Chore(deps-dev): Bump mlflow from 2.10.2 to 2.11.1

[dependabot[bot]]

Bumps mlflow from 2.10.2 to 2.11.1.

Release notes

Sourced from mlflow's releases.

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, @​daniellok-db)
• [Databricks Integration] Explicitly import vectorstores and embeddings in databricks_dependencies (#11334, @​daniellok-db)
• [Databricks Integration] Modify DBR version parsing logic (#11328, @​daniellok-db)

Small bug fixes and documentation updates:

#11336, #11335, @​harupy; #11303, @​B-Step62; #11319, @​BenWilson2; #11306, @​daniellok-db

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.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, @​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 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, @​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:

• [UI] Revamp the MLflow Tracking UI for Deep Learning workflows, offering a more intuitive and efficient user experience (#11233, @​daniellok-db)
• [Data] Introduce the ability to log datasets without loading them into memory, optimizing resource usage and processing time (#11172, @​chenmoneygithub)
• [Models] Introduce logging frequency controls for TensorFlow, aligning it with Keras for consistent performance monitoring (#11094, @​chenmoneygithub)
• [Models] Add PySpark DataFrame support in mlflow.pyfunc.predict, enhancing data compatibility and analysis options for batch inference (#10939, @​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, @​daniellok-db)

... (truncated)

Changelog

Sourced from mlflow's changelog.

2.11.1 (2024-03-06)

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, @​daniellok-db)
• [Databricks Integration] Explicitly import vectorstores and embeddings in databricks_dependencies (#11334, @​daniellok-db)
• [Databricks Integration] Modify DBR version parsing logic (#11328, @​daniellok-db)

Small bug fixes and documentation updates:

#11336, #11335, @​harupy; #11303, @​B-Step62; #11319, @​BenWilson2; #11306, @​daniellok-db

2.11.0 (2024-03-01)

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.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, @​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 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, @​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:

• [UI] Revamp the MLflow Tracking UI for Deep Learning workflows, offering a more intuitive and efficient user experience (#11233, @​daniellok-db)

... (truncated)

Commits

• abad05e Run python3 dev/update_mlflow_versions.py pre-release ... (#11340)
• c04f5b6 Import genai in mlflow.metrics (#11336)
• f34b888 Add Commit Hash back to UI (#11324)
• 9766da5 Add a note for unsupported Langchain models in the documentation (#11303)
• 10192af Exclude null fields when serializing MLmodel file (#11335)
• 84d28f8 Explicitly import vectorstores and embeddings in databricks_dependencies (#11...
• 3688ce0 replace videos with high resolution gif (#11319)
• 505798c Modify DBR version parsing logic (#11328)
• 085109c Fix databricks_utils.py for the "client" image key (#11306)
• 830940a Fix torch_dtype issue when specified via kwargs (#11297)
• Additional commits viewable in compare view

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Superseded by #121.