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docs/source/ads.automl.rst

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ads.automl package
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==================
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Submodules
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----------
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ads.automl.driver module
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------------------------
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.. automodule:: ads.automl.driver
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:members:
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ads.automl.provider module
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--------------------------
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Module contents
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---------------
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.. automodule:: ads.automl
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docs/source/ads.explanations.rst

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ads.explanations package
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========================
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ads.explanations.base\_explainer module
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ads.explanations.explainer module
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ads.explanations.mlx\_global\_explainer module
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ads.explanations.mlx\_interface module
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ads.explanations.mlx\_local\_explainer module
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ads.explanations.mlx\_whatif\_explainer module
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docs/source/ads.model.extractor.rst

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ads.model.extractor.automl\_extractor module
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ads.model.extractor.keras\_extractor module
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docs/source/ads.model.framework.rst

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ads.model.framework.automl\_model module
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ads.model.framework.huggingface\_model module
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docs/source/ads.model.rst

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ads.model.datascience\_model module
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ads.model.generic\_model module
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docs/source/ads.rst

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.. toctree::
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ads.automl
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ads.catalog
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ads.common

docs/source/release_notes.rst

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Release Notes
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=============
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2.8.2
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Release date: March 2, 2023
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* Remove support for Python 3.7.
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* Improved the DataScienceMode.create() to support timeout argument and auto extract region from the signer and signer config.
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* Support Jupyter Notebook as ``entrypoint`` when defining Data Science jobs with ``PythonRuntime`` and ``GitPythonRuntime``.
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* Support environment variable substitution in Data Science job names and output URI.
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* Support JSON serialization of list/dictionary when assigning them as Data Science jobs environment variables.
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* Support saving the notebook to output URI even if the job run failed when running a Data Science job using ``NotebookRuntime``.
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* Added ``job.build()`` method to Data Science job to load default values from environment.
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* Added ``DataScienceJob.fast_launch_shapes()`` method to list fast launch shapes available for Data Science job.
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* Added :doc:`HuggingFacePipelineModel <./user_guide/model_registration/frameworks/huggingfacemodel>` class to support prepare, save, deploy and predict for HuggingFace pipelines.
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* Updated Data Science job run YAML representation to include configurations inherited from the job.
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* Fixed custom conda environment not showing in Data Science Job YAML specification.
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* Fixed an issue where model saving was failing in notebook session without ipywidgets installed.
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* Fixed "Unknown archive format" error in ads.jobs.PythonRuntime, when the source code folder name ends with "zip". List of supported archive files are: "zip", "tar.gz", "tar" and "tgz".
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Release date: February 16, 2023

docs/source/user_guide/loading_data/connect_legacy.rst

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Train a Models with ADB
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After you load your data from ADB, the ``ADSDataset`` object is created, which allows you to build models using AutoML.
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from ads.automl.provider import OracleAutoMLProvider
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model, baseline = AutoML(train, provider= OracleAutoMLProvider()).train(model_list=["LGBMClassifier"])
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docs/source/user_guide/model_catalog/model_catalog.rst

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* The ``ADSModel`` approach prepares an artifact from the ``ADSModel`` object, and saves it to the model catalog using the ``.prepare()`` method. ``ADSModel`` objects are typically created from the AutoML engine. Data scientists can also convert models trained with other machine learning libraries into an ``ADSModel`` object (using the ``.from_estimator()`` method).
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docs/source/user_guide/model_deployment/predict.rst

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The change needed in `score.py`:

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