Bump mlflow from 2.10.0 to 2.11.0 in /requirements

[dependabot[bot]]

Bumps mlflow from 2.10.0 to 2.11.0.

Release notes

Sourced from mlflow's releases.

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)
• [Models] Update Embedding API for sentence transformers to ensure compatibility with OpenAI format, broadening model application scopes (#11019, @​lu-wang-dl)
• [Models] Improve input and signature support for text-generation models, optimizing for Chat and Completions tasks (#11027, @​es94129)
• [Models] Enable chat and completions task outputs in the text-generation pipeline, expanding interactive capabilities (#10872, @​es94129)
• [Tracking] Add node id to system metrics for enhanced logging in multi-node setups, improving diagnostics and monitoring (#11021, @​chenmoneygithub)
• [Tracking] Implement mlflow.config.enable_async_logging for asynchronous logging, improving log handling and system performance (#11138, @​chenmoneygithub)
• [Evaluate] Enhance model evaluation with endpoint URL support, streamlining performance assessments and integrations (#11262, @​B-Step62)
• [Deployments] Implement chat & chat streaming support for Cohere, enhancing interactive model deployment capabilities (#10976, @​gabrielfu)
• [Deployments] Enable Cohere streaming support, allowing real-time interaction functionalities for the MLflow Deployments server with the Cohere provider (#10856, @​gabrielfu)
• [Docker / Scoring] Optimize Docker images for model serving, ensuring more efficient deployment and scalability (#10954, @​B-Step62)
• [Scoring] Support completions (prompt) and embeddings (input) format inputs in the scoring server, increasing model interaction flexibility (#10958, @​es94129)

Bug Fixes:

... (truncated)

Changelog

Sourced from mlflow's changelog.

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)
• [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)
• [Models] Update Embedding API for sentence transformers to ensure compatibility with OpenAI format, broadening model application scopes (#11019, @​lu-wang-dl)
• [Models] Improve input and signature support for text-generation models, optimizing for Chat and Completions tasks (#11027, @​es94129)
• [Models] Enable chat and completions task outputs in the text-generation pipeline, expanding interactive capabilities (#10872, @​es94129)
• [Tracking] Add node id to system metrics for enhanced logging in multi-node setups, improving diagnostics and monitoring (#11021, @​chenmoneygithub)
• [Tracking] Implement mlflow.config.enable_async_logging for asynchronous logging, improving log handling and system performance (#11138, @​chenmoneygithub)
• [Evaluate] Enhance model evaluation with endpoint URL support, streamlining performance assessments and integrations (#11262, @​B-Step62)
• [Deployments] Implement chat & chat streaming support for Cohere, enhancing interactive model deployment capabilities (#10976, @​gabrielfu)
• [Deployments] Enable Cohere streaming support, allowing real-time interaction functionalities for the MLflow Deployments server with the Cohere provider (#10856, @​gabrielfu)
• [Docker / Scoring] Optimize Docker images for model serving, ensuring more efficient deployment and scalability (#10954, @​B-Step62)
• [Scoring] Support completions (prompt) and embeddings (input) format inputs in the scoring server, increasing model interaction flexibility (#10958, @​es94129)

... (truncated)

Commits

• 830940a Fix torch_dtype issue when specified via kwargs (#11297)
• ad05adc Update documentation for system tags (#11292)
• 3a2003f Cast torch.dtype name to torch.dtype (#11295)
• fbb8fbb Update mlflow.metrics.rst (#11287)
• 5f40c23 Run python3 dev/update_mlflow_versions.py pre-release ... (#11293)
• ec41e42 Fix endpoint construction for proxy MPU (#11284)
• eeb1d4d Support model evaluation with an endpoint URL (#11262)
• b223ac6 Return dict-like object in MlflowDeploymentClient.get_endpoint (#11096)
• 281f65c Release cards 2 11 (#11289)
• bc910c0 Add more helpful pickle error when saving custom pyfuncs (#11290)
• Additional commits viewable in compare view

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Superseded by #452.