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readme: move How it works after Quick started sections

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Jorge Orpinel Perez
2022-07-20 07:16:26 +05:45
committed by skshetry
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README.rst
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@@ -32,28 +32,6 @@
.. contents:: **Contents**
:backlinks: none
How DVC works
=============
We encourage you to read our `Get Started <https://dvc.org/doc/get-started>`_ guide to better understand what DVC
is and how it can fit your scenarios.
The easiest (but not perfect!) *analogy* to describe it: DVC is Git for data & Makefiles tailored specifically for ML and Data Science scenarios.
#. **Git** part - DVC helps store and share data artifacts (like Git-LFS but without a server) and models, connecting them with a Git repository.
#. **Make** part - DVC describes how data or model artifacts are built from other data and code -- a data pipeline.
DVC usually works on top of Git.
Git is used as usual to store and version code (including DVC meta-files).
DVC helps to store data and model files seamlessly out of Git, while preserving almost the same user experience as if they were stored in Git itself.
To store and share the *data cache*, DVC supports multiple remotes - any cloud (S3, Azure, Google Cloud, etc.) or on-premise network storage (via SSH, for example).
|Flowchart|
DVC pipelines (computational graphs) connect code and data together.
They specify all steps required to produce a model: input dependencies including code, data, commands to run; and output information to be saved.
Quick start
===========
@@ -79,6 +57,29 @@ Please read `Get Started <https://dvc.org/doc/get-started>`_ guide for a full ve
| | | ``$ dvc push`` |
+-----------------------------------+----------------------------------------------------------------------------+
How DVC works
=============
We encourage you to read our `Get Started
<https://dvc.org/doc/get-started>`_ guides to better understand what DVC
does and how it can fit your scenarios.
The easiest (but not perfect!) *analogy* to describe it: DVC is Git for data & Makefiles tailored specifically for ML and Data Science scenarios.
#. **Git** part - DVC helps store and share data artifacts (like Git-LFS but without a server) and models, connecting them with a Git repository.
#. **Make** part - DVC describes how data or model artifacts are built from other data and code -- a data pipeline.
DVC usually works on top of Git.
Git is used as usual to store and version code (including DVC meta-files).
DVC helps to store data and model files seamlessly out of Git, while preserving almost the same user experience as if they were stored in Git itself.
To store and share the *data cache*, DVC supports multiple remotes - any cloud (S3, Azure, Google Cloud, etc.) or on-premise network storage (via SSH, for example).
|Flowchart|
DVC pipelines (computational graphs) connect code and data together.
They specify all steps required to produce a model: input dependencies including code, data, commands to run; and output information to be saved.
Visual Studio Code Extension
============================