From 1750760f99c7ec75601f0a6d1577f3125c4a0920 Mon Sep 17 00:00:00 2001 From: Eric Liang Date: Fri, 5 Aug 2022 18:42:45 -0700 Subject: [PATCH] [docs] Improve the "Why Ray" and "Why AIR" sections of the docs (#27480) --- README.rst | 12 ++- doc/source/data/dataset-tensor-support.rst | 2 +- doc/source/index.md | 10 +- .../examples/pytorch_tabular_starter.py | 13 +-- .../ray-air/examples/tf_tabular_starter.py | 15 +-- .../ray-air/examples/xgboost_starter.py | 15 +-- doc/source/ray-air/getting-started.rst | 98 ++++++++++-------- doc/source/ray-air/images/ray-air.svg | 2 +- doc/source/ray-air/images/when-air.svg | 1 + doc/source/ray-air/images/why-air-2.svg | 1 + doc/source/ray-air/images/why-air.png | Bin 179552 -> 0 bytes doc/source/ray-air/images/why-air.svg | 1 + 12 files changed, 95 insertions(+), 75 deletions(-) create mode 100644 doc/source/ray-air/images/when-air.svg create mode 100644 doc/source/ray-air/images/why-air-2.svg delete mode 100644 doc/source/ray-air/images/why-air.png create mode 100644 doc/source/ray-air/images/why-air.svg diff --git a/README.rst b/README.rst index 404a5afcb7fd..2cb00050a254 100644 --- a/README.rst +++ b/README.rst @@ -35,7 +35,7 @@ Or more about `Ray Core`_ and its key abstractions: - `Actors`_: Stateful worker processes created in the cluster. - `Objects`_: Immutable values accessible across the cluster. -Ray runs on any machine, cluster, cloud provider, and Kubernetes, and also features a growing +Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing `ecosystem of community integrations`_. Install Ray with: ``pip install ray``. For nightly wheels, see the @@ -49,6 +49,16 @@ Install Ray with: ``pip install ray``. For nightly wheels, see the .. _`RLlib`: https://docs.ray.io/en/latest/rllib/index.html .. _`ecosystem of community integrations`: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html + +Why Ray? +-------- + +Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands. + +Ray is a unified way to scale Python and AI applications from a laptop to a cluster. + +With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required. + More Information ---------------- diff --git a/doc/source/data/dataset-tensor-support.rst b/doc/source/data/dataset-tensor-support.rst index 6330a72c6212..be9f14f08138 100644 --- a/doc/source/data/dataset-tensor-support.rst +++ b/doc/source/data/dataset-tensor-support.rst @@ -194,7 +194,7 @@ Because Tensor datasets rely on Datasets-specific extension types, they can only .. _disable_tensor_extension_casting: Disabling Tensor Extension Casting -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +---------------------------------- To disable automatic casting of Pandas and Arrow arrays to :class:`TensorArray `, run the code diff --git a/doc/source/index.md b/doc/source/index.md index 8b6d6e33c65e..acb644a1724f 100644 --- a/doc/source/index.md +++ b/doc/source/index.md @@ -103,9 +103,17 @@ Or more about [Ray Core](ray-core/walkthrough) and its key abstractions: - [Actors](ray-core/actors): Stateful worker processes created in the cluster. - [Objects](ray-core/objects): Immutable values accessible across the cluster. -Ray runs on any machine, cluster, cloud provider, and Kubernetes, and also features a growing +Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing [ecosystem of community integrations](ray-overview/ray-libraries). +## Why Ray? + +Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands. + +Ray is a unified way to scale Python and AI applications from a laptop to a cluster. + +With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required. + ## How to get involved? Ray is more than a framework for distributed applications but also an active community of developers, researchers, and folks that love machine learning. diff --git a/doc/source/ray-air/examples/pytorch_tabular_starter.py b/doc/source/ray-air/examples/pytorch_tabular_starter.py index d141958dc17f..392b85060035 100644 --- a/doc/source/ray-air/examples/pytorch_tabular_starter.py +++ b/doc/source/ray-air/examples/pytorch_tabular_starter.py @@ -3,7 +3,6 @@ # __air_generic_preprocess_start__ import ray -from ray.data.preprocessors import StandardScaler # Load data. dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv") @@ -15,21 +14,16 @@ test_dataset = valid_dataset.map_batches( lambda df: df.drop("target", axis=1), batch_format="pandas" ) - -# Create a preprocessor to scale some columns -columns_to_scale = ["mean radius", "mean texture"] -preprocessor = StandardScaler(columns=columns_to_scale) # __air_generic_preprocess_end__ # __air_pytorch_preprocess_start__ import numpy as np -import pandas as pd -from ray.data.preprocessors import Concatenator, Chain +from ray.data.preprocessors import Concatenator, Chain, StandardScaler -# Chain the preprocessors together. +# Create a preprocessor to scale some columns and concatenate the result. preprocessor = Chain( - preprocessor, + StandardScaler(columns=["mean radius", "mean texture"]), Concatenator(exclude=["target"], dtype=np.float32), ) # __air_pytorch_preprocess_end__ @@ -161,4 +155,5 @@ def to_tensor_iterator(dataset, batch_size): predicted_probabilities.show() # {'predictions': array([1.], dtype=float32)} # {'predictions': array([0.], dtype=float32)} +# ... # __air_pytorch_batchpred_end__ diff --git a/doc/source/ray-air/examples/tf_tabular_starter.py b/doc/source/ray-air/examples/tf_tabular_starter.py index abb492b2005c..bf88e003976a 100644 --- a/doc/source/ray-air/examples/tf_tabular_starter.py +++ b/doc/source/ray-air/examples/tf_tabular_starter.py @@ -3,10 +3,8 @@ # __air_generic_preprocess_start__ import ray -from ray.data.preprocessors import StandardScaler -from ray.air.config import ScalingConfig - +# Load data. dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv") # Split data into train and validation. @@ -16,21 +14,16 @@ test_dataset = valid_dataset.map_batches( lambda df: df.drop("target", axis=1), batch_format="pandas" ) - -# Create a preprocessor to scale some columns -columns_to_scale = ["mean radius", "mean texture"] -preprocessor = StandardScaler(columns=columns_to_scale) # __air_generic_preprocess_end__ # __air_tf_preprocess_start__ import numpy as np -import pandas as pd -from ray.data.preprocessors import Concatenator, Chain +from ray.data.preprocessors import Concatenator, Chain, StandardScaler -# Chain the preprocessors together. +# Create a preprocessor to scale some columns and concatenate the result. preprocessor = Chain( - preprocessor, + StandardScaler(columns=["mean radius", "mean texture"]), Concatenator(exclude=["target"], dtype=np.float32), ) # __air_tf_preprocess_end__ diff --git a/doc/source/ray-air/examples/xgboost_starter.py b/doc/source/ray-air/examples/xgboost_starter.py index d8c4adae798c..f14272bbe075 100644 --- a/doc/source/ray-air/examples/xgboost_starter.py +++ b/doc/source/ray-air/examples/xgboost_starter.py @@ -3,8 +3,6 @@ # __air_generic_preprocess_start__ import ray -from ray.data.preprocessors import StandardScaler -from ray.air.config import ScalingConfig # Load data. dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv") @@ -16,16 +14,18 @@ test_dataset = valid_dataset.map_batches( lambda df: df.drop("target", axis=1), batch_format="pandas" ) - -# Create a preprocessor to scale some columns -columns_to_scale = ["mean radius", "mean texture"] -preprocessor = StandardScaler(columns=columns_to_scale) # __air_generic_preprocess_end__ +# __air_xgb_preprocess_start__ +# Create a preprocessor to scale some columns. +from ray.data.preprocessors import StandardScaler + +preprocessor = StandardScaler(columns=["mean radius", "mean texture"]) +# __air_xgb_preprocess_end__ # __air_xgb_train_start__ -from ray.train.xgboost import XGBoostTrainer from ray.air.config import ScalingConfig +from ray.train.xgboost import XGBoostTrainer trainer = XGBoostTrainer( scaling_config=ScalingConfig( @@ -84,4 +84,5 @@ # {'predictions': 0.9970690608024597} # {'predictions': 0.9943051934242249} # {'predictions': 0.00334902573376894} +# ... # __air_xgb_batchpred_end__ diff --git a/doc/source/ray-air/getting-started.rst b/doc/source/ray-air/getting-started.rst index 7b67ed582ba0..435fb7c5a580 100644 --- a/doc/source/ray-air/getting-started.rst +++ b/doc/source/ray-air/getting-started.rst @@ -9,13 +9,54 @@ Ray AI Runtime (AIR) 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. +.. + https://docs.google.com/drawings/d/1atB1dLjZIi8ibJ2-CoHdd3Zzyl_hDRWyK2CJAVBBLdU/edit + .. image:: images/ray-air.svg AIR comes with ready-to-use libraries for :ref:`Preprocessing `, :ref:`Training `, :ref:`Tuning `, :ref:`Scoring `, :ref:`Serving `, and :ref:`Reinforcement Learning `, as well as an ecosystem of integrations. -Ray AIR focuses on the compute aspects of ML: - * It provides scalability by leveraging Ray’s distributed compute layer for ML workloads. - * It is designed to interoperate with other systems for storage and metadata needs. +Why AIR? +-------- + +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: + +.. image:: images/why-air-2.svg + +.. + https://docs.google.com/drawings/d/1oi_JwNHXVgtR_9iTdbecquesUd4hOk0dWgHaTaFj6gk/edit + +**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. + +**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. + +**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. + +When to use AIR? +---------------- + +AIR is for both data scientists and ML engineers alike. + +.. image:: images/when-air.svg + +.. + https://docs.google.com/drawings/d/1Qw_h457v921jWQkx63tmKAsOsJ-qemhwhCZvhkxWrWo/edit + +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. + +Quick Start +----------- + +Below, we walk through how AIR's unified ML API enables scaling of end-to-end ML workflows, focusing on +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: + +.. + https://docs.google.com/drawings/d/1z0r_Yc7-0NAPVsP2jWUkLV2jHVHdcJHdt9uN1GDANSY/edit + +.. figure:: images/why-air.svg + + AIR provides a unified API for the ML ecosystem. + This diagram shows how AIR enables an ecosystem of libraries to be run at scale in just a few lines of code. Get started by installing Ray AIR: @@ -31,30 +72,24 @@ Get started by installing Ray AIR: pip install -U tensorflow>=2.6.2 pip install -U pyarrow>=6.0.1 -Quick Start ------------ - -Below, we demonstrate how AIR enables simple scaling of end-to-end ML workflows, focusing on -a few of the popular frameworks AIR integrates with (XGBoost, Pytorch, and Tensorflow): - Preprocessing ~~~~~~~~~~~~~ -Below, let's start by preprocessing your data with Ray AIR's ``Preprocessors``: +First, let's start by loading a dataset from storage: .. literalinclude:: examples/xgboost_starter.py :language: python :start-after: __air_generic_preprocess_start__ :end-before: __air_generic_preprocess_end__ -If using Tensorflow or Pytorch, format your data for use with your training framework: +Then, we define a ``Preprocessor`` pipeline for our task: .. tabbed:: XGBoost - .. code-block:: python - - # No extra preprocessing is required for XGBoost. - # The data is already in the correct format. + .. literalinclude:: examples/xgboost_starter.py + :language: python + :start-after: __air_xgb_preprocess_start__ + :end-before: __air_xgb_preprocess_end__ .. tabbed:: Pytorch @@ -155,38 +190,13 @@ Use the trained model for scalable batch prediction with a ``BatchPredictor``. :start-after: __air_tf_batchpred_start__ :end-before: __air_tf_batchpred_end__ -Why Ray AIR? ------------- -Ray AIR aims to simplify the ecosystem of machine learning frameworks, platforms, and tools. It does this by taking a scalable, single-system approach to ML infrastructure (i.e., leveraging Ray as a unified compute framework): +Project Status +-------------- -**1. Seamless Dev to Prod**: AIR reduces friction going from development to production. Traditional orchestration approaches introduce separate systems and operational overheads. With Ray and AIR, the same Python code scales seamlessly from a laptop to a large cluster. - -**2. Unified API**: Want to switch between frameworks like XGBoost and PyTorch, or try out a new library like HuggingFace? Thanks to the flexibility of AIR, you can do this by just swapping out a single class, without needing to set up new systems or change other aspects of your workflow. - -**3. Open and Evolvable**: Ray core and libraries are fully open-source and can run on any cluster, cloud, or Kubernetes, reducing the costs of platform lock-in. Want to go out of the box? Run any framework you want using AIR's integration APIs, or build advanced use cases directly on Ray core. - -.. figure:: images/why-air.png - - AIR enables a single-system / single-script approach to scaling ML. Ray's - distributed Python APIs enable scaling of ML workloads without the burden of - setting up or orchestrating separate distributed systems. - -AIR is for both data scientists and ML engineers. Consider using AIR when you want to: - * Scale a single workload. - * Scale end-to-end ML applications. - * Build a custom ML platform for your organization. - -AIR Ecosystem -------------- - -AIR comes with built-in integrations with the most popular ecosystem libraries. The following diagram provides an overview of the AIR libraries, ecosystem integrations, and their readiness. -AIR's developer APIs also enable *custom integrations* to be easily created. - -.. - https://docs.google.com/drawings/d/1pZkRrkAbRD8jM-xlGlAaVo3T66oBQ_HpsCzomMT7OIc/edit +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 `__. -.. image:: images/air-ecosystem.svg +For an overview of the AIR libraries, ecosystem integrations, and their readiness, check out the latest `AIR ecosystem map `_. 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