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Bump the pip group across 5 directories with 3 updates#27

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Bump the pip group across 5 directories with 3 updates#27
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@dependabot dependabot bot commented on behalf of github Mar 26, 2026

Bumps the pip group with 1 update in the /EdgeFieldDay2025/EdgeAI_on_ZKS/edge-ai-car-classification-main/sync-sidecar directory: requests.
Bumps the pip group with 1 update in the /EdgeFieldDay2025/edge-ai-car-classification/sync-sidecar directory: requests.
Bumps the pip group with 2 updates in the /edgeai/efficientnet-b3-stanford-cars/mlruns/387138003911017072/models/m-5af6dec84a924fdda55144b9ef86abdd/artifacts directory: mlflow and onnx.
Bumps the pip group with 2 updates in the /edgeai/efficientnet-b3-stanford-cars/mlruns/387138003911017072/models/m-5efb4eab983947ca98b18aadd7d74183/artifacts directory: mlflow and onnx.
Bumps the pip group with 2 updates in the /edgeai/efficientnet-b5-stanford-cars/mlruns/505550186997757802/models/m-dcb6682ff42b407a9e297fe345ca5355/artifacts directory: mlflow and onnx.

Updates requests from 2.32.4 to 2.33.0

Release notes

Sourced from requests's releases.

v2.33.0

2.33.0 (2026-03-25)

Announcements

  • 📣 Requests is adding inline types. If you have a typed code base that uses Requests, please take a look at #7271. Give it a try, and report any gaps or feedback you may have in the issue. 📣

Security

  • CVE-2026-25645 requests.utils.extract_zipped_paths now extracts contents to a non-deterministic location to prevent malicious file replacement. This does not affect default usage of Requests, only applications calling the utility function directly.

Improvements

  • Migrated to a PEP 517 build system using setuptools. (#7012)

Bugfixes

  • Fixed an issue where an empty netrc entry could cause malformed authentication to be applied to Requests on Python 3.11+. (#7205)

Deprecations

  • Dropped support for Python 3.9 following its end of support. (#7196)

Documentation

  • Various typo fixes and doc improvements.

New Contributors

Full Changelog: https://github.com/psf/requests/blob/main/HISTORY.md#2330-2026-03-25

v2.32.5

2.32.5 (2025-08-18)

Bugfixes

  • The SSLContext caching feature originally introduced in 2.32.0 has created a new class of issues in Requests that have had negative impact across a number of use cases. The Requests team has decided to revert this feature as long term maintenance of it is proving to be unsustainable in its current iteration.

Deprecations

  • Added support for Python 3.14.
  • Dropped support for Python 3.8 following its end of support.
Changelog

Sourced from requests's changelog.

2.33.0 (2026-03-25)

Announcements

  • 📣 Requests is adding inline types. If you have a typed code base that uses Requests, please take a look at #7271. Give it a try, and report any gaps or feedback you may have in the issue. 📣

Security

  • CVE-2026-25645 requests.utils.extract_zipped_paths now extracts contents to a non-deterministic location to prevent malicious file replacement. This does not affect default usage of Requests, only applications calling the utility function directly.

Improvements

  • Migrated to a PEP 517 build system using setuptools. (#7012)

Bugfixes

  • Fixed an issue where an empty netrc entry could cause malformed authentication to be applied to Requests on Python 3.11+. (#7205)

Deprecations

  • Dropped support for Python 3.9 following its end of support. (#7196)

Documentation

  • Various typo fixes and doc improvements.

2.32.5 (2025-08-18)

Bugfixes

  • The SSLContext caching feature originally introduced in 2.32.0 has created a new class of issues in Requests that have had negative impact across a number of use cases. The Requests team has decided to revert this feature as long term maintenance of it is proving to be unsustainable in its current iteration.

Deprecations

  • Added support for Python 3.14.
  • Dropped support for Python 3.8 following its end of support.
Commits
  • bc04dfd v2.33.0
  • 66d21cb Merge commit from fork
  • 8b9bc8f Move badges to top of README (#7293)
  • e331a28 Remove unused extraction call (#7292)
  • 753fd08 docs: fix FAQ grammar in httplib2 example
  • 774a0b8 docs(socks): same block as other sections
  • 9c72a41 Bump github/codeql-action from 4.33.0 to 4.34.1
  • ebf7190 Bump github/codeql-action from 4.32.0 to 4.33.0
  • 0e4ae38 docs: exclude Response.is_permanent_redirect from API docs (#7244)
  • d568f47 docs: clarify Quickstart POST example (#6960)
  • Additional commits viewable in compare view

Updates requests from 2.32.4 to 2.33.0

Release notes

Sourced from requests's releases.

v2.33.0

2.33.0 (2026-03-25)

Announcements

  • 📣 Requests is adding inline types. If you have a typed code base that uses Requests, please take a look at #7271. Give it a try, and report any gaps or feedback you may have in the issue. 📣

Security

  • CVE-2026-25645 requests.utils.extract_zipped_paths now extracts contents to a non-deterministic location to prevent malicious file replacement. This does not affect default usage of Requests, only applications calling the utility function directly.

Improvements

  • Migrated to a PEP 517 build system using setuptools. (#7012)

Bugfixes

  • Fixed an issue where an empty netrc entry could cause malformed authentication to be applied to Requests on Python 3.11+. (#7205)

Deprecations

  • Dropped support for Python 3.9 following its end of support. (#7196)

Documentation

  • Various typo fixes and doc improvements.

New Contributors

Full Changelog: https://github.com/psf/requests/blob/main/HISTORY.md#2330-2026-03-25

v2.32.5

2.32.5 (2025-08-18)

Bugfixes

  • The SSLContext caching feature originally introduced in 2.32.0 has created a new class of issues in Requests that have had negative impact across a number of use cases. The Requests team has decided to revert this feature as long term maintenance of it is proving to be unsustainable in its current iteration.

Deprecations

  • Added support for Python 3.14.
  • Dropped support for Python 3.8 following its end of support.
Changelog

Sourced from requests's changelog.

2.33.0 (2026-03-25)

Announcements

  • 📣 Requests is adding inline types. If you have a typed code base that uses Requests, please take a look at #7271. Give it a try, and report any gaps or feedback you may have in the issue. 📣

Security

  • CVE-2026-25645 requests.utils.extract_zipped_paths now extracts contents to a non-deterministic location to prevent malicious file replacement. This does not affect default usage of Requests, only applications calling the utility function directly.

Improvements

  • Migrated to a PEP 517 build system using setuptools. (#7012)

Bugfixes

  • Fixed an issue where an empty netrc entry could cause malformed authentication to be applied to Requests on Python 3.11+. (#7205)

Deprecations

  • Dropped support for Python 3.9 following its end of support. (#7196)

Documentation

  • Various typo fixes and doc improvements.

2.32.5 (2025-08-18)

Bugfixes

  • The SSLContext caching feature originally introduced in 2.32.0 has created a new class of issues in Requests that have had negative impact across a number of use cases. The Requests team has decided to revert this feature as long term maintenance of it is proving to be unsustainable in its current iteration.

Deprecations

  • Added support for Python 3.14.
  • Dropped support for Python 3.8 following its end of support.
Commits
  • bc04dfd v2.33.0
  • 66d21cb Merge commit from fork
  • 8b9bc8f Move badges to top of README (#7293)
  • e331a28 Remove unused extraction call (#7292)
  • 753fd08 docs: fix FAQ grammar in httplib2 example
  • 774a0b8 docs(socks): same block as other sections
  • 9c72a41 Bump github/codeql-action from 4.33.0 to 4.34.1
  • ebf7190 Bump github/codeql-action from 4.32.0 to 4.33.0
  • 0e4ae38 docs: exclude Response.is_permanent_redirect from API docs (#7244)
  • d568f47 docs: clarify Quickstart POST example (#6960)
  • Additional commits viewable in compare view

Updates mlflow from 3.5.0rc0 to 3.9.0rc0

Release notes

Sourced from mlflow's releases.

v3.9.0rc0

We're excited to announce MLflow 3.9.0rc0, a pre-release including several notable updates:

Major New Features:

  • 🔮 MLflow Assistant: Figuring out the next steps to debug your apps and agents can be challenging. We're excited to introduce the MLflow Assistant, an in-product chatbot that can help you identify, diagnose, and fix issues. The assistant is backed by Claude Code, and directly passes context from the MLflow UI to Claude. Click on the floating "Assistant" button in the bottom right of the MLflow UI to get started!
  • 📈 Trace Overview Dashboard: You can now get insights into your agent's performance at a glance with the new "Overview" tab in GenAI experiments. Many pre-built statistics are available out of the box, including performance metrics (e.g. latency, request count), quality metrics (based on assessments), and tool call summaries. If there are any additional charts you'd like to see, please feel free to raise an issue in the MLflow repository!
  • ✨ AI Gateway: We're revamping our AI Gateway feature! AI Gateway provides a unified interface for your API requests, allowing you to route queries to your LLM provider(s) of choice. In MLflow 3.9.0rc0, the Gateway server is now located directly in the tracking server, so you don't need to spin up a new process. Additional features such as passthrough endpoints, traffic splits, and fallback models are also available, with more to come soon! For more detailed information, please take a look at the docs.
  • 🔎 Online Monitoring with LLM Judges: Configure LLM judges to automatically run on your traces, without having to write a line of code! You can either use one of our pre-defined judges, or provide your own prompt and instructions to create custom metrics. Head to the new "Judges" tab within the GenAI Experiment UI to get started.
  • 🤖 Judge Builder UI: Define and iterate on custom LLM judge prompts directly from the UI! Within the new "Judges" tab, you can create your own prompt for an LLM judge, and test-run it on your traces to see what the output would be. Once you're happy with it, you can either use it for online monitoring (as mentioned above), or use it via the Python SDK for your evals.
  • 🔗 Distributed Tracing: Trace context can now be propagated across different services and processes, allowing you to truly track request lifecycles from end to end. The related APIs are defined in the mlflow.tracing.distributed module (with more documentation to come soon).
  • 📚 MemAlign - a new judge optimizer algorithm: We're excited to introduce MemAlignOptimizer, a new algorithm that makes your judges smarter over time. It learns general guidelines from past feedback while dynamically retrieving relevant examples at runtime, giving you more accurate evaluations.

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==3.9.0rc0

Please try it out and report any issues on the issue tracker.

v3.8.1

MLflow 3.8.1 includes several bug fixes and documentation updates.

Bug fixes:

Small bug fixes and documentation updates:

#19539, #19451, #19409, @​smoorjani; #19493, @​alkispoly-db

v3.8.0

MLflow 3.8.0 includes several major features and improvements

Major Features

  • ⚙️ Prompt Model Configuration: Prompts can now include model configuration, allowing you to associate specific model settings with prompt templates for more reproducible LLM workflows. (#18963, #19174, #19279, @​chenmoneygithub)
  • ⏳ In-Progress Trace Display: The Traces UI now supports displaying spans from in-progress traces with auto-polling, enabling real-time debugging and monitoring of long-running LLM applications. (#19265, @​B-Step62)
  • ⚖️ DeepEval and RAGAS Judges Integration: New get_judge API enables using DeepEval and RAGAS evaluation metrics as MLflow scorers, providing access to 20+ evaluation metrics including answer relevancy, faithfulness, and hallucination detection. (#18988, @​smoorjani, #19345, @​SomtochiUmeh)
  • 🛡️ Conversational Safety Scorer: New built-in scorer for evaluating safety of multi-turn conversations, analyzing entire conversation histories for hate speech, harassment, violence, and other safety concerns. (#19106, @​joelrobin18)
  • ⚡ Conversational Tool Call Efficiency Scorer: New built-in scorer for evaluating tool call efficiency in multi-turn agent interactions, detecting redundant calls, missing batching opportunities, and poor tool selections. (#19245, @​joelrobin18)

Important Notice

  • Collection of UI Telemetry. From MLflow 3.8.0 onwards, MLflow will collect anonymized data about UI interactions, similar to the telemetry we collect for the Python SDK. If you manage your own server, UI telemetry is automatically disabled by setting the existing environment variables: MLFLOW_DISABLE_TELEMETRY=true or DO_NOT_TRACK=true. If you do not manage your own server (e.g. you use a managed service or are not the admin), you can still opt out personally via the new "Settings" tab in the MLflow UI. For more information, please read the documentation on usage tracking.

... (truncated)

Changelog

Sourced from mlflow's changelog.

3.9.0rc0 (2026-01-15)

We're excited to announce MLflow 3.9.0rc0, a pre-release including several notable updates:

Major New Features:

  • 🔮 MLflow Assistant: Figuring out the next steps to debug your apps and agents can be challenging. We're excited to introduce the MLflow Assistant, an in-product chatbot that can help you identify, diagnose, and fix issues. The assistant is backed by Claude Code, and directly passes context from the MLflow UI to Claude. Click on the floating "Assistant" button in the bottom right of the MLflow UI to get started!
  • 📈 Trace Overview Dashboard: You can now get insights into your agent's performance at a glance with the new "Overview" tab in GenAI experiments. Many pre-built statistics are available out of the box, including performance metrics (e.g. latency, request count), quality metrics (based on assessments), and tool call summaries. If there are any additional charts you'd like to see, please feel free to raise an issue in the MLflow repository!
  • ✨ AI Gateway: We're revamping our AI Gateway feature! AI Gateway provides a unified interface for your API requests, allowing you to route queries to your LLM provider(s) of choice. In MLflow 3.9.0rc0, the Gateway server is now located directly in the tracking server, so you don't need to spin up a new process. Additional features such as passthrough endpoints, traffic splits, and fallback models are also available, with more to come soon! For more detailed information, please take a look at the docs.
  • 🔎 Online Monitoring with LLM Judges: Configure LLM judges to automatically run on your traces, without having to write a line of code! You can either use one of our pre-defined judges, or provide your own prompt and instructions to create custom metrics. Head to the new "Judges" tab within the GenAI Experiment UI to get started.
  • 🤖 Judge Builder UI: Define and iterate on custom LLM judge prompts directly from the UI! Within the new "Judges" tab, you can create your own prompt for an LLM judge, and test-run it on your traces to see what the output would be. Once you're happy with it, you can either use it for online monitoring (as mentioned above), or use it via the Python SDK for your evals.
  • 🔗 Distributed Tracing: Trace context can now be propagated across different services and processes, allowing you to truly track request lifecycles from end to end. The related APIs are defined in the mlflow.tracing.distributed module (with more documentation to come soon).
  • 📚 MemAlign - a new judge optimizer algorithm: We're excited to introduce MemAlignOptimizer, a new algorithm that makes your judges smarter over time. It learns general guidelines from past feedback while dynamically retrieving relevant examples at runtime, giving you more accurate evaluations.

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==3.9.0rc0

3.8.1 (2025-12-26)

MLflow 3.8.1 includes several bug fixes and documentation updates.

Bug fixes:

Small bug fixes and documentation updates:

#19539, #19451, #19409, @​smoorjani; #19493, @​alkispoly-db

3.8.0 (2025-12-19)

MLflow 3.8.0 includes several major features and improvements

Major Features

  • ⚙️ Prompt Model Configuration: Prompts can now include model configuration, allowing you to associate specific model settings with prompt templates for more reproducible LLM workflows. (#18963, #19174, #19279, @​chenmoneygithub)
  • ⏳ In-Progress Trace Display: The Traces UI now supports displaying spans from in-progress traces with auto-polling, enabling real-time debugging and monitoring of long-running LLM applications. (#19265, @​B-Step62)
  • ⚖️ DeepEval Judges Integration: New get_judge API enables using DeepEval's evaluation metrics as MLflow scorers, providing access to 20+ evaluation metrics including answer relevancy, faithfulness, and hallucination detection. (#18988, @​smoorjani)
  • 🛡️ Conversational Safety Scorer: New built-in scorer for evaluating safety of multi-turn conversations, analyzing entire conversation histories for hate speech, harassment, violence, and other safety concerns. (#19106, @​joelrobin18)
  • ⚡ Conversational Tool Call Efficiency Scorer: New built-in scorer for evaluating tool call efficiency in multi-turn agent interactions, detecting redundant calls, missing batching opportunities, and poor tool selections. (#19245, @​joelrobin18)

Important Notice

  • Collection of UI Telemetry. From MLflow 3.8.0 onwards, MLflow will collect anonymized data about UI interactions, similar to the telemetry we collect for the Python SDK. If you manage your own server, UI telemetry is automatically disabled by setting the existing environment variables: MLFLOW_DISABLE_TELEMETRY=true or DO_NOT_TRACK=true. If you do not manage your own server (e.g. you use a managed service or are not the admin), you can still opt out personally via the new "Settings" tab in the MLflow UI. For more information, please read the documentation on usage tracking.

... (truncated)

Commits

Updates onnx from 1.19.0 to 1.21.0rc1

Release notes

Sourced from onnx's releases.

v1.20.1

[!NOTE] This patch release includes important bug fixes to ONNX build.

What's Changed

Commits

Full Changelog: onnx/onnx@v1.20.0...v1.20.1

v1.20.0

ONNX v1.20.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

Updated Op list:

Cast, CastLike, Constant, ConstantOfShape, DequantizeLinear, Flatten, Identity, If, Loop, Pad, QuantizeLinear, Reshape, Scan, Shape, Size, Squeeze, Transpose, Unsqueeze

Key Updates:

  • Support for Python 3.14 via Python's stable ABI. (#7276)
  • Opset 25
  • 2-bit dtype support (#7446)
  • A new "node determinism" attribute in operator schemas (#7176)

Breaking Changes and Deprecations

  • Update manylinux_2014 -> manylinux_2_28 (#7151)
  • Update attention gqa to use repeat interleave within repeat kv (#7274)
  • Update required Python version to 3.10 and related fixes (#7220)
  • Remove Python 3.9 wheel build (#7217)
  • Remove deprecated methods (#7214)

Spec and Operator

  • Add 2 bit support to onnx (#7446)
  • Remove enforcement to node determinism attribute (#7473)
  • Fix handling of empty inputs for the Softmax operator (#7206)
  • Fix OneHotEncoder segfault due to missing input shape validation (#7302)
  • Fix Attention backend test: correct dimension of Range input (#7300)
  • Fix Range input rank in Attention op function definition (#7240)

... (truncated)

Commits

Updates mlflow from 3.5.0rc0 to 3.9.0rc0

Release notes

Sourced from mlflow's releases.

v3.9.0rc0

We're excited to announce MLflow 3.9.0rc0, a pre-release including several notable updates:

Major New Features:

  • 🔮 MLflow Assistant: Figuring out the next steps to debug your apps and agents can be challenging. We're excited to introduce the MLflow Assistant, an in-product chatbot that can help you identify, diagnose, and fix issues. The assistant is backed by Claude Code, and directly passes context from the MLflow UI to Claude. Click on the floating "Assistant" button in the bottom right of the MLflow UI to get started!
  • 📈 Trace Overview Dashboard: You can now get insights into your agent's performance at a glance with the new "Overview" tab in GenAI experiments. Many pre-built statistics are available out of the box, including performance metrics (e.g. latency, request count), quality metrics (based on assessments), and tool call summaries. If there are any additional charts you'd like to see, please feel free to raise an issue in the MLflow repository!
  • ✨ AI Gateway: We're revamping our AI Gateway feature! AI Gateway provides a unified interface for your API requests, allowing you to route queries to your LLM provider(s) of choice. In MLflow 3.9.0rc0, the Gateway server is now located directly in the tracking server, so you don't need to spin up a new process. Additional features such as passthrough endpoints, traffic splits, and fallback models are also available, with more to come soon! For more detailed information, please take a look at the docs.
  • 🔎 Online Monitoring with LLM Judges: Configure LLM judges to automatically run on your traces, without having to write a line of code! You can either use one of our pre-defined judges, or provide your own prompt and instructions to create custom metrics. Head to the new "Judges" tab within the GenAI Experiment UI to get started.
  • 🤖 Judge Builder UI: Define and iterate on custom LLM judge prompts directly from the UI! Within the new "Judges" tab, you can create your own prompt for an LLM judge, and test-run it on your traces to see what the output would be. Once you're happy with it, you can either use it for online monitoring (as mentioned above), or use it via the Python SDK for your evals.
  • 🔗 Distributed Tracing: Trace context can now be propagated across different services and processes, allowing you to truly track request lifecycles from end to end. The related APIs are defined in the mlflow.tracing.distributed module (with more documentation to come soon).
  • 📚 MemAlign - a new judge optimizer algorithm: We're excited to introduce MemAlignOptimizer, a new algorithm that makes your judges smarter over time. It learns general guidelines from past feedback while dynamically retrieving relevant examples at runtime, giving you more accurate evaluations.

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==3.9.0rc0

Please try it out and report any issues on the issue tracker.

v3.8.1

MLflow 3.8.1 includes several bug fixes and documentation updates.

Bug fixes:

Small bug fixes and documentation updates:

#19539, #19451, #19409, @​smoorjani; #19493, @​alkispoly-db

v3.8.0

MLflow 3.8.0 includes several major features and improvements

Major Features

  • ⚙️ Prompt Model Configuration: Prompts can now include model configuration, allowing you to associate specific model settings with prompt templates for more reproducible LLM workflows. (#18963, #19174, #19279, @​chenmoneygithub)
  • ⏳ In-Progress Trace Display: The Traces UI now supports displaying spans from in-progress traces with auto-polling, enabling real-time debugging and monitoring of long-running LLM applications. (#19265, @​B-Step62)
  • ⚖️ DeepEval and RAGAS Judges Integration: New get_judge API enables using DeepEval and RAGAS evaluation metrics as MLflow scorers, providing access to 20+ evaluation metrics including answer relevancy, faithfulness, and hallucination detection. (#18988, @​smoorjani, #19345, @​SomtochiUmeh)
  • 🛡️ Conversational Safety Scorer: New built-in scorer for evaluating safety of multi-turn conversations, analyzing entire conversation histories for hate speech, harassment, violence, and other safety concerns. (#19106, @​joelrobin18)
  • ⚡ Conversational Tool Call Efficiency Scorer: New built-in scorer for evaluating tool call efficiency in multi-turn agent interactions, detecting redundant calls, missing batching opportunities, and poor tool selections. (#19245, @​joelrobin18)

Important Notice

  • Collection of UI Telemetry. From MLflow 3.8.0 onwards, MLflow will collect anonymized data about UI interactions, similar to the telemetry we collect for the Python SDK. If you manage your own server, UI telemetry is automatically disabled by setting the existing environment variables: MLFLOW_DISABLE_TELEMETRY=true or DO_NOT_TRACK=true. If you do not manage your own server (e.g. you use a managed service or are not the admin), you can still opt out personally via the new "Settings" tab in the MLflow UI. For more information, please read the documentation on usage tracking.

... (truncated)

Changelog

Sourced from mlflow's changelog.

3.9.0rc0 (2026-01-15)

We're excited to announce MLflow 3.9.0rc0, a pre-release including several notable updates:

Major New Features:

  • 🔮 MLflow Assistant: Figuring out the next steps to debug your apps and agents can be challenging. We're excited to introduce the MLflow Assistant, an in-product chatbot that can help you identify, diagnose, and fix issues. The assistant is backed by Claude Code, and directly passes context from the MLflow UI to Claude. Click on the floating "Assistant" button in the bottom right of the MLflow UI to get started!
  • 📈 Trace Overview Dashboard: You can now get insights into your agent's performance at a glance with the new "Overview" tab in GenAI experiments. Many pre-built statistics are available out of the box, including performance metrics (e.g. latency, request count), quality metrics (based on assessments), and tool call summaries. If there are any additional charts you'd like to see, please feel free to raise an issue in the MLflow repository!
  • ✨ AI Gateway: We're revamping our AI Gateway feature! AI Gateway provides a unified interface for your API requests, allowing you to route queries to your LLM provider(s) of choice. In MLflow 3.9.0rc0, the Gateway server is now located directly in the tracking server, so you don't need to spin up a new process. Additional features such as passthrough endpoints, traffic splits, and fallback models are also available, with more to come soon! For more detailed information, please take a look at the docs.
  • 🔎 Online Monitoring with LLM Judges: Configure LLM judges to automatically run on your traces, without having to write a line of code! You can either use one of our pre-defined judges, or provide your own prompt and instructions to create custom metrics. Head to the new "Judges" tab within the GenAI Experiment UI to get started.
  • 🤖 Judge Builder UI: Define and iterate on custom LLM judge prompts directly from the UI! Within the new "Judges" tab, you can create your own prompt for an LLM judge, and test-run it on your traces to see what the output would be. Once you're happy with it, you can either use it for online monitoring (as mentioned above), or use it via the Python SDK for your evals.
  • 🔗 Distributed Tracing: Trace context can now be propagated across different services and processes, allowing you to truly track request lifecycles from end to end. The related APIs are defined in the mlflow.tracing.distributed module (with more documentation to come soon).
  • 📚 MemAlign - a new judge optimizer algorithm: We're excited to introduce MemAlignOptimizer, a new algorithm that makes your judges smarter over time. It learns general guidelines from past feedback while dynamically retrieving relevant examples at runtime, giving you more accurate evaluations.

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==3.9.0rc0

3.8.1 (2025-12-26)

MLflow 3.8.1 includes several bug fixes and documentation updates.

Bug fixes:

Small bug fixes and documentation updates:

#19539, #19451, #19409, @​smoorjani; #19493, @​alkispoly-db

3.8.0 (2025-12-19)

MLflow 3.8.0 includes several major features and improvements

Major Features

  • ⚙️ Prompt Model Configuration: Prompts can now include model configuration, allowing you to associate specific model settings with prompt templates for more reproducible LLM workflows. (#18963, #19174, #19279, @​chenmoneygithub)
  • ⏳ In-Progress Trace Display: The Traces UI now supports displaying spans from in-progress traces with auto-polling, enabling real-time debugging and monitoring of long-running LLM applications. (#19265, @​B-Step62)
  • ⚖️ DeepEval Judges Integration: New get_judge API enables using DeepEval's evaluation metrics as MLflow scorers, providing access to 20+ evaluation metrics including answer relevancy, faithfulness, and hallucination detection. (#18988, @​smoorjani)
  • 🛡️ Conversational Safety Scorer: New built-in scorer for evaluating safety of multi-turn conversations, analyzing entire conversation histories for hate speech, harassment, violence, and other safety concerns. (#19106, @​joelrobin18)
  • ⚡ Conversational Tool Call Efficiency Scorer: New built-in scorer for evaluating tool call efficiency in multi-turn agent interactions, detecting redundant calls, missing batching opportunities, and poor tool selections. (#19245, @​joelrobin18)

Important Notice

  • Collection of UI Telemetry. From MLflow 3.8.0 onwards, MLflow will collect anonymized data about UI interactions, similar to the telemetry we collect for the Python SDK. If you manage your own server, UI telemetry is automatically disabled by setting the existing environment variables: MLFLOW_DISABLE_TELEMETRY=true or DO_NOT_TRACK=true. If you do not manage your own server (e.g. you use a managed service or are not the admin), you can still opt out personally via the new "Settings" tab in the MLflow UI. For more information, please read the documentation on usage tracking.

... (truncated)

Commits

Updates onnx from 1.19.0 to 1.21.0rc1

Release notes

Sourced from onnx's releases.

v1.20.1

[!NOTE] This patch release includes important bug fixes to ONNX build.

What's Changed

Commits

Full Changelog: onnx/onnx@v1.20.0...v1.20.1

v1.20.0

ONNX v1.20.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

Updated Op list:

Cast, CastLike, Constant, ConstantOfShape, DequantizeLinear, Flatten, Identity, If, Loop, Pad, QuantizeLinear, Reshape, Scan, Shape, Size, Squeeze, Transpose, Unsqueeze

Key Updates:

  • Support for Python 3.14 via Python's stable ABI. (#7276)
  • Opset 25
  • 2-bit dtype support (#7446)
  • A new "node determinism" attribute in operator schemas (#7176)

Breaking Changes and Deprecations

  • Update manylinux_2014 -> manylinux_2_28 (#7151)
  • Update attention gqa to use repeat interleave within repeat kv (

@dependabot
Bump the pip group across 5 directories with 3 updates
1ffb18b 
Bumps the pip group with 1 update in the /EdgeFieldDay2025/EdgeAI_on_ZKS/edge-ai-car-classification-main/sync-sidecar directory: [requests](https://github.com/psf/requests).
Bumps the pip group with 1 update in the /EdgeFieldDay2025/edge-ai-car-classification/sync-sidecar directory: [requests](https://github.com/psf/requests).
Bumps the pip group with 2 updates in the /edgeai/efficientnet-b3-stanford-cars/mlruns/387138003911017072/models/m-5af6dec84a924fdda55144b9ef86abdd/artifacts directory: [mlflow](https://github.com/mlflow/mlflow) and [onnx](https://github.com/onnx/onnx).
Bumps the pip group with 2 updates in the /edgeai/efficientnet-b3-stanford-cars/mlruns/387138003911017072/models/m-5efb4eab983947ca98b18aadd7d74183/artifacts directory: [mlflow](https://github.com/mlflow/mlflow) and [onnx](https://github.com/onnx/onnx).
Bumps the pip group with 2 updates in the /edgeai/efficientnet-b5-stanford-cars/mlruns/505550186997757802/models/m-dcb6682ff42b407a9e297fe345ca5355/artifacts directory: [mlflow](https://github.com/mlflow/mlflow) and [onnx](https://github.com/onnx/onnx).


Updates `requests` from 2.32.4 to 2.33.0
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.32.4...v2.33.0)

Updates `requests` from 2.32.4 to 2.33.0
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.32.4...v2.33.0)

Updates `mlflow` from 3.5.0rc0 to 3.9.0rc0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v3.5.0rc0...v3.9.0rc0)

Updates `onnx` from 1.19.0 to 1.21.0rc1
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](https://github.com/onnx/onnx/commits)

Updates `mlflow` from 3.5.0rc0 to 3.9.0rc0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v3.5.0rc0...v3.9.0rc0)

Updates `onnx` from 1.19.0 to 1.21.0rc1
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](https://github.com/onnx/onnx/commits)

Updates `mlflow` from 3.5.0rc0 to 3.9.0rc0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v3.5.0rc0...v3.9.0rc0)

Updates `onnx` from 1.19.0 to 1.21.0rc1
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](https://github.com/onnx/onnx/commits)

---
updated-dependencies:
- dependency-name: requests
  dependency-version: 2.33.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: requests
  dependency-version: 2.33.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.9.0rc0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: onnx
  dependency-version: 1.21.0rc1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.9.0rc0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: onnx
  dependency-version: 1.21.0rc1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.9.0rc0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: onnx
  dependency-version: 1.21.0rc1
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Mar 26, 2026
@dependabot dependabot bot mentioned this pull request Mar 26, 2026
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