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115 lines
3.6 KiB
ReStructuredText
115 lines
3.6 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:`Result <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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.. _air-session-key-concepts:
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Session
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-------
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Ray AIR exposes a functional API for users to define training behavior, or for developers to create their own ``Trainer``\s.
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In both cases, there is a need for the following interactions:
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1. To disseminate information downstream, including ``trial_name``, ``trial_id``, ``trial_resources``, rank information etc.
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2. To report information to upstream, including metrics and checkpoint.
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To facilitate such interactions, we introduce the :ref:`Session <air-session-ref>` concept.
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The session concept exists on several levels: The execution layer (called `Tune Session`) and the Data Parallel training layer
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(called `Train Session`).
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The following figure shows how these two sessions look like in a Data Parallel training scenario.
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.. image:: images/session.svg
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:width: 650px
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:align: center
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..
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https://docs.google.com/drawings/d/1g0pv8gqgG29aPEPTcd4BC0LaRNbW1sAkv3H6W1TCp0c/edit
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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 ``PredictorDeployment`` 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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