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91 lines
2.7 KiB
ReStructuredText
91 lines
2.7 KiB
ReStructuredText
.. _air-key-concepts:
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Key Concepts
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============
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Here, we cover the main concepts in AIR.
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.. contents::
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:local:
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Preprocessors
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-------------
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Preprocessors are primitives that can be used to transform input data into features.
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A preprocessor can be fitted during Training, and applied at runtime in both Training and Serving on data batches in the same way. AIR comes with a collection of built-in preprocessors, and you can also define your own with simple templates.
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Preprocessors operate on :ref:`Ray Datasets <datasets>`, which makes them scalable and compatible with a variety of datasources and dataframe libraries.
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.. literalinclude:: doc_code/air_key_concepts.py
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:language: python
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:start-after: __air_preprocessors_start__
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:end-before: __air_preprocessors_end__
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Trainers
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--------
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Trainers are wrapper classes around third-party training frameworks like XGBoost and Pytorch. They are built to help integrate with core Ray actors (for distribution), Ray Tune, and Ray Datasets.
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See the documentation on :ref:`Trainers <air-trainer-ref>`.
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.. literalinclude:: doc_code/air_key_concepts.py
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:language: python
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:start-after: __air_trainer_start__
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:end-before: __air_trainer_end__
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Trainer objects will produce a :ref:`Results <air-results-ref>` object after calling ``.fit()``. These objects will contain training metrics as long as checkpoints to retrieve the best model.
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.. literalinclude:: doc_code/air_key_concepts.py
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:language: python
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:start-after: __air_trainer_output_start__
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:end-before: __air_trainer_output_end__
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Tuner
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-----
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:ref:`Tuners <air-tuner-ref>` offer scalable hyperparameter tuning as part of :ref:`Ray Tune <tune-main>`.
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Tuners can work seamlessly with any Trainer but also can support arbitrary training functions.
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.. literalinclude:: doc_code/air_key_concepts.py
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:language: python
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:start-after: __air_tuner_start__
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:end-before: __air_tuner_end__
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Batch Predictor
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---------------
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You can take a trained model and do batch inference using the BatchPredictor object.
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.. literalinclude:: doc_code/air_key_concepts.py
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:language: python
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:start-after: __air_batch_predictor_start__
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:end-before: __air_batch_predictor_end__
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.. _air-key-concepts-online-inference:
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Online Inference
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----------------
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Deploy the model as an inference service by using Ray Serve and the ``ModelWrapperDeployment`` class.
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.. literalinclude:: doc_code/air_key_concepts.py
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:language: python
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:start-after: __air_deploy_start__
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:end-before: __air_deploy_end__
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After deploying the service, you can send requests to it.
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.. literalinclude:: doc_code/air_key_concepts.py
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:language: python
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:start-after: __air_inference_start__
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:end-before: __air_inference_end__
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