Build(deps): Bump the uv group across 1 directory with 2 updates

[dependabot]

Bumps the uv group with 2 updates in the /pipelines/matrix directory: mlflow and torch.

Updates mlflow from 2.20.3 to 3.5.0rc0

Release notes

Sourced from mlflow's releases.

v3.5.0rc0

MLflow 3.5.0rc0 includes several major features and improvements

Major new features:

• 🤖 Tracing support for Claude Code SDK: MLflow now provides a tracing integration for both the Claude Code CLI and SDK! Configure the autologging integration to track your prompts, Claude's responses, tool calls, and more. Check out this doc page to get started. (#18022, @​smoorjani)
• ✨ Improved UI homepage: The MLflow UI's homepage has been updated to help you get started with more of our latest features. This page will be updated regularly moving forward, allowing you to get more in-product guidance. (#18098, @​B-Step62)
• 🗂️ Evaluation datasets UI integration: In MLflow 3.4.0, we released backend support for creating evaluation datasets for GenAI applications. In this release, we've added a new tab to the MLflow Experiment UI, allowing you to create, manage, and export traces to your datasets without having to write a line of code. (#18110, @​daniellok-db)
• 🧮 GEPA support for prompt optimization: MLflow's prompt optimization feature now supports the GEPA algorithm, allowing you to achieve higher performing prompts with less rollouts. For instructions on how to get started with prompt optimization, visit this doc page! (#18031, @​TomeHirata)
• 🔐 Security middleware layer for tracking server: MLflow now ships with a security middleware layer by default, allowing you to protect against DNS rebinding, CORS attacks, and more. Read the documentation here to learn how to configure these options. (#17910, @​BenWilson2)

Stay tuned for the full release, which will be packed with more features and bugfixes.

To try out this release candidate, please run:

pip install mlflow==3.5.0rc0

v3.4.0

MLflow 3.4.0rc0 includes several major features and improvements

Major New Features

• 📊 OpenTelemetry Metrics Export: MLflow now exports span-level statistics as OpenTelemetry metrics, providing enhanced observability and monitoring capabilities for traced applications. (#17325, @​dbczumar)
• 🤖 MCP Server Integration: Introducing the Model Context Protocol (MCP) server for MLflow, enabling AI assistants and LLMs to interact with MLflow programmatically. (#17122, @​harupy)
• 🧑‍⚖️ Custom Judges API: New make_judge API enables creation of custom evaluation judges for assessing LLM outputs with domain-specific criteria. (#17647, @​BenWilson2, @​dbczumar, @​alkispoly-db, @​smoorjani)
• 📈 Correlations Backend: Implemented backend infrastructure for storing and computing correlations between experiment metrics using NPMI (Normalized Pointwise Mutual Information). (#17309, #17368, @​BenWilson2)
• 🗂️ Evaluation Datasets: MLflow now supports storing and versioning evaluation datasets directly within experiments for reproducible model assessment. (#17447, @​BenWilson2)
• 🔗 Databricks Backend for MLflow Server: MLflow server can now use Databricks as a backend, enabling seamless integration with Databricks workspaces. (#17411, @​nsthorat)
• 🤖 Claude Autologging: Automatic tracing support for Claude AI interactions, capturing conversations and model responses. (#17305, @​smoorjani)
• 🌊 Strands Agent Tracing: Added comprehensive tracing support for Strands agents, including automatic instrumentation for agent workflows and interactions. (#17151, @​joelrobin18)
• 🧪 Experiment Types in UI: MLflow now introduces experiment types, helping reduce clutter between classic ML/DL and GenAI features. MLflow auto-detects the type, but you can easily adjust it via a selector next to the experiment name. (#17605, @​daniellok-db)

Features:

• [Evaluation] Add ability to pass tags via dataframe in mlflow.genai.evaluate (#17549, @​smoorjani)
• [Evaluation] Add custom judge model support for Safety and RetrievalRelevance builtin scorers (#17526, @​dbrx-euirim)
• [Tracing] Add AI commands as MCP prompts for LLM interaction (#17608, @​nsthorat)
• [Tracing] Add MLFLOW_ENABLE_OTLP_EXPORTER environment variable (#17505, @​dbczumar)
• [Tracing] Support OTel and MLflow dual export (#17187, @​dbczumar)
• [Tracing] Make set_destination use ContextVar for thread safety (#17219, @​B-Step62)
• [CLI] Add MLflow commands CLI for exposing prompt commands to LLMs (#17530, @​nsthorat)
• [CLI] Add 'mlflow runs link-traces' command (#17444, @​nsthorat)
• [CLI] Add 'mlflow runs create' command for programmatic run creation (#17417, @​nsthorat)
• [CLI] Add MLflow traces CLI command with comprehensive search and management capabilities (#17302, @​nsthorat)
• [CLI] Add --env-file flag to all MLflow CLI commands (#17509, @​nsthorat)
• [Tracking] Backend for storing scorers in MLflow experiments (#17090, @​WeichenXu123)
• [Model Registry] Allow cross-workspace copying of model versions between WMR and UC (#17458, @​arpitjasa-db)
• [Models] Add automatic Git-based model versioning for GenAI applications (#17076, @​harupy)
• [Models] Improve WheeledModel._download_wheels safety (#17004, @​serena-ruan)
• [Projects] Support resume run for Optuna hyperparameter optimization (#17191, @​lu-wang-dl)

... (truncated)

Changelog

Sourced from mlflow's changelog.

3.5.0rc0 (2025-10-08)

MLflow 3.5.0rc0 includes several major features and improvements

Major new features:

• 🤖 Tracing support for Claude Code SDK: MLflow now provides a tracing integration for both the Claude Code CLI and SDK! Configure the autologging integration to track your prompts, Claude's responses, tool calls, and more. Check out this doc page to get started. (#18022, @​smoorjani)
• ✨ Improved UI homepage: The MLflow UI's homepage has been updated to help you get started with more of our latest features. This page will be updated regularly moving forward, allowing you to get more in-product guidance.
• 🗂️ Evaluation datasets UI integration: In MLflow 3.4.0, we released backend support for creating evaluation datasets for GenAI applications. In this release, we've added a new tab to the MLflow Experiment UI, allowing you to create, manage, and export traces to your datasets without having to write a line of code.
• 🧮 GEPA support for prompt optimization: MLflow's prompt optimization feature now supports the GEPA algorithm, allowing you to achieve higher performing prompts with less rollouts. For instructions on how to get started with prompt optimization, visit this doc page!
• 🔐 Security middleware layer for tracking server: MLflow now ships with a security middleware layer by default, allowing you to protect against DNS rebinding, CORS attacks, and more. Read the documentation here to learn how to configure these options.

Stay tuned for the full release, which will be packed with more features and bugfixes.

To try out this release candidate, please run:

pip install mlflow==3.5.0rc0

3.4.0rc0 (2025-09-11)

MLflow 3.4.0rc0 includes several major features and improvements

Major New Features

• 📊 OpenTelemetry Metrics Export: MLflow now exports span-level statistics as OpenTelemetry metrics, providing enhanced observability and monitoring capabilities for traced applications. (#17325, @​dbczumar)
• 🤖 MCP Server Integration: Introducing the Model Context Protocol (MCP) server for MLflow, enabling AI assistants and LLMs to interact with MLflow programmatically. (#17122, @​harupy)
• 🧑‍⚖️ Custom Judges API: New make_judge API enables creation of custom evaluation judges for assessing LLM outputs with domain-specific criteria. (#17647, @​BenWilson2, @​dbczumar, @​alkispoly-db, @​smoorjani)
• 📈 Correlations Backend: Implemented backend infrastructure for storing and computing correlations between experiment metrics using NPMI (Normalized Pointwise Mutual Information). (#17309, #17368, @​BenWilson2)
• 🗂️ Evaluation Datasets: MLflow now supports storing and versioning evaluation datasets directly within experiments for reproducible model assessment. (#17447, @​BenWilson2)
• 🔗 Databricks Backend for MLflow Server: MLflow server can now use Databricks as a backend, enabling seamless integration with Databricks workspaces. (#17411, @​nsthorat)
• 🤖 Claude Autologging: Automatic tracing support for Claude AI interactions, capturing conversations and model responses. (#17305, @​smoorjani)
• 🌊 Strands Agent Tracing: Added comprehensive tracing support for Strands agents, including automatic instrumentation for agent workflows and interactions. (#17151, @​joelrobin18)
• 🧪 Experiment Types in UI: MLflow now introduces experiment types, helping reduce clutter between classic ML/DL and GenAI features. MLflow auto-detects the type, but you can easily adjust it via a selector next to the experiment name. (#17605, @​daniellok-db)

Features:

• [Evaluation] Add ability to pass tags via dataframe in mlflow.genai.evaluate (#17549, @​smoorjani)
• [Evaluation] Add custom judge model support for Safety and RetrievalRelevance builtin scorers (#17526, @​dbrx-euirim)
• [Tracing] Add AI commands as MCP prompts for LLM interaction (#17608, @​nsthorat)
• [Tracing] Add MLFLOW_ENABLE_OTLP_EXPORTER environment variable (#17505, @​dbczumar)
• [Tracing] Support OTel and MLflow dual export (#17187, @​dbczumar)
• [Tracing] Make set_destination use ContextVar for thread safety (#17219, @​B-Step62)
• [CLI] Add MLflow commands CLI for exposing prompt commands to LLMs (#17530, @​nsthorat)
• [CLI] Add 'mlflow runs link-traces' command (#17444, @​nsthorat)
• [CLI] Add 'mlflow runs create' command for programmatic run creation (#17417, @​nsthorat)
• [CLI] Add MLflow traces CLI command with comprehensive search and management capabilities (#17302, @​nsthorat)
• [CLI] Add --env-file flag to all MLflow CLI commands (#17509, @​nsthorat)
• [Tracking] Backend for storing scorers in MLflow experiments (#17090, @​WeichenXu123)
• [Model Registry] Allow cross-workspace copying of model versions between WMR and UC (#17458, @​arpitjasa-db)
• [Models] Add automatic Git-based model versioning for GenAI applications (#17076, @​harupy)

... (truncated)

Commits

• 19c618c Run python3 dev/update_mlflow_versions.py pre-release ... (#18181)
• 13115e4 Support GEPA in mlflow.genai.optimize_prompt (#18031)
• fa83107 Run python3 dev/update_ml_package_versions.py (#18177)
• 16fc22f Add uv lock call after pyproject.toml generation in DEV branch (#18180)
• be2125a Run python3 dev/update_requirements.py && python3 bin/... (#18176)
• 2fd3145 Dump sql_warehouse_id into trace UI mimebundle (#18165)
• 5cb6a95 Replace Docker with uv in tests/test_import.py for faster test execution (#18...
• eaadd39 Add support for trace inputs to built-in scorers (#17943)
• 30f2f55 Add B012 (jump-statement-in-finally) rule to ruff configuration (#18170)
• 97ac7e1 Increase MAX_DOCSTRING_LENGTH_RATIO to 1.25 and remove redundant test docstri...
• Additional commits viewable in compare view

Updates torch from 2.7.1 to 2.8.0

Release notes

Sourced from torch's releases.

PyTorch 2.8.0 Release Notes

• Highlights
• Backwards Incompatible Changes
• Deprecations
• New Features
• Improvements
• Bug fixes
• Performance
• Documentation
• Developers

Highlights

... (truncated)

Commits

• ba56102 Cherrypick: Add the RunLLM widget to the website (#159592)
• c525a02 [dynamo, docs] cherry pick torch.compile programming model docs into 2.8 (#15...
• a1cb3cc [Release Only] Remove nvshmem from list of preload libraries (#158925)
• c76b235 Move out super large one off foreach_copy test (#158880)
• 20a0e22 Revert "[Dynamo] Allow inlining into AO quantization modules (#152934)" (#158...
• 9167ac8 [MPS] Switch Cholesky decomp to column wise (#158237)
• 5534685 [MPS] Reimplement tri[ul] as Metal shaders (#158867)
• d19e08d Cherry pick PR 158746 (#158801)
• a6c044a [cherry-pick] Unify torch.tensor and torch.ops.aten.scalar_tensor behavior (#...
• 620ebd0 [Dynamo] Use proper sources for constructing dataclass defaults (#158689)
• Additional commits viewable in compare view

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