89 lines
3.5 KiB
ReStructuredText
89 lines
3.5 KiB
ReStructuredText
:orphan:
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.. _what:
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###################
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What is Lightning?
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###################
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Lightning is a free, modular, distributed, and open-source framework for building
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AI applications where the components you want to use interact together.
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Lightning apps can be built for **any AI use case**, ranging from AI research to
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production-ready pipelines (and everything in between!).
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By abstracting the engineering boilerplate, Lightning allows researchers, data scientists, and software engineers to
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build highly-scalable, production-ready AI apps using the tools and technologies of their choice,
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regardless of their level of engineering expertise.
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.. figure:: https://pl-public-data.s3.amazonaws.com/assets_lightning/Lightning.gif
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:alt: What is Lightning gif.
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:width: 100 %
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----
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.. _why:
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***************
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Why Lightning?
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***************
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Easy to learn
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^^^^^^^^^^^^^
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Lightning was built for creating AI apps, not for dev-ops. It offers an intuitive, pythonic
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and highly composable interface that allows you to focus on solving the problems that are important to you.
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----
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Quick to deliver
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^^^^^^^^^^^^^^^^
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Lightning speeds the development process by offering testable templates you can build from,
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accelerating the process of moving from idea to prototype and finally to market.
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----
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Easy to scale
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^^^^^^^^^^^^^
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Lightning provides a mirrored experience locally and in the cloud. The `lightning.ai <https://lightning.ai>`_.
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cloud platform abstracts the infrastructure, so you can run your apps at any scale.
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----
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Easy to collaborate
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^^^^^^^^^^^^^^^^^^^
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Lightning was built for collaboration.
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By following the best MLOps practices provided through our documentation and example use cases,
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you can deploy state-of-the-art ML applications that are ready to be used by teams of all sizes.
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----
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*****************************
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What's Novel With Lightning?
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*****************************
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Cloud Infra Made Simple and Pythonic
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Lightning is for building reactive, scalable, cost effective, easy-to-maintain and reliable ML products in the cloud without worrying about infrastructure. Lightning provides several engineering novelties to enable this:
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#. **Reactivity**: Lightning allows you to run stateful components distributed across different machines, so you can design async, dynamic and reactive workflows in python, without having to define DAGs.
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#. **Scalable & Cost-Effective**: Lightning provides a granular and simple way to run components preemptively or on-demand and on any desired resource such as CPU or GPU. It also enables you to easily transfer artifacts from one machine to another.
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#. **Reliability**:
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#. **Checkpointing**: Lightning apps can be paused and resumed from generated state and artifact-based checkpoints.
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#. **Resilience**: Lightning has a strong fault-tolerance foundation. Your application can be written and tested to be resilient for cloud hazards at the component level.
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#. **Testing Tools**: Lightning provides you with tools and best practices you can use to develop and test your application. All of our built-in templates have unit integration and end-to-end tests.
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#. **Easy to maintain**:
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#. **Easy Debugging**: Lightning apps can be debugged locally and in the cloud with **breakpoints** in any components.
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#. **Non-Invasive**: Lightning is the glue that connects all parts of your workflow, but this is done in a non-invasive way by formalizing API contracts between components. In other words, your application can run someone else's code with little assumption.
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