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security(deps): bump the training-dependencies group in /training/rl with 7 updates#408

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security(deps): bump the training-dependencies group in /training/rl with 7 updates#408
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@dependabot dependabot bot commented on behalf of github Apr 7, 2026

Bumps the training-dependencies group in /training/rl with 7 updates:

Package From To
marshmallow 4.2.3 4.3.0
mlflow 3.10.1 3.11.1
azure-monitor-opentelemetry-exporter 1.0.0b50 1.0.0b51
cuda-pathfinder 1.5.1 1.5.2
mlflow-skinny 3.10.1 3.11.1
mlflow-tracing 3.10.1 3.11.1
uvicorn 0.43.0 0.44.0

Updates marshmallow from 4.2.3 to 4.3.0

Changelog

Sourced from marshmallow's changelog.

4.3.0 (2026-04-03)

Features:

  • Add pre_load and post_load parameters to marshmallow.fields.Field for field-level pre- and post-processing (:issue:2787).
  • Typing: improvements to marshmallow.validate (:pr:2940).

4.2.4 (2026-04-02)

Bug fixes:

  • marshmallow.validate.URL and marshmallow.validate.Email accept Internationalized Domain Names (IDNs) (:issue:2821, :issue:2936). marshmallow.validate.Email also correctly rejects IDN domains with leading/trailing hyphens. Thanks :user:touhidurrr for the report.
  • Typing: Fix typing of nested in marshmallow.fields.Nested (:pr:2935).
Commits

Updates mlflow from 3.10.1 to 3.11.1

Release notes

Sourced from mlflow's releases.

v3.11.0rc1

Stripped third-party dependencies from evaluation and AI Gateway features, replacing external provider routing with built-in implementations.

v3.11.0rc0

We're excited to announce MLflow 3.11.0rc0, which includes several notable updates:

Major New Features:

  • 🔍 Automatic Issue Identification: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. (#21431, #21204, #21165, #21163, #21161, @​smoorjani, @​serena-ruan)
  • 💰 Gateway Budget Alerts & Limits: Control your AI Gateway spending with configurable budget policies! Set spending limits by time window (daily, weekly, or monthly), receive alerts before hitting limits, and prevent runaway costs with automatic request blocking. The new budget management UI lets you track spending, configure webhooks for notifications, and monitor violations across all your gateway endpoints. (#21116, #21534, #21569, #21473, #21108, @​TomeHirata, @​copilot-swe-agent)
  • 📊 Trace Graph View: Visualize complex trace hierarchies with an interactive graph view! Navigate multi-level trace structures, understand parent-child relationships at a glance, and debug complex systems more effectively with a visual representation of your trace topology. (#20607, @​joelrobin18)
  • 🌐 Native OpenTelemetry GenAI Convention Support: MLflow now natively supports the OpenTelemetry GenAI Semantic Conventions for trace export! When exporting traces via OTLP with MLFLOW_ENABLE_OTEL_GENAI_SEMCONV enabled, MLflow automatically translates them to follow the OTel GenAI semantic conventions, enabling seamless integration with OTel-compatible observability platforms while preserving GenAI-specific metadata. (#21494, #21495, @​B-Step62)
  • 🔧 Opencode Tracing Integration: Debug smarter with Opencode CLI integration! Track and analyze code execution flows directly from your development workflow, making it easier to identify performance bottlenecks and trace issues back to specific code paths. (#20133, @​joelrobin18)
  • ⚡ UV Package Manager Support: Automatic dependency inference now supports UV! MLflow automatically detects UV projects and captures exact, locked dependencies from your lockfile when logging models, ensuring reproducible environments. (#20344, #20935, @​debu-sinha)
  • 🔒 Pickle-Free Model Serialization: Enhance security with pickle-free model formats! MLflow now supports safer model serialization using torch.export and skops formats, with improved controls when MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False. Comprehensive documentation guides you through migrating existing models to pickle-free formats for production deployments. (#21404, #21188, #20774, @​WeichenXu123)

Breaking Changes:

  • ⚠️ TypeScript SDK Package Renaming: The MLflow TypeScript SDK packages have been renamed to use npm organization scoping. If you're using the TypeScript SDK, update your package.json dependencies and import statements: mlflow-tracing → @mlflow/core, mlflow-openai → @mlflow/openai, mlflow-anthropic → @mlflow/anthropic, mlflow-gemini → @mlflow/gemini. All packages are now at version 0.2.0. (#20792, @​B-Step62)

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

To try out this release candidate, please run:

pip install mlflow==3.11.0rc0

Changelog

Sourced from mlflow's changelog.

CHANGELOG

3.11.0 (2026-04-07)

MLflow 3.11.0 includes several major features and improvements.

Major New Features:

  • 🔍 Automatic Issue Identification: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. Docs (#21431, #21204, #21165, #21163, #21161, @​smoorjani, @​serena-ruan)
  • 💰 Gateway Budget Alerts & Limits: Control your AI Gateway spending with configurable budget policies! Set spending limits by time window (daily, weekly, or monthly), receive alerts before hitting limits, and prevent runaway costs with automatic request blocking. The new budget management UI lets you track spending, configure webhooks for notifications, and monitor violations across all your gateway endpoints. Docs (#21116, #21534, #21569, #21473, #21108, @​TomeHirata, @​copilot-swe-agent)
  • 📊 Trace Graph View: Visualize complex trace hierarchies with an interactive graph view! Navigate multi-level trace structures, understand parent-child relationships at a glance, and debug complex systems more effectively with a visual representation of your trace topology. Docs (#20607, @​joelrobin18)
  • 🌐 Native OpenTelemetry GenAI Convention Support: MLflow now natively supports the OpenTelemetry GenAI Semantic Conventions for trace export! When exporting traces via OTLP with MLFLOW_ENABLE_OTEL_GENAI_SEMCONV enabled, MLflow automatically translates them to follow the OTel GenAI semantic conventions, enabling seamless integration with OTel-compatible observability platforms while preserving GenAI-specific metadata. Docs (#21494, #21495, @​B-Step62)
  • 🔧 OpenCode Tracing Integration: Debug smarter with OpenCode CLI integration! Track and analyze code execution flows directly from your development workflow, making it easier to identify performance bottlenecks and trace issues back to specific code paths. Docs (#20133, @​joelrobin18)
  • ⚡ Native UV Support for Model Dependencies: Automatic dependency inference now supports UV! MLflow automatically detects UV projects and captures exact, locked dependencies from your lockfile when logging models, ensuring reproducible environments. Docs (#20344, #20935, @​debu-sinha)
  • 🔒 Pickle-Free Model Serialization: Enhance security with pickle-free model formats! MLflow now supports safer model serialization using torch.export and skops formats, with improved controls when MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False. Comprehensive documentation guides you through migrating existing models to pickle-free formats for production deployments. Docs (#21404, #21188, #20774, @​WeichenXu123)

Breaking Changes:

  • ⚠️ TypeScript SDK Package Renaming: The MLflow TypeScript SDK packages have been renamed to use npm organization scoping. If you're using the TypeScript SDK, update your package.json dependencies and import statements: mlflow-tracing → @mlflow/core, mlflow-openai → @mlflow/openai, mlflow-anthropic → @mlflow/anthropic, mlflow-gemini → @mlflow/gemini. All packages are now at version 0.2.0. (#20792, @​B-Step62)
  • Remove MLFLOW_ENABLE_INCREMENTAL_SPAN_EXPORT environment variable (#22182, @​PattaraS)
  • Remove litellm and gepa from genai extras (#22059, @​TomeHirata)
  • Block / and : in Registered Model names (#21458, @​Bhuvan-08)

Features:

... (truncated)

Commits

Updates azure-monitor-opentelemetry-exporter from 1.0.0b50 to 1.0.0b51

Release notes

Sourced from azure-monitor-opentelemetry-exporter's releases.

azure-monitor-opentelemetry-exporter_1.0.0b51

1.0.0b51 (2026-04-07)

Bugs Fixed

  • Added credential authentication support for customer sdkstats (#46143)
Commits
  • fc2b705 Exporter release 1.0.0b51 (#46155)
  • 83a443d Added credential authentication support for customer sdkstats (#46143)
  • ce4549d Increment package version after release of azure-monitor-opentelemetry-export...
  • See full diff in compare view

Updates cuda-pathfinder from 1.5.1 to 1.5.2

Release notes

Sourced from cuda-pathfinder's releases.

cuda-pathfinder v1.5.2

Release notes

Documentation

PyPI

Conda

Commits
  • 1696fcf docs(pathfinder): prepare 1.5.2 release notes (#1867)
  • b57a674 Update context7.json (#1864)
  • ac34454 [FEA]: Add support for pathfinder.find_nvidia_header_directory("profiler") ...
  • 5ae423f [FEA]: Add pathfinder cudla support (.so, .h) (#1855)
  • c6aea12 Publish the graph API as cuda.core.graph (#1858)
  • c2f79a1 Graph node follow-ups: repr, containment, empty(), registry docs (#1859)
  • 5064470 Add GraphNode identity cache for stable object round-trips (#1853)
  • 5777275 Add edge mutation and MutableSet interface for graph nodes (#1850)
  • See full diff in compare view

Updates mlflow-skinny from 3.10.1 to 3.11.1

Release notes

Sourced from mlflow-skinny's releases.

v3.11.0rc1

Stripped third-party dependencies from evaluation and AI Gateway features, replacing external provider routing with built-in implementations.

v3.11.0rc0

We're excited to announce MLflow 3.11.0rc0, which includes several notable updates:

Major New Features:

  • 🔍 Automatic Issue Identification: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. (#21431, #21204, #21165, #21163, #21161, @​smoorjani, @​serena-ruan)
  • 💰 Gateway Budget Alerts & Limits: Control your AI Gateway spending with configurable budget policies! Set spending limits by time window (daily, weekly, or monthly), receive alerts before hitting limits, and prevent runaway costs with automatic request blocking. The new budget management UI lets you track spending, configure webhooks for notifications, and monitor violations across all your gateway endpoints. (#21116, #21534, #21569, #21473, #21108, @​TomeHirata, @​copilot-swe-agent)
  • 📊 Trace Graph View: Visualize complex trace hierarchies with an interactive graph view! Navigate multi-level trace structures, understand parent-child relationships at a glance, and debug complex systems more effectively with a visual representation of your trace topology. (#20607, @​joelrobin18)
  • 🌐 Native OpenTelemetry GenAI Convention Support: MLflow now natively supports the OpenTelemetry GenAI Semantic Conventions for trace export! When exporting traces via OTLP with MLFLOW_ENABLE_OTEL_GENAI_SEMCONV enabled, MLflow automatically translates them to follow the OTel GenAI semantic conventions, enabling seamless integration with OTel-compatible observability platforms while preserving GenAI-specific metadata. (#21494, #21495, @​B-Step62)
  • 🔧 Opencode Tracing Integration: Debug smarter with Opencode CLI integration! Track and analyze code execution flows directly from your development workflow, making it easier to identify performance bottlenecks and trace issues back to specific code paths. (#20133, @​joelrobin18)
  • ⚡ UV Package Manager Support: Automatic dependency inference now supports UV! MLflow automatically detects UV projects and captures exact, locked dependencies from your lockfile when logging models, ensuring reproducible environments. (#20344, #20935, @​debu-sinha)
  • 🔒 Pickle-Free Model Serialization: Enhance security with pickle-free model formats! MLflow now supports safer model serialization using torch.export and skops formats, with improved controls when MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False. Comprehensive documentation guides you through migrating existing models to pickle-free formats for production deployments. (#21404, #21188, #20774, @​WeichenXu123)

Breaking Changes:

  • ⚠️ TypeScript SDK Package Renaming: The MLflow TypeScript SDK packages have been renamed to use npm organization scoping. If you're using the TypeScript SDK, update your package.json dependencies and import statements: mlflow-tracing → @mlflow/core, mlflow-openai → @mlflow/openai, mlflow-anthropic → @mlflow/anthropic, mlflow-gemini → @mlflow/gemini. All packages are now at version 0.2.0. (#20792, @​B-Step62)

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

To try out this release candidate, please run:

pip install mlflow==3.11.0rc0

Changelog

Sourced from mlflow-skinny's changelog.

CHANGELOG

3.11.0 (2026-04-07)

MLflow 3.11.0 includes several major features and improvements.

Major New Features:

  • 🔍 Automatic Issue Identification: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. Docs (#21431, #21204, #21165, #21163, #21161, @​smoorjani, @​serena-ruan)
  • 💰 Gateway Budget Alerts & Limits: Control your AI Gateway spending with configurable budget policies! Set spending limits by time window (daily, weekly, or monthly), receive alerts before hitting limits, and prevent runaway costs with automatic request blocking. The new budget management UI lets you track spending, configure webhooks for notifications, and monitor violations across all your gateway endpoints. Docs (#21116, #21534, #21569, #21473, #21108, @​TomeHirata, @​copilot-swe-agent)
  • 📊 Trace Graph View: Visualize complex trace hierarchies with an interactive graph view! Navigate multi-level trace structures, understand parent-child relationships at a glance, and debug complex systems more effectively with a visual representation of your trace topology. Docs (#20607, @​joelrobin18)
  • 🌐 Native OpenTelemetry GenAI Convention Support: MLflow now natively supports the OpenTelemetry GenAI Semantic Conventions for trace export! When exporting traces via OTLP with MLFLOW_ENABLE_OTEL_GENAI_SEMCONV enabled, MLflow automatically translates them to follow the OTel GenAI semantic conventions, enabling seamless integration with OTel-compatible observability platforms while preserving GenAI-specific metadata. Docs (#21494, #21495, @​B-Step62)
  • 🔧 OpenCode Tracing Integration: Debug smarter with OpenCode CLI integration! Track and analyze code execution flows directly from your development workflow, making it easier to identify performance bottlenecks and trace issues back to specific code paths. Docs (#20133, @​joelrobin18)
  • ⚡ Native UV Support for Model Dependencies: Automatic dependency inference now supports UV! MLflow automatically detects UV projects and captures exact, locked dependencies from your lockfile when logging models, ensuring reproducible environments. Docs (#20344, #20935, @​debu-sinha)
  • 🔒 Pickle-Free Model Serialization: Enhance security with pickle-free model formats! MLflow now supports safer model serialization using torch.export and skops formats, with improved controls when MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False. Comprehensive documentation guides you through migrating existing models to pickle-free formats for production deployments. Docs (#21404, #21188, #20774, @​WeichenXu123)

Breaking Changes:

  • ⚠️ TypeScript SDK Package Renaming: The MLflow TypeScript SDK packages have been renamed to use npm organization scoping. If you're using the TypeScript SDK, update your package.json dependencies and import statements: mlflow-tracing → @mlflow/core, mlflow-openai → @mlflow/openai, mlflow-anthropic → @mlflow/anthropic, mlflow-gemini → @mlflow/gemini. All packages are now at version 0.2.0. (#20792, @​B-Step62)
  • Remove MLFLOW_ENABLE_INCREMENTAL_SPAN_EXPORT environment variable (#22182, @​PattaraS)
  • Remove litellm and gepa from genai extras (#22059, @​TomeHirata)
  • Block / and : in Registered Model names (#21458, @​Bhuvan-08)

Features:

... (truncated)

Commits

Updates mlflow-tracing from 3.10.1 to 3.11.1

Release notes

Sourced from mlflow-tracing's releases.

v3.11.0rc1

Stripped third-party dependencies from evaluation and AI Gateway features, replacing external provider routing with built-in implementations.

v3.11.0rc0

We're excited to announce MLflow 3.11.0rc0, which includes several notable updates:

Major New Features:

  • 🔍 Automatic Issue Identification: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. (#21431, #21204, #21165, #21163, #21161, @​smoorjani, @​serena-ruan)
  • 💰 Gateway Budget Alerts & Limits: Control your AI Gateway spending with configurable budget policies! Set spending limits by time window (daily, weekly, or monthly), receive alerts before hitting limits, and prevent runaway costs with automatic request blocking. The new budget management UI lets you track spending, configure webhooks for notifications, and monitor violations across all your gateway endpoints. (#21116, #21534, #21569, #21473, #21108, @​TomeHirata, @​copilot-swe-agent)
  • 📊 Trace Graph View: Visualize complex trace hierarchies with an interactive graph view! Navigate multi-level trace structures, understand parent-child relationships at a glance, and debug complex systems more effectively with a visual representation of your trace topology. (#20607, @​joelrobin18)
  • 🌐 Native OpenTelemetry GenAI Convention Support: MLflow now natively supports the OpenTelemetry GenAI Semantic Conventions for trace export! When exporting traces via OTLP with MLFLOW_ENABLE_OTEL_GENAI_SEMCONV enabled, MLflow automatically translates them to follow the OTel GenAI semantic conventions, enabling seamless integration with OTel-compatible observability platforms while preserving GenAI-specific metadata. (#21494, #21495, @​B-Step62)
  • 🔧 Opencode Tracing Integration: Debug smarter with Opencode CLI integration! Track and analyze code execution flows directly from your development workflow, making it easier to identify performance bottlenecks and trace issues back to specific code paths. (#20133, @​joelrobin18)
  • ⚡ UV Package Manager Support: Automatic dependency inference now supports UV! MLflow automatically detects UV projects and captures exact, locked dependencies from your lockfile when logging models, ensuring reproducible environments. (#20344, #20935, @​debu-sinha)
  • 🔒 Pickle-Free Model Serialization: Enhance security with pickle-free model formats! MLflow now supports safer model serialization using torch.export and skops formats, with improved controls when MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False. Comprehensive documentation guides you through migrating existing models to pickle-free formats for production deployments. (#21404, #21188, #20774, @​WeichenXu123)

Breaking Changes:

  • ⚠️ TypeScript SDK Package Renaming: The MLflow TypeScript SDK packages have been renamed to use npm organization scoping. If you're using the TypeScript SDK, update your package.json dependencies and import statements: mlflow-tracing → @mlflow/core, mlflow-openai → @mlflow/openai, mlflow-anthropic → @mlflow/anthropic, mlflow-gemini → @mlflow/gemini. All packages are now at version 0.2.0. (#20792, @​B-Step62)

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

To try out this release candidate, please run:

pip install mlflow==3.11.0rc0

Changelog

Sourced from mlflow-tracing's changelog.

CHANGELOG

3.11.0 (2026-04-07)

MLflow 3.11.0 includes several major features and improvements.

Major New Features:

  • 🔍 Automatic Issue Identification: Automatically identify quality issues in your agent with AI! Use the new "Detect Issues" button in the traces table to analyze selected traces and surface potential problems across categories like correctness, safety, and performance. Issues are linked directly to traces for easy investigation and debugging. Docs (#21431, #21204, #21165, #21163, #21161, @​smoorjani, @​serena-ruan)
  • 💰 Gateway Budget Alerts & Limits: Control your AI Gateway spending with configurable budget policies! Set spending limits by time window (daily, weekly, or monthly), receive alerts before hitting limits, and prevent runaway costs with automatic request blocking. The new budget management UI lets you track spending, configure webhooks for notifications, and monitor violations across all your gateway endpoints. Docs (#21116, #21534, #21569, #21473, #21108, @​TomeHirata, @​copilot-swe-agent)
  • 📊 Trace Graph View: Visualize complex trace hierarchies with an interactive graph view! Navigate multi-level trace structures, understand parent-child relationships at a glance, and debug complex systems more effectively with a visual representation of your trace topology. Docs (#20607, @​joelrobin18)
  • 🌐 Native OpenTelemetry GenAI Convention Support: MLflow now natively supports the OpenTelemetry GenAI Semantic Conventions for trace export! When exporting traces via OTLP with MLFLOW_ENABLE_OTEL_GENAI_SEMCONV enabled, MLflow automatically translates them to follow the OTel GenAI semantic conventions, enabling seamless integration with OTel-compatible observability platforms while preserving GenAI-specific metadata. Docs (#21494, #21495, @​B-Step62)
  • 🔧 OpenCode Tracing Integration: Debug smarter with OpenCode CLI integration! Track and analyze code execution flows directly from your development workflow, making it easier to identify performance bottlenecks and trace issues back to specific code paths. Docs (#20133, @​joelrobin18)
  • ⚡ Native UV Support for Model Dependencies: Automatic dependency inference now supports UV! MLflow automatically detects UV projects and captures exact, locked dependencies from your lockfile when logging models, ensuring reproducible environments. Docs (#20344, #20935, @​debu-sinha)
  • 🔒 Pickle-Free Model Serialization: Enhance security with pickle-free model formats! MLflow now supports safer model serialization using torch.export and skops formats, with improved controls when MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False. Comprehensive documentation guides you through migrating existing models to pickle-free formats for production deployments. Docs (#21404, #21188, #20774, @​WeichenXu123)

Breaking Changes:

  • ⚠️ TypeScript SDK Package Renaming: The MLflow TypeScript SDK packages have been renamed to use npm organization scoping. If you're using the TypeScript SDK, update your package.json dependencies and import statements: mlflow-tracing → @mlflow/core, mlflow-openai → @mlflow/openai, mlflow-anthropic → @mlflow/anthropic, mlflow-gemini → @mlflow/gemini. All packages are now at version 0.2.0. (#20792, @​B-Step62)
  • Remove MLFLOW_ENABLE_INCREMENTAL_SPAN_EXPORT environment variable (#22182, @​PattaraS)
  • Remove litellm and gepa from genai extras (#22059, @​TomeHirata)
  • Block / and : in Registered Model names (#21458, @​Bhuvan-08)

Features:

  • [Tracking] Update Databricks API calls to use new gRPC APIs instead of py4j APIs (#22205, @​WeichenXu123)
  • [Evaluation] Extend _get_provider_instance with groq, deepseek, xai, openrouter, ollama, databricks, vertex_ai (#22148, @​kriscon-db)
  • [UI] Move native providers to non-LiteLLM in gateway UI (#22203, @​TomeHirata)
  • [Tracing / Tracking] Add trace_location parameter to create_experiment (#22075, @​dbrx-euirim)
  • [Gateway] Complete Bedrock provider with Converse API support (#21999, @​TomeHirata)
  • [Gateway] Add native Vertex AI gateway provider (#21998, @​TomeHirata)
  • [Gateway] Add native Databricks gateway provider (#21997, @​TomeHirata)
  • [Gateway] Add native Ollama gateway provider (#21995, @​TomeHirata)
  • [Gateway] Add native xAI (Grok) gateway provider (#21993, @​TomeHirata)
  • [Tracing] Use bulk upsert in log_spans() to eliminate per-span ORM overhead (#21954, @​harupy)
  • [Tracing] Add builtin cost_per_token to remove litellm dependency for cost tracking (#22046, @​TomeHirata)
  • [Evaluation] Remove LiteLLM hard dependency from the discovery pipeline and judge adapters (#21739, @​harupy)
  • [Evaluation] Add pipelined predict-score execution for mlflow.genai.evaluate (#20940, @​alkispoly-db)
  • [Tracing / Tracking] Default trace location table_prefix to experiment ID in set_experiment (#21815, @​danielseong1)
  • [Tracking] Add default uvicorn log config with timestamps (#21838, @​harupy)
  • [Tracing / UI] Add Session ID filter to GenAI traces table filter dropdown (#21794, @​daniellok-db)
  • [Evaluation / UI] Add Default Credential Chain auth mode for Bedrock/SageMaker in AI Gateway (#21061, @​timsolovev)
  • [UI] Add multi metric bar chart support (#21258, @​RenzoMXD)
  • [Tracking] Add TCP keepalive to HTTP sessions to detect stale connections and reduce timeout han...

    Description has been truncated

@dependabot
chore(deps): bump the training-dependencies group
7b5d76f 
Bumps the training-dependencies group in /training/rl with 7 updates:

| Package | From | To |
| --- | --- | --- |
| [marshmallow](https://github.com/marshmallow-code/marshmallow) | `4.2.3` | `4.3.0` |
| [mlflow](https://github.com/mlflow/mlflow) | `3.10.1` | `3.11.1` |
| [azure-monitor-opentelemetry-exporter](https://github.com/Azure/azure-sdk-for-python) | `1.0.0b50` | `1.0.0b51` |
| [cuda-pathfinder](https://github.com/NVIDIA/cuda-python) | `1.5.1` | `1.5.2` |
| [mlflow-skinny](https://github.com/mlflow/mlflow) | `3.10.1` | `3.11.1` |
| [mlflow-tracing](https://github.com/mlflow/mlflow) | `3.10.1` | `3.11.1` |
| [uvicorn](https://github.com/Kludex/uvicorn) | `0.43.0` | `0.44.0` |


Updates `marshmallow` from 4.2.3 to 4.3.0
- [Changelog](https://github.com/marshmallow-code/marshmallow/blob/dev/CHANGELOG.rst)
- [Commits](marshmallow-code/marshmallow@4.2.3...4.3.0)

Updates `mlflow` from 3.10.1 to 3.11.1
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](https://github.com/mlflow/mlflow/commits)

Updates `azure-monitor-opentelemetry-exporter` from 1.0.0b50 to 1.0.0b51
- [Release notes](https://github.com/Azure/azure-sdk-for-python/releases)
- [Commits](Azure/azure-sdk-for-python@azure-monitor-opentelemetry-exporter_1.0.0b50...azure-monitor-opentelemetry-exporter_1.0.0b51)

Updates `cuda-pathfinder` from 1.5.1 to 1.5.2
- [Release notes](https://github.com/NVIDIA/cuda-python/releases)
- [Commits](NVIDIA/cuda-python@cuda-pathfinder-v1.5.1...cuda-pathfinder-v1.5.2)

Updates `mlflow-skinny` from 3.10.1 to 3.11.1
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](https://github.com/mlflow/mlflow/commits)

Updates `mlflow-tracing` from 3.10.1 to 3.11.1
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](https://github.com/mlflow/mlflow/commits)

Updates `uvicorn` from 0.43.0 to 0.44.0
- [Release notes](https://github.com/Kludex/uvicorn/releases)
- [Changelog](https://github.com/Kludex/uvicorn/blob/main/docs/release-notes.md)
- [Commits](Kludex/uvicorn@0.43.0...0.44.0)

---
updated-dependencies:
- dependency-name: marshmallow
  dependency-version: 4.3.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: training-dependencies
- dependency-name: mlflow
  dependency-version: 3.11.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: training-dependencies
- dependency-name: azure-monitor-opentelemetry-exporter
  dependency-version: 1.0.0b51
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: training-dependencies
- dependency-name: cuda-pathfinder
  dependency-version: 1.5.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: training-dependencies
- dependency-name: mlflow-skinny
  dependency-version: 3.11.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: training-dependencies
- dependency-name: mlflow-tracing
  dependency-version: 3.11.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: training-dependencies
- dependency-name: uvicorn
  dependency-version: 0.44.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: training-dependencies
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added dependencies Dependency version updates python Pull requests that update python code training labels Apr 7, 2026
@github-actions github-actions bot changed the title chore(deps): bump the training-dependencies group in /training/rl with 7 updates security(deps): bump the training-dependencies group in /training/rl with 7 updates Apr 7, 2026
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github-actions bot commented Apr 7, 2026

Dependency Review

The following issues were found:
  • ✅ 0 vulnerable package(s)
  • ✅ 0 package(s) with incompatible licenses
  • ✅ 0 package(s) with invalid SPDX license definitions
  • ⚠️ 7 package(s) with unknown licenses.
See the Details below.

Snapshot Warnings

⚠️: No snapshots were found for the head SHA 7b5d76f.
Ensure that dependencies are being submitted on PR branches and consider enabling retry-on-snapshot-warnings. See the documentation for more information and troubleshooting advice.

License Issues

training/rl/pyproject.toml

PackageVersionLicenseIssue Type
marshmallow4.3.0NullUnknown License
mlflow3.11.1NullUnknown License

training/rl/requirements.txt

PackageVersionLicenseIssue Type
cuda-pathfinder1.5.2NullUnknown License
mlflow3.11.1NullUnknown License
mlflow-skinny3.11.1NullUnknown License
mlflow-tracing3.11.1NullUnknown License
uvicorn0.44.0NullUnknown License

OpenSSF Scorecard

PackageVersionScoreDetails
pip/marshmallow 4.3.0 UnknownUnknown
pip/mlflow 3.11.1 UnknownUnknown
pip/azure-monitor-opentelemetry-exporter 1.0.0b51 🟢 6.7
Details
CheckScoreReason
Code-Review🟢 10all changesets reviewed
Maintained🟢 1030 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10
Packaging⚠️ -1packaging workflow not detected
CII-Best-Practices🟢 5badge detected: Passing
Dangerous-Workflow🟢 10no dangerous workflow patterns detected
Security-Policy🟢 10security policy file detected
Token-Permissions⚠️ 0detected GitHub workflow tokens with excessive permissions
License🟢 10license file detected
Signed-Releases⚠️ -1no releases found
Branch-Protection🟢 5branch protection is not maximal on development and all release branches
Binary-Artifacts🟢 8binaries present in source code
SAST⚠️ 0SAST tool is not run on all commits -- score normalized to 0
Pinned-Dependencies⚠️ 0dependency not pinned by hash detected -- score normalized to 0
Fuzzing🟢 10project is fuzzed
pip/cuda-pathfinder 1.5.2 UnknownUnknown
pip/mlflow 3.11.1 UnknownUnknown
pip/mlflow-skinny 3.11.1 UnknownUnknown
pip/mlflow-tracing 3.11.1 UnknownUnknown
pip/uvicorn 0.44.0 UnknownUnknown

Scanned Files

  • training/rl/pyproject.toml
  • training/rl/requirements.txt

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codecov-commenter commented Apr 7, 2026 •
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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 50.48%. Comparing base (0c29148) to head (7b5d76f).

Additional details and impacted files 
@@           Coverage Diff           @@
##             main     #408   +/-   ##
=======================================
  Coverage   50.48%   50.48%           
=======================================
  Files         267      267           
  Lines       18188    18188           
  Branches     1903     1903           
=======================================
  Hits         9182     9182           
  Misses       8716     8716           
  Partials      290      290
Flag Coverage Δ *Carryforward flag
pester 81.21% <ø> (ø)
pytest 6.89% <ø> (ø) Carriedforward from 0c29148
pytest-dataviewer 61.97% <ø> (ø)
vitest 50.72% <ø> (ø)

*This pull request uses carry forward flags. Click here to find out more.

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