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The original link doesn't exist. https://docs.ray.io/en/master/_images/air-ecosystem.svg I fixed it by linking the raw github file link. This should have the exactly same flow as before. I tried finding a link to this image file, but I couldn't. I also couldn't find an easy way to add only a link (without embedding an image). Please lmk if you prefer other option
207 lines
6.9 KiB
ReStructuredText
207 lines
6.9 KiB
ReStructuredText
.. _air:
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Ray AI Runtime (AIR)
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====================
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.. tip::
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AIR is currently in **beta**. Fill out `this short form <https://forms.gle/wCCdbaQDtgErYycT6>`__ to get involved. We'll be holding office hours, development sprints, and other activities as we get closer to the GA release. Join us!
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Ray AI Runtime (AIR) is a scalable and unified toolkit for ML applications. AIR enables easy scaling of individual workloads, end-to-end workflows, and popular ecosystem frameworks, all in just Python.
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..
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https://docs.google.com/drawings/d/1atB1dLjZIi8ibJ2-CoHdd3Zzyl_hDRWyK2CJAVBBLdU/edit
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.. image:: images/ray-air.svg
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AIR comes with ready-to-use libraries for :ref:`Preprocessing <datasets>`, :ref:`Training <train-docs>`, :ref:`Tuning <tune-main>`, :ref:`Scoring <air-predictors>`, :ref:`Serving <rayserve>`, and :ref:`Reinforcement Learning <rllib-index>`, as well as an ecosystem of integrations.
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Why AIR?
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--------
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Ray AIR aims to simplify the ecosystem of machine learning frameworks, platforms, and tools. It does this by leveraging Ray to provide a seamless, unified, and open experience for scalable ML:
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.. image:: images/why-air-2.svg
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..
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https://docs.google.com/drawings/d/1oi_JwNHXVgtR_9iTdbecquesUd4hOk0dWgHaTaFj6gk/edit
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**1. Seamless Dev to Prod**: AIR reduces friction going from development to production. With Ray and AIR, the same Python code scales seamlessly from a laptop to a large cluster.
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**2. Unified ML API**: AIR's unified ML API enables swapping between popular frameworks, such as XGBoost, PyTorch, and HuggingFace, with just a single class change in your code.
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**3. Open and Extensible**: AIR and Ray are fully open-source and can run on any cluster, cloud, or Kubernetes. Build custom components and integrations on top of scalable developer APIs.
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When to use AIR?
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----------------
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AIR is for both data scientists and ML engineers alike.
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.. image:: images/when-air.svg
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..
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https://docs.google.com/drawings/d/1Qw_h457v921jWQkx63tmKAsOsJ-qemhwhCZvhkxWrWo/edit
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For data scientists, AIR can be used to scale individual workloads, and also end-to-end ML applications. For ML Engineers, AIR provides scalable platform abstractions that can be used to easily onboard and integrate tooling from the broader ML ecosystem.
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Quick Start
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-----------
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Below, we walk through how AIR's unified ML API enables scaling of end-to-end ML workflows, focusing on
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a few of the popular frameworks AIR integrates with (XGBoost, Pytorch, and Tensorflow). The ML workflow we're going to build is summarized by the following diagram:
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..
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https://docs.google.com/drawings/d/1z0r_Yc7-0NAPVsP2jWUkLV2jHVHdcJHdt9uN1GDANSY/edit
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.. figure:: images/why-air.svg
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AIR provides a unified API for the ML ecosystem.
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This diagram shows how AIR enables an ecosystem of libraries to be run at scale in just a few lines of code.
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Get started by installing Ray AIR:
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.. code:: bash
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pip install -U "ray[air]"
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# The below Ray AIR tutorial was written with the following libraries.
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# Consider running the following to ensure that the code below runs properly:
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pip install -U pandas>=1.3.5
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pip install -U torch>=1.12
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pip install -U numpy>=1.19.5
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pip install -U tensorflow>=2.6.2
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pip install -U pyarrow>=6.0.1
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Preprocessing
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~~~~~~~~~~~~~
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First, let's start by loading a dataset from storage:
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.. literalinclude:: examples/xgboost_starter.py
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:language: python
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:start-after: __air_generic_preprocess_start__
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:end-before: __air_generic_preprocess_end__
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Then, we define a ``Preprocessor`` pipeline for our task:
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.. tabbed:: XGBoost
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.. literalinclude:: examples/xgboost_starter.py
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:language: python
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:start-after: __air_xgb_preprocess_start__
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:end-before: __air_xgb_preprocess_end__
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.. tabbed:: Pytorch
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.. literalinclude:: examples/pytorch_tabular_starter.py
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:language: python
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:start-after: __air_pytorch_preprocess_start__
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:end-before: __air_pytorch_preprocess_end__
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.. tabbed:: Tensorflow
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.. literalinclude:: examples/tf_tabular_starter.py
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:language: python
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:start-after: __air_tf_preprocess_start__
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:end-before: __air_tf_preprocess_end__
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Training
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~~~~~~~~
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Train a model with a ``Trainer`` with common ML frameworks:
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.. tabbed:: XGBoost
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.. literalinclude:: examples/xgboost_starter.py
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:language: python
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:start-after: __air_xgb_train_start__
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:end-before: __air_xgb_train_end__
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.. tabbed:: Pytorch
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.. literalinclude:: examples/pytorch_tabular_starter.py
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:language: python
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:start-after: __air_pytorch_train_start__
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:end-before: __air_pytorch_train_end__
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.. tabbed:: Tensorflow
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.. literalinclude:: examples/tf_tabular_starter.py
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:language: python
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:start-after: __air_tf_train_start__
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:end-before: __air_tf_train_end__
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Hyperparameter Tuning
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~~~~~~~~~~~~~~~~~~~~~
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You can specify a hyperparameter space to search over for each trainer:
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.. tabbed:: XGBoost
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.. literalinclude:: examples/xgboost_starter.py
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:language: python
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:start-after: __air_xgb_tuner_start__
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:end-before: __air_xgb_tuner_end__
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.. tabbed:: Pytorch
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.. literalinclude:: examples/pytorch_tabular_starter.py
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:language: python
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:start-after: __air_pytorch_tuner_start__
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:end-before: __air_pytorch_tuner_end__
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.. tabbed:: Tensorflow
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.. literalinclude:: examples/tf_tabular_starter.py
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:language: python
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:start-after: __air_tf_tuner_start__
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:end-before: __air_tf_tuner_end__
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Then use the ``Tuner`` to run the search:
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.. literalinclude:: examples/pytorch_tabular_starter.py
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:language: python
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:start-after: __air_tune_generic_start__
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:end-before: __air_tune_generic_end__
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Batch Inference
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~~~~~~~~~~~~~~~
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Use the trained model for scalable batch prediction with a ``BatchPredictor``.
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.. tabbed:: XGBoost
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.. literalinclude:: examples/xgboost_starter.py
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:language: python
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:start-after: __air_xgb_batchpred_start__
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:end-before: __air_xgb_batchpred_end__
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.. tabbed:: Pytorch
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.. literalinclude:: examples/pytorch_tabular_starter.py
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:language: python
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:start-after: __air_pytorch_batchpred_start__
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:end-before: __air_pytorch_batchpred_end__
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.. tabbed:: Tensorflow
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.. literalinclude:: examples/tf_tabular_starter.py
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:language: python
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:start-after: __air_tf_batchpred_start__
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:end-before: __air_tf_batchpred_end__
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Project Status
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--------------
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AIR is currently in **beta**. If you have questions for the team or are interested in getting involved in the development process, fill out `this short form <https://forms.gle/wCCdbaQDtgErYycT6>`__.
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For an overview of the AIR libraries, ecosystem integrations, and their readiness, check out the latest :ref:`AIR ecosystem map <air-ecosystem-map>`.
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Next Steps
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----------
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- :ref:`air-key-concepts`
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- `Examples <https://github.com/ray-project/ray/tree/master/python/ray/air/examples>`__
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- :ref:`Deployment Guide <air-deployment>`
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- :ref:`API reference <air-api-ref>`
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