (#11197, #10935, @WeichenXu123)
We've added a new interface to Pyfunc for GenAI workloads. The new
ChatModelinterface allows for interacting with a deployed GenAI chat model as you would with any other provider.The simplified interface (no longer requiring conformance to a Pandas DataFrame input type) strives to unify the API interface experience. (#10820, @daniellok-db)
We now support Keras 3. This large overhaul of the Keras library introduced new fundamental changes to how Keras integrates with different DL frameworks, bringing with it
a host of new functionality and interoperability. To learn more, see the Keras 3.0 Tutorial
to start using the updated model flavor today! (#10830, @chenmoneygithub)
Mistral AI has been added as a native provider for the MLflow Deployments Server. You can
now create proxied connections to the Mistral AI services for completions and embeddings with their powerful GenAI models. (#11020, @thnguyendn)
We've added compatibility support for the OpenAI 1.x SDK. Whether you're using an OpenAI LLM for model evaluation or calling OpenAI within a LangChain model, you'll now be able to
utilize the 1.x family of the OpenAI SDK without having to point to deprecated legacy APIs. (#11123, @harupy)
Features:
mlflow.pyfunc.predict, enhancing data compatibility and analysis options for batch inference (#10939, @ernestwong-db)mlflow.config.enable_async_loggingfor asynchronous logging, improving log handling and system performance (#11138, @chenmoneygithub)prompt) and embeddings (input) format inputs in the scoring server, increasing model interaction flexibility (#10958, @es94129)Bug Fixes:
load_context()is called when enforcingChatModeloutputs so that all required external references are included in the model object instance (#11150, @daniellok-db)torch.dtypeas a string was not being applied correctly to the underlying transformers model (#11297, #11295, @harupy)mlflow.evaluatecol_mappingbug for non-LLM/custom metrics, ensuring accurate evaluation and metric calculation (#11156, @sunishsheth2009)TensorInfoTypeError exception message issue, ensuring clarity and accuracy in error reporting for users (#10953, @leecs0503)RestExceptionobjects to be picklable, improving their usability in distributed computing scenarios where serialization is essential (#10936, @WeichenXu123)io.delta:delta-spark_2.12:3.0.0dependency to the correct scala version, aligning dependencies with project requirements (#11149, @WeichenXu123)importlib.metadata.entry_points().get, enhancing compatibility and stability (#10752, @raphaelauv)mlflow.login(), streamlining the authentication process and improving security (#11039, @chenmoneygithub)Documentation Updates:
log_input, enriching the documentation with actionable advice and examples for effective data handling (#10956, @BenWilson2)Small bug fixes and documentation updates:
#11284, #11096, #11285, #11245, #11254, #11252, #11250, #11249, #11234, #11248, #11242, #11244, #11236, #11208, #11220, #11222, #11221, #11219, #11218, #11210, #11209, #11207, #11196, #11194, #11177, #11205, #11183, #11192, #11179, #11178, #11175, #11174, #11166, #11162, #11151, #11168, #11167, #11153, #11158, #11143, #11141, #11119, #11123, #11124, #11117, #11121, #11078, #11097, #11079, #11095, #11082, #11071, #11076, #11070, #11072, #11073, #11069, #11058, #11034, #11046, #10951, #11055, #11045, #11035, #11044, #11043, #11031, #11030, #11023, #10932, #10986, #10949, #10943, #10928, #10929, #10925, #10924, #10911, @harupy; #11289, @BenWilson2; #11290, #11145, #11125, #11098, #11053, #11006, #11001, #11011, #11007, #10985, #10944, #11231, @daniellok-db; #11276, #11280, #11275, #11263, #11247, #11257, #11258, #11256, #11224, #11211, #11182, #11059, #11056, #11048, #11008, #10923, @serena-ruan; #11129, #11086, @victorsun123; #11292, #11004, #11204, #11148, #11165, #11146, #11115, #11099, #11092, #11029, #10983, @B-Step62; #11189, #11191, #11022, #11160, #11110, #11088, #11042, #10879, #10832, #10831, #10888, #10908, @michael-berk; #10627, #11217, #11200, #10969, @liangz1; #11215, #11173, #11000, #10931, @edwardfeng-db; #11188, #10711, @TomeHirata; #11186, @xhochy; #10916, @annzhang-db; #11131, #11010, #11060, @WeichenXu123; #11063, #10981, #10889, ##11269, @chenmoneygithub; #11054, #10921, @smurching; #11018, @mingyangge-db; #10989, @minkj1992; #10796, @kriscon-db; #10984, @eltociear; #10982, @holzman; #10972, @bmuskalla; #10959, @prithvikannan; #10941, @mahesh-venkatachalam; #10915, @Cokral; #10904, @dannyfriar; #11134, @WP-LKL; #11287, @serkef;
Configuration
📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
🔕 Ignore: Close this PR and you won't be reminded about this update again.
This PR has been generated by Mend Renovate. View repository job log here.