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Ruslan Kuprieiev
2019-12-03 15:24:20 +02:00
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README.rst
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**Data Version Control** or **DVC** is an **open-source** tool for data science and machine
learning projects. Key features:
#. simple **command line** Git-like experience. Does not require installing and maintaining
any databases. Does not depend on any proprietary online services;
#. Simple **command line** Git-like experience. Does not require installing and maintaining
any databases. Does not depend on any proprietary online services.
#. it manages and versions **datasets** and **machine learning models**. Data is saved in
S3, Google cloud, Azure, Alibaba cloud, SSH server, HDFS or even local HDD RAID;
#. Management and versioning of **datasets** and **machine learning models**. Data is saved in
S3, Google cloud, Azure, Alibaba cloud, SSH server, HDFS, or even local HDD RAID.
#. it makes projects **reproducible** and **shareable**, it helps answering question how
the model was build;
#. Makes projects **reproducible** and **shareable**; helping to answer questions about how
a model was built.
#. it helps manage experiments with Git tags or branches and **metrics** tracking;
#. Helps manage experiments with Git tags/branches and **metrics** tracking.
**DVC** aims to replace tools like Excel and Google Docs that are being commonly used as a knowledge repo and
a ledger for the team, ad-hoc scripts to track and move deploy different model versions, ad-hoc
data file suffixes and prefixes.
**DVC** aims to replace spreadsheet and document sharing tools (such as Excel or Google Docs)
which are being used frequently as both knowledge repositories and team ledgers.
DVC also replaces both ad-hoc scripts to track, move, and deploy different model versions;
as well as ad-hoc data file suffixes and prefixes.
.. contents:: **Contents**
:backlinks: none
@@ -60,34 +61,33 @@ data file suffixes and prefixes.
How DVC works
=============
We encourage you to read our `Get Started <https://dvc.org/doc/get-started>`_ to better understand what DVC
is and how does it fit your scenarios.
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 (or Git-lfs to be precise) + ``makefiles``
The easiest (but not perfect!) *analogy* to describe it: DVC is Git (or Git-LFS to be precise) & Makefiles
made right and tailored specifically for ML and Data Science scenarios.
#. ``Git/Git-lfs`` part - DVC helps you storing and sharing data artifacts, models. It connects them with your
Git repository.
#. ``Makefiles`` part - DVC describes how one data or model artifact was build from another data.
#. ``Git/Git-LFS`` part - DVC helps store and share data artifacts and models, connecting them with a Git repository.
#. ``Makefile``\ s part - DVC describes how one data or model artifact was built from other data and code.
DVC usually runs along with Git. Git is used as usual to store and version code and 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 data files cache DVC supports remotes - any cloud (S3, Azure,
DVC usually runs along with 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 any on-premise network storage (via SSH, for example).
.. image:: https://dvc.org/static/img/flow.gif
:target: https://dvc.org/static/img/flow.gif
:alt: how_dvc_works
DVC pipelines (aka computational graph) feature connects code and data together. In a very explicit way you can
specify, run, and save information that a certain command with certain dependencies needs to be run to produce
a model. See the quick start section below or check `Get Started <https://dvc.org/doc/get-started>`_ tutorial to
learn more.
The DVC pipelines (computational graph) feature connects code and data together. It is possible to explicitly
specify all steps required to produce a model: input dependencies including data, commands to run,
and output information to be saved. See the quick start section below or
the `Get Started <https://dvc.org/doc/get-started>`_ tutorial to learn more.
Quick start
===========
Please read `Get Started <https://dvc.org/doc/get-started>`_ for the full version. Common workflow commands include:
Please read `Get Started <https://dvc.org/doc/get-started>`_ guide for a full version. Common workflow commands include:
+-----------------------------------+-------------------------------------------------------------------+
| Step | Command |
@@ -112,8 +112,8 @@ Please read `Get Started <https://dvc.org/doc/get-started>`_ for the full versio
Installation
============
Read this `instruction <https://dvc.org/doc/get-started/install>`_ to get more details. There are four
options to install DVC: ``pip``, Homebrew, Conda (Anaconda) or an OS-specific package:
There are four options to install DVC: ``pip``, Homebrew, Conda (Anaconda) or an OS-specific package.
Full instructions are `available here <https://dvc.org/doc/get-started/install>`_.
pip (PyPI)
----------
@@ -124,8 +124,8 @@ pip (PyPI)
Depending on the remote storage type you plan to use to keep and share your data, you might need to specify
one of the optional dependencies: ``s3``, ``gs``, ``azure``, ``oss``, ``ssh``. Or ``all`` to include them all.
The command should look like this: ``pip install dvc[s3]`` - it installs the ``boto3`` library along with
DVC to support the AWS S3 storage.
The command should look like this: ``pip install dvc[s3]`` (in this case AWS S3 dependencies such as ``boto3``
will be installed automatically).
To install the development version, run:
@@ -148,7 +148,7 @@ Conda (Anaconda)
conda install -c conda-forge dvc
Currently, it supports only python version 2.7, 3.6 and 3.7.
Currently, this includes support for Python versions 2.7, 3.6 and 3.7.
Snap (Snapcraft)
----------------
@@ -168,8 +168,8 @@ there will be no need to download ``dvc_*.snap`` or use ``--dangerous``
Package
-------
Self-contained packages for Windows, Linux, Mac are available. The latest version of the packages can be found at
GitHub `releases page <https://github.com/iterative/dvc/releases>`_.
Self-contained packages for Linux, Windows, and Mac are available. The latest version of the packages
can be found on the GitHub `releases page <https://github.com/iterative/dvc/releases>`_.
Ubuntu / Debian (deb)
^^^^^^^^^^^^^^^^^^^^^
@@ -187,23 +187,23 @@ Fedora / CentOS (rpm)
sudo yum update
sudo yum install dvc
Related technologies
====================
Comparison to related technologies
==================================
#. `Git-annex <https://git-annex.branchable.com/>`_ - DVC uses the idea of storing the content of large files (that you
don't want to see in your Git repository) in a local key-value store and uses file hardlinks/symlinks instead of the
copying actual files.
#. `Git-annex <https://git-annex.branchable.com/>`_ - DVC uses the idea of storing the content of large files (which should
not be in a Git repository) in a local key-value store, and uses file hardlinks/symlinks instead of
copying/duplicating files.
#. `Git-LFS <https://git-lfs.github.com/>`_ - DVC is compatible with any remote storage (S3, Google Cloud, Azure, SSH,
etc). DVC utilizes reflinks or hardlinks to avoid copy operation on checkouts which makes much more efficient for
large data files.
etc). DVC also uses reflinks or hardlinks to avoid copy operations on checkouts; thus handling large data files
much more efficiently.
#. *Makefile* (and its analogues). DVC tracks dependencies (DAG).
#. *Makefile* (and analogues including ad-hoc scripts) - DVC tracks dependencies (in a directed acyclic graph).
#. `Workflow Management Systems <https://en.wikipedia.org/wiki/Workflow_management_system>`_. DVC is a workflow
#. `Workflow Management Systems <https://en.wikipedia.org/wiki/Workflow_management_system>`_ - DVC is a workflow
management system designed specifically to manage machine learning experiments. DVC is built on top of Git.
#. `DAGsHub <https://dagshub.com/>`_ Is a Github equivalent for DVC - pushing your Git+DVC based repo to DAGsHub will give you a high level dashboard of your project, including DVC pipeline and metrics visualizations, as well as links to DVC managed files if they are in cloud storage.
#. `DAGsHub <https://dagshub.com/>`_ - This is a Github equivalent for DVC. Pushing Git+DVC based repositories to DAGsHub will produce in a high level project dashboard; including DVC pipelines and metrics visualizations, as well as links to any DVC-managed files present in cloud storage.
Contributing
============
@@ -252,5 +252,5 @@ Copyright
This project is distributed under the Apache license version 2.0 (see the LICENSE file in the project root).
By submitting a pull request for this project, you agree to license your contribution under the Apache license version
By submitting a pull request to this project, you agree to license your contribution under the Apache license version
2.0 to this project.