Files
FLAML/website
Li Jiang 1c9835dc0a Add support to Python 3.12, Sync Fabric till dc382961 (#1467)
* Merged PR 1686010: Bump version to 2.3.5.post2, Distribute source and wheel, Fix license-file, Only log better models

- Fix license-file
- Bump version to 2.3.5.post2
- Distribute source and wheel
- Log better models only
- Add artifact_path to register_automl_pipeline
- Improve logging of _automl_user_configurations

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This pull request fixes the project’s configuration by updating the license metadata for compliance with FLAML OSS 2.3.5.

The changes in `/pyproject.toml` update the project’s license and readme metadata by replacing deprecated keys with the new structured fields.
- `/pyproject.toml`: Replaced `license_file` with `license = { text = "MIT" }`.
- `/pyproject.toml`: Replaced `description-file` with `readme = "README.md"`.
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Related work items: #4252053

* Merged PR 1688479: Handle feature_importances_ is None, Catch RuntimeError and wait for spark cluster to recover

- Add warning message when feature_importances_ is None (#3982120)
- Catch RuntimeError and wait for spark cluster to recover (#3982133)

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Bug fix.

This pull request prevents an AttributeError in the feature importance plotting function by adding a check for a `None` value with an informative warning message.
- `flaml/fabric/visualization.py`: Checks if `result.feature_importances_` is `None`, logs a warning with possible reasons, and returns early.
- `flaml/fabric/visualization.py`: Imports `logger` from `flaml.automl.logger` to support the warning message.
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Related work items: #3982120, #3982133

* Removed deprecated metadata section

* Fix log_params, log_artifact doesn't support run_id in mlflow 2.6.0

* Remove autogen

* Remove autogen

* Remove autogen

* Merged PR 1776547: Fix flaky test test_automl

Don't throw error when time budget is not enough

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#### AI description  (iteration 1)
#### PR Classification
Bug fix addressing a failing test in the AutoML notebook example.

#### PR Summary
This PR fixes a flaky test by adding a conditional check in the AutoML test that prints a message and exits early if no best estimator is set, thereby preventing unpredictable test failures.
- `test/automl/test_notebook_example.py`: Introduced a check to print "Training budget is not sufficient" and return if `automl.best_estimator` is not found.
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Related work items: #4573514

* Merged PR 1777952: Fix unrecognized or malformed field 'license-file' when uploading wheel to feed

Try to fix InvalidDistribution: Invalid distribution metadata: unrecognized or malformed field 'license-file'

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Bug fix addressing package metadata configuration.

This pull request fixes the error with unrecognized or malformed license file fields during wheel uploads by updating the setup configuration.
- In `setup.py`, added `license="MIT"` and `license_files=["LICENSE"]` to provide proper license metadata.
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Related work items: #4560034

* Cherry-pick Merged PR 1879296: Add support to python 3.12 and spark 4.0

* Cherry-pick Merged PR 1890869: Improve time_budget estimation for mlflow logging

* Cherry-pick Merged PR 1879296: Add support to python 3.12 and spark 4.0

* Disable openai workflow

* Add python 3.12 to test envs

* Manually trigger openai

* Support markdown files with underscore-prefixed file names

* Improve save dependencies

* SynapseML is not installed

* Fix syntax error:Module !flaml/autogen was never imported

* macos 3.12 also hangs

* fix syntax error

* Update python version in actions

* Install setuptools for using pkg_resources

* Fix test_automl_performance in Github actions

* Fix test_nested_run
2026-01-10 12:17:21 +08:00
..
2023-09-23 16:55:30 +00:00
2025-07-09 18:33:10 +08:00
2021-12-16 17:11:33 -08:00
2021-12-16 17:11:33 -08:00

Website

This website is built using Docusaurus 2, a modern static website generator.

Prerequisites

To build and test documentation locally, begin by downloading and installing Node.js, and then installing Yarn. On Windows, you can install via the npm package manager (npm) which comes bundled with Node.js:

npm install --global yarn

Installation

pip install pydoc-markdown
cd website
yarn install

Local Development

Navigate to the website folder and run:

pydoc-markdown
yarn start

This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.