To disable the interactive prompt for credentials (useful in CI/CD pipelines), use the `interactive` argument: Sorry, something went wrong. Quote reply Collaborator chenmoneygithub Dec 7, 2023 There was a problem hiding this comment. Choose a reason for hiding this comment The reason will be displayed to describe this comment to others. Learn more. Choose a reason Spam Abuse Off Topic Outdated Duplicate Resolved Hide comment I think this is out of the scope of the tracking server overview, non-interactive mode is used in automation pipeline. Can we delete this part?

New unit/integration tests

What changes are proposed in this pull request? The mlflow.evaluate API includes a predictions argument that selects the column from the return of predict_fn when it is a dataframe. That logic was previously implemented in _extract_output_and_other_columns as part of the default evaluator. When #13360 refactors the evaluators into multiple class, that function was omitted from the regressor evaluator. #14388 intended to fix it but simply threw an error if predict_fn returns a dataframe. This PR proposes reusing _extract_output_and_other_columns from the default evaluator to handle different format types in the predict return type.

What component(s) does this bug affect?

Code to reproduce issue

inside the BuiltInEvaluator for 2.18, the prediction arg expects a pd.Series but what actually is given is a pd.DataFrame

But in 2.18, for the "regression" model type, I don't think the prediction variable ever hits _extract_output_and_other_columns and so it remains a dataframe.

My proposed implementation would add the argument interactive with the default value of True to mlflow.login() and check its value before proceeding to ask for user input after an unsuccessful attempt to log in via _validate_databricks_auth. If it is set to False, I would raise a relevant exception, rather than continue to ask for user input.

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[HLO Componentization] Populate hlo/testlib sub-component (Phase II). This CL takes care of Migrating external projects dependencies from tensorflow/compiler/xla:test tensorflow/compiler/xla:test_helpers tensorflow/compiler/xla/service:pattern_matcher_gmock to tensorflow/compiler/xla/hlo/testlib:*

[HLO Componentization] Populate hlo/testlib sub-component (Phase II). This CL takes care of Migrating external projects dependencies from tensorflow/compiler/xla:test tensorflow/compiler/xla:test_helpers tensorflow/compiler/xla/service:pattern_matcher_gmock to tensorflow/compiler/xla/hlo/testlib:*

Merged via the queue into mlflow:master with commit b3cd564 Feb 25, 2025 42 checks passed

Merged via the queue into onnx:main with commit c9a1f89 Mar 9, 2025 89 checks passed

Codecov Report All modified and coverable lines are covered by tests ✅ Project coverage is 56.27%. Comparing base (768bc0b) to head (27435bf). Report is 1 commits behind head on main. Additional details and impacted files @@ Coverage Diff @@ ## main #6780 +/- ## ======================================= Coverage 56.27% 56.27% ======================================= Files 509 509 Lines 32580 32580 Branches 3099 3099 ======================================= Hits 18336 18336 Misses 13386 13386 Partials 858 858 ☔ View full report in Codecov by Sentry. 📢 Have feedback on the report? Share it here. 👍 👎 😄 🎉 😕 ❤️ 🚀 👀

Description Use latest ruff Motivation and Context Better tooling.

@mohantym This is my gist https://gist.github.com/MATTYGILO/9f32ab8837a6ef794360ce1e128e7cc9

pkgoogleadded type:featureFeature requestsFeature requests ModelOptimizationToolkitTF Model Optimization ToolkitTF Model Optimization Toolkit and removed type:bugBugBug

I've also done some research into just quantising the spectrogram layer. Spectrogram output without any quantisation, No QAT: Spectrogram output with quantisation, No QAT: QAT spectrogram without quantisation: QAT spectrogram with quantisation As you can see with quantisation and QAT the output are just 0's. mohantymremoved stat:awaiting responseStatus - Awaiting response from authorStatus - Awaiting response from author on Jun 1, 2022mohantym commented on Jun 1, 2022 mohantymon Jun 1, 2022 · edited by mohantymEditsContributor@MATTYGILO ! Actually "Quant " seems not to be working in Colab. Could you please update on this. Attached gist for reference. Thank you!

The TFLite team is constantly improving the framework by fixing bugs and adding new features. We suggest you try the latest TensorFlow version with the latest compatible hardware configuration which could potentially resolve the issue. If you are still facing the issue, please create a new GitHub issue with your latest findings, with all the debugging information which could help us investigate.

sklearn/preprocessing/_target_encoder.py Outdated Comment on lines 345 to 346 def _more_tags(self): return {"requires_y": True}

Just found out that our encoders are not tested via the common test. I added the necessary tags and solve a couple of issues.

@dtcxzyw But do we really want to merge this despite the regression?

So I agree that we just leave it alone

@llvm/pr-subscribers-llvm-transforms Author: Nikita Popov (nikic) Changes This was subsumed by FoldOpIntoSelect() in #116073, and has incorrect FMF propagation on top.

This was subsumed by FoldOpIntoSelect() in #116073, and has incorrect FMF propagation on top.

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Apply comments group 1 … 7f6206b Signed-off-by: Michael Berk <michaelberk99@gmail.com> Apply comments group 1 … 28bfa28

yuki review comments - simplify chat input parse logic … Loading Loading status checks… 17c3cbe

area/models: MLmodel format, model serialization/deserialization, flavors

tests/llama_index/test_llama_index_model_export.py assert type(loaded_model) == type(index) assert loaded_model.as_query_engine().query("Spell llamaindex").response != ""

I'm hoping with the recent additions of to/from_dict(), we'll be able to support serialization of many llama index objects. Path to model definition code (hopefully) won't be necessary for most use cases.

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Let's keep the explicit default instead of inferring it from None, which is easier to maintain.