Here, we cover the main concepts in AIR.
Ray Data <data> is the standard way to load and exchange data in Ray AIR. It provides a Dataset <dataset_concept> concept which is used extensively for data loading, preprocessing, and batch inference.
Preprocessors are primitives that can be used to transform input data into features. Preprocessors operate on Datasets <dataset_concept>, which makes them scalable and compatible with a variety of datasources and dataframe libraries.
A Preprocessor is 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.
See the documentation on Preprocessors <air-preprocessor-ref>.
doc_code/air_key_concepts.py
Trainers are wrapper classes around third-party training frameworks such as XGBoost and Pytorch. They are built to help integrate with core Ray actors (for distribution), Ray Tune, and Ray Data.
See the documentation on Trainers <air-trainers>.
doc_code/air_key_concepts.py
Trainer objects produce a Result <air-results-ref> object after calling .fit(). These objects contain training metrics as well as checkpoints to retrieve the best model.
doc_code/air_key_concepts.py
Tuners <air-tuner-ref> offer scalable hyperparameter tuning as part of Ray Tune <tune-main>.
Tuners can work seamlessly with any Trainer but also can support arbitrary training functions.
doc_code/air_key_concepts.py
The AIR trainers, tuners, and custom pretrained model generate a framework-specific Checkpoint<ray.air.Checkpoint> object. Checkpoints are a common interface for models that are used across different AIR components and libraries.
There are two main ways to generate a checkpoint.
Checkpoint objects can be retrieved from the Result object returned by a Trainer or Tuner .fit() call.
doc_code/air_key_concepts.py
You can also generate a checkpoint from a pretrained model. Each AIR supported machine learning (ML) framework has a Checkpoint object that can be used to generate an AIR checkpoint:
doc_code/air_key_concepts.py
Checkpoints can be used to instantiate a Predictor, BatchPredictor, or PredictorDeployment classes, as seen below.
You can take a checkpoint and do batch inference using the BatchPredictor object.
doc_code/air_key_concepts.py
Deploy the model as an inference service by using Ray Serve and the PredictorDeployment class.
doc_code/air_key_concepts.py
After deploying the service, you can send requests to it.
doc_code/air_key_concepts.py