mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
4629a3a649
Co-authored-by: xwjiang2010 <xwjiang2010@gmail.com> Co-authored-by: Kai Fricke <kai@anyscale.com> Co-authored-by: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com> Co-authored-by: Eric Liang <ekhliang@gmail.com>
121 lines
4.8 KiB
ReStructuredText
121 lines
4.8 KiB
ReStructuredText
.. _train-key-concepts:
|
|
|
|
Key Concepts
|
|
============
|
|
|
|
There are four main concepts in the Ray Train library.
|
|
|
|
1. ``Trainers`` execute distributed training.
|
|
2. ``Configuration`` objects are used to configure training.
|
|
3. ``Checkpoints`` are returned as the result of training.
|
|
4. ``Predictors`` can be used for inference and batch prediction.
|
|
|
|
.. https://docs.google.com/drawings/d/1FezcdrXJuxLZzo6Rjz1CHyJzseH8nPFZp6IUepdn3N4/edit
|
|
|
|
.. image:: images/train-specific.svg
|
|
|
|
Trainers
|
|
--------
|
|
|
|
Trainers are responsible for executing (distributed) training runs.
|
|
The output of a Trainer run is a :ref:`Result <train-key-concepts-results>` that contains
|
|
metrics from the training run and the latest saved :ref:`Checkpoint <air-checkpoint-ref>`.
|
|
Trainers can also be configured with :ref:`Datasets <air-ingest>` and :ref:`Preprocessors <air-preprocessors>` for scalable data ingest and preprocessing.
|
|
|
|
There are three categories of built-in Trainers:
|
|
|
|
.. tabbed:: Deep Learning Trainers
|
|
|
|
Ray Train supports the following deep learning trainers:
|
|
|
|
- :class:`TorchTrainer <ray.train.torch.TorchTrainer>`
|
|
- :class:`TensorflowTrainer <ray.train.tensorflow.TensorflowTrainer>`
|
|
- :class:`HorovodTrainer <ray.train.horovod.HorovodTrainer>`
|
|
|
|
For these trainers, you usually define your own training function that loads the model
|
|
and executes single-worker training steps. Refer to the following guides for more details:
|
|
|
|
- :ref:`Deep learning user guide <train-dl-guide>`
|
|
- :ref:`Quick overview of deep-learning trainers in the Ray AIR documentation <air-trainers-dl>`
|
|
|
|
.. tabbed:: Tree-Based Trainers
|
|
|
|
Tree-based trainers utilize gradient-based decision trees for training. The most popular libraries
|
|
for this are XGBoost and LightGBM.
|
|
|
|
- :class:`XGBoostTrainer <ray.train.xgboost.XGBoostTrainer>`
|
|
- :class:`LightGBMTrainer <ray.train.lightgbm.LightGBMTrainer>`
|
|
|
|
For these trainers, you just pass a dataset and parameters. The training loop is configured
|
|
automatically.
|
|
|
|
- :ref:`XGBoost/LightGBM user guide <train-gbdt-guide>`
|
|
- :ref:`Quick overview of tree-based trainers in the Ray AIR documentation <air-trainers-tree>`
|
|
|
|
|
|
.. tabbed:: Other Trainers
|
|
|
|
Some trainers don't fit into the other two categories, such as:
|
|
|
|
- :class:`HuggingFaceTrainer <ray.train.huggingface.HuggingFaceTrainer>` for NLP
|
|
- :class:`RLTrainer <ray.train.rl.RLTrainer>` for reinforcement learning
|
|
- :class:`SklearnTrainer <ray.train.sklearn.sklearn_trainer.SklearnTrainer>` for (non-distributed) training of sklearn models.
|
|
|
|
- :ref:`Other trainers in the Ray AIR documentation <air-trainers-other>`
|
|
|
|
.. _train-key-concepts-config:
|
|
|
|
Configuration
|
|
-------------
|
|
|
|
Trainers are configured with configuration objects. There are two main configuration classes,
|
|
the :class:`ScalingConfig <ray.air.config.ScalingConfig>` and the :class:`RunConfig <ray.air.config.RunConfig>`.
|
|
The latter contains subconfigurations, such as the :class:`FailureConfig <ray.air.config.FailureConfig>`,
|
|
:class:`SyncConfig <ray.tune.syncer.SyncConfig>` and :class:`CheckpointConfig <ray.air.config.CheckpointConfig>`.
|
|
|
|
Check out the :ref:`Configurations User Guide <train-config>` for an in-depth guide on using these configurations.
|
|
|
|
.. _train-key-concepts-results:
|
|
|
|
Checkpoints
|
|
-----------
|
|
|
|
Calling ``Trainer.fit()`` returns a :class:`Result <ray.air.result.Result>` object, which includes
|
|
information about the run such as the reported metrics and the saved checkpoints.
|
|
|
|
Checkpoints have the following purposes:
|
|
|
|
* They can be passed to a Trainer to resume training from the given model state.
|
|
* They can be used to create a Predictor / BatchPredictor for scalable batch prediction.
|
|
* They can be deployed with Ray Serve.
|
|
|
|
.. _train-key-concepts-predictors:
|
|
|
|
Predictors
|
|
----------
|
|
|
|
Predictors are the counterpart to Trainers. A Trainer trains a model on a dataset, and a predictor
|
|
uses the resulting model and performs inference on it.
|
|
|
|
Each Trainer has a respective Predictor implementation that is compatible with its generated checkpoints.
|
|
|
|
.. dropdown:: Example: :class:`XGBoostPredictor <ray.train.xgboost.XGBoostPredictor>`
|
|
|
|
.. literalinclude:: /train/doc_code/xgboost_train_predict.py
|
|
:language: python
|
|
:start-after: __train_predict_start__
|
|
:end-before: __train_predict_end__
|
|
|
|
|
|
A predictor can be passed into a :class:`BatchPredictor <ray.train.batch_predictor.BatchPredictor>`
|
|
is used to scale up prediction over a Ray cluster.
|
|
It takes a Ray Dataset as input.
|
|
|
|
.. dropdown:: Example: Batch prediction with :class:`XGBoostPredictor <ray.train.xgboost.XGBoostPredictor>`
|
|
|
|
.. literalinclude:: /train/doc_code/xgboost_train_predict.py
|
|
:language: python
|
|
:start-after: __batch_predict_start__
|
|
:end-before: __batch_predict_end__
|
|
|
|
See :ref:`the Predictors user guide <air-predictors>` for more information and examples.
|