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* Add Studio authentication to DVC Implemented a new feature to authenticate DVC with Iterative Studio. This includes generating an access token, starting device login, checking if user code is authorized, and handling any request exceptions. Furthermore, the commit creates a new authentication command for DVC. This feature was added to allow DVC users to authenticate with Studio, which is necessary for functionality such as sharing live experiments and notifying Studio about pushed experiments. * Add authentication validation and error handling to DVC Studio This commit introduces validation for scopes used in DVC Studio's authentication process. If an unsupported scope is passed, the user will receive an error message listing the invalid scopes and the program will return an error status. Additionally, this commit changes the program's exit behavior when device code authorization fails or authentication process is successful, replacing 'sys.exit' with appropriate returned values. * Add logout functionality to DVC Studio commands This commit introduces the logout command for DVC Studio. It allows users to manually log out from DVC Studio via the command line interface. The logout command only removes the user's token if it exists, otherwise, it will print an error message and return an error status. This adds an extra layer of control for the users and enhances the security for the usage of DVC Studio. * Improve User Authentication process for DVC Studio Implemented a new 'token' command facilitating users to check their authentication token while operating with DVC Studio. This implementation enhances user experience by providing the ability to validate logged-in status. If not logged in, it rightly prompts an error, ensuring secure access. * Add unit tests for auth commands Unit tests are added for the authentication commands used in DVC Studio. The modifications include tests for login with invalid scope, standard login, logout, and token commands. The tests verify successful execution and appropriate responses for different user authentication scenarios, thereby increasing the robustness and reliability of the auth module. * Update unit tests for auth commands Unit tests for DVC Studio authentication commands have been updated. These checks verify successful execution and appropriate responses under various authentication scenarios, including login with invalid scope, standard login, logout, and token commands in order to enhance the robustness and reliability of the auth module. * Add unit tests and modify authentication function in studio utils This commit adds several new unit tests in test_studio.py. Tests for start_device_login and check_token_authorization functionality are included to verify the correct behavior of these methods. Moreover, it also modifies the authentication function in studio.py for improving the robustness. Changes in the function aimed to handle unexpected error scenarios and make the code more resilient. * Refactor mock_response usage in tests and add test_auth.py This commit refactors the way mock responses are used in test_studio.py to follow DRY principles, changing the private _mock_response to a public reusable function. It also adds a new file, test_auth.py, that provides functional tests for authorisation methods, ensuring their functionality. The patches in test_auth.py helps test different cases like expired authorization and successful authorization. Furthermore, test_auth.py also tests if correct configuration is set on successful authorization. Modifications on the method test_auth_login_arguments in test_auth.py were made to refine test cases, thus making the tests more precise and straightforward. * Refactor error handling in `dvc/utils/studio.py` Refactored error handling by removing redundant code blocks and introducing more detailed logging for request exceptions. This makes the failure cases handled explicitly and streamlines the traceability. Also, tests for authorization have been refined and more specific cases like 'expired authorization' have been included. * Replace `auth` module with `studio` in DVC Renamed all instances of `auth` to `studio`, as we are moving the authentication process into a new module `studio`. This includes changes to command names, classes, and test file names to reflect the updated module. It improves the organization of the authentication features related to DVC Studio, separating them from other types of authorization. * Remove device login methods in utils/studio Removed methods related to device login and token authorization in `utils/studio.py` and its corresponding test cases. These functionalities have been moved to the `dvc-studio-client` module to decouple the login process and make the code maintainable. Updated the dependencies in `pyproject.toml` to use the new `dvc-studio-client` module. Also, updated the command/studio to use the `dvc-studio-client` module for auth functionalities. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update dvc-studio-client dependency in pyproject.toml The "dvc-studio-client" dependency in pyproject.toml is updated from a specific git commit to v0.16.0, removing the TODO comment. This change was made because the necessary features from the PR linked in the TODO comment have been merged and released in v0.16.0, making the git commit unnecessary. Now the code is cleaner and easier to track with versioned dependency. * Update DVC Studio components and simplify authentication process This commit modifies several parts of the ‘dvc/commands/studio.py’ and updates DVC Studio Client's version in 'pyproject.toml' for compatibility with the changes. Primarily, the authentication process has been simplified. The unnecessary generation of a random token name is removed while initiating authorization in the run method of CmdStudioLogin class, enhancing the code quality. Changes made in 'dvc/commands/studio.py' are mainly restructuring the code to adapt to the newer version of DVC Studio Client, including changes to the scopes and methods used from this client. The corresponding changes were also reflected in unit tests. The test files 'tests/func/test_studio.py' and 'tests/unit/command/test_studio.py' are updated in accordance with the approach now used in 'dvc/commands/studio.py'. * Remove unnecessary tests and update StudioClient methods Removed functional test file `test_studio.py`from DVC to move it to studio client. The new mock response no longer needs a full testing file, simplifying our test environment. In `dvc/commands/studio.py`, adjusted function imports to align with new StudioClient function names, which better indicate their purpose: `initiate_authorization` changed to `get_access_token`. Additionally, `DEFAULT_SCOPES` was removed from both `dvc/commands/studio.py` and `tests/unit/command/test_studio.py`, simplifying the token acquisition process and removing unnecessary global variable. The `--scopes` command, if needed, can be passed directly without requiring a default fallback. The corresponding changes were reflected in unit tests : the mock functions in `tests/unit/command/test_studio.py` were changed to align with the new authentication method, and updated the usage of the `get_access_token` function. * Update token success message in dvc/commands/studio.py This commit replaces f-string (formatted string literal) with a regular string for the success message after token authentication. The change was necessary as the variable `token_name` is already being passed as a formatted string, thus no need for f-string in the success message sentence. This makes code cleaner and more consistent. * Refactor tests in `test_studio.py` for better readability * Updated terminology in `studio.py` and `test_studio.py` Changes terminology from 'authorize' to 'authenticate' to better align with common practices and improve understanding. Also fixed the `get_access_token` function in 'test_studio.py' where 'AuthorizationExpired' was replaced with 'AuthenticationExpired' for better error handling. * Change 'dvc' to 'DVC' in `studio.py` Updates all instances of 'dvc' to 'DVC' in `studio.py` for enhanced consistency with the rest of the project. This change aims to standardize the usage of the term across all project files. * Improve Studio commands information clarity Updated descriptions in various commands on 'dvc/commands/studio.py' focused on improving clarity and context. Authentication message, command helper, and descriptions have been expanded to provide better understanding. Also, 'dvc' has been changed to 'DVC' in 'studio.py' for consistency with the rest of the project files. * Not relevant anymore * Change argument name in studio login Renamed the argument from "--use-device-code" to "--no-open" in the login function under the Studio authentication. The option's name was changed to make it more intuitive and better align with its actual effect, avoiding potential user confusion. Corresponding changes have also been made in unit tests. * Modify flag for Studio login function Updated the argument name from "-d" to "-o" in `studio.py` for the Studio login function. The change makes the argument's intention clearer for users. This is significant in improving user interaction with the feature and mitigating potential user misinterpretation. * Apply suggestions from code review --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: skshetry <[email protected]>
|Banner|
`Website <https://dvc.org>`_
• `Docs <https://dvc.org/doc>`_
• `Blog <http://blog.dataversioncontrol.com>`_
• `Tutorial <https://dvc.org/doc/get-started>`_
• `Related Technologies <https://dvc.org/doc/user-guide/related-technologies>`_
• `How DVC works`_
• `VS Code Extension`_
• `Installation`_
• `Contributing`_
• `Community and Support`_
|CI| |Python Version| |Coverage| |VS Code| |DOI|
|PyPI| |PyPI Downloads| |Packages| |Brew| |Conda| |Choco| |Snap|
|
**Data Version Control** or **DVC** is a command line tool and `VS Code Extension`_ to help you develop reproducible machine learning projects:
#. **Version** your data and models.
Store them in your cloud storage but keep their version info in your Git repo.
#. **Iterate** fast with lightweight pipelines.
When you make changes, only run the steps impacted by those changes.
#. **Track** experiments in your local Git repo (no servers needed).
#. **Compare** any data, code, parameters, model, or performance plots.
#. **Share** experiments and automatically reproduce anyone's experiment.
Quick start
===========
Please read our `Command Reference <https://dvc.org/doc/command-reference>`_ for a complete list.
A common CLI workflow includes:
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Task | Terminal |
+===================================+====================================================================================================+
| Track data | | ``$ git add train.py params.yaml`` |
| | | ``$ dvc add images/`` |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Connect code and data | | ``$ dvc stage add -n featurize -d images/ -o features/ python featurize.py`` |
| | | ``$ dvc stage add -n train -d features/ -d train.py -o model.p -M metrics.json python train.py`` |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Make changes and experiment | | ``$ dvc exp run -n exp-baseline`` |
| | | ``$ vi train.py`` |
| | | ``$ dvc exp run -n exp-code-change`` |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Compare and select experiments | | ``$ dvc exp show`` |
| | | ``$ dvc exp apply exp-baseline`` |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Share code | | ``$ git add .`` |
| | | ``$ git commit -m 'The baseline model'`` |
| | | ``$ git push`` |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Share data and ML models | | ``$ dvc remote add myremote -d s3://mybucket/image_cnn`` |
| | | ``$ dvc push`` |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
How DVC works
=============
We encourage you to read our `Get Started
<https://dvc.org/doc/get-started>`_ docs to better understand what DVC
does and how it can fit your scenarios.
The closest *analogies* to describe the main DVC features are these:
#. **Git for data**: Store and share data artifacts (like Git-LFS but without a server) and models, connecting them with a Git repository. Data management meets GitOps!
#. **Makefiles** for ML: Describes how data or model artifacts are built from other data and code in a standard format. Now you can version your data pipelines with Git.
#. Local **experiment tracking**: Turn your machine into an ML experiment management platform, and collaborate with others using existing Git hosting (Github, Gitlab, etc.).
Git is employed as usual to store and version code (including DVC meta-files as placeholders for data).
DVC `stores data and model files <https://dvc.org/doc/start/data-management>`_ seamlessly in a cache outside of Git, while preserving almost the same user experience as if they were in the repo.
To share and back up the *data cache*, DVC supports multiple remote storage platforms - any cloud (S3, Azure, Google Cloud, etc.) or on-premise network storage (via SSH, for example).
|Flowchart|
`DVC pipelines <https://dvc.org/doc/start/data-management/data-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.
Last but not least, `DVC Experiment Versioning <https://dvc.org/doc/start/experiments>`_ lets you prepare and run a large number of experiments.
Their results can be filtered and compared based on hyperparameters and metrics, and visualized with multiple plots.
.. _`VS Code Extension`:
VS Code Extension
=================
|VS Code|
To use DVC as a GUI right from your VS Code IDE, install the `DVC Extension <https://marketplace.visualstudio.com/items?itemName=Iterative.dvc>`_ from the Marketplace.
It currently features experiment tracking and data management, and more features (data pipeline support, etc.) are coming soon!
|VS Code Extension Overview|
Note: You'll have to install core DVC on your system separately (as detailed
below). The Extension will guide you if needed.
Installation
============
There are several ways to install DVC: in VS Code; using ``snap``, ``choco``, ``brew``, ``conda``, ``pip``; or with an OS-specific package.
Full instructions are `available here <https://dvc.org/doc/get-started/install>`_.
Snapcraft (Linux)
-----------------
|Snap|
.. code-block:: bash
snap install dvc --classic
This corresponds to the latest tagged release.
Add ``--beta`` for the latest tagged release candidate, or ``--edge`` for the latest ``main`` version.
Chocolatey (Windows)
--------------------
|Choco|
.. code-block:: bash
choco install dvc
Brew (mac OS)
-------------
|Brew|
.. code-block:: bash
brew install dvc
Anaconda (Any platform)
-----------------------
|Conda|
.. code-block:: bash
conda install -c conda-forge mamba # installs much faster than conda
mamba install -c conda-forge dvc
Depending on the remote storage type you plan to use to keep and share your data, you might need to install optional dependencies: `dvc-s3`, `dvc-azure`, `dvc-gdrive`, `dvc-gs`, `dvc-oss`, `dvc-ssh`.
PyPI (Python)
-------------
|PyPI|
.. code-block:: bash
pip install dvc
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]'`` (in this case AWS S3 dependencies such as ``boto3`` will be installed automatically).
To install the development version, run:
.. code-block:: bash
pip install git+git://github.com/iterative/dvc
Package (Platform-specific)
---------------------------
|Packages|
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)
^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
sudo wget https://dvc.org/deb/dvc.list -O /etc/apt/sources.list.d/dvc.list
wget -qO - https://dvc.org/deb/iterative.asc | sudo apt-key add -
sudo apt update
sudo apt install dvc
Fedora / CentOS (rpm)
^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
sudo wget https://dvc.org/rpm/dvc.repo -O /etc/yum.repos.d/dvc.repo
sudo rpm --import https://dvc.org/rpm/iterative.asc
sudo yum update
sudo yum install dvc
Contributing
============
|Maintainability|
Contributions are welcome!
Please see our `Contributing Guide <https://dvc.org/doc/user-guide/contributing/core>`_ for more details.
Thanks to all our contributors!
|Contribs|
Community and Support
=====================
* `Twitter <https://twitter.com/DVCorg>`_
* `Forum <https://discuss.dvc.org/>`_
* `Discord Chat <https://dvc.org/chat>`_
* `Email <mailto:[email protected]>`_
* `Mailing List <https://sweedom.us10.list-manage.com/subscribe/post?u=a08bf93caae4063c4e6a351f6&id=24c0ecc49a>`_
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 to this project, you agree to license your contribution under the Apache license version 2.0 to this project.
Citation
========
|DOI|
Iterative, *DVC: Data Version Control - Git for Data & Models* (2020)
`DOI:10.5281/zenodo.012345 <https://doi.org/10.5281/zenodo.3677553>`_.
Barrak, A., Eghan, E.E. and Adams, B. `On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects <https://mcis.cs.queensu.ca/publications/2021/saner.pdf>`_ , in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2021. Hawaii, USA.
.. |Banner| image:: https://dvc.org/img/logo-github-readme.png
:target: https://dvc.org
:alt: DVC logo
.. |VS Code Extension Overview| image:: https://raw.githubusercontent.com/iterative/vscode-dvc/main/extension/docs/overview.gif
:alt: DVC Extension for VS Code
.. |CI| image:: https://github.com/iterative/dvc/workflows/Tests/badge.svg?branch=main
:target: https://github.com/iterative/dvc/actions
:alt: GHA Tests
.. |Maintainability| image:: https://codeclimate.com/github/iterative/dvc/badges/gpa.svg
:target: https://codeclimate.com/github/iterative/dvc
:alt: Code Climate
.. |Python Version| image:: https://img.shields.io/pypi/pyversions/dvc
:target: https://pypi.org/project/dvc
:alt: Python Version
.. |Coverage| image:: https://codecov.io/gh/iterative/dvc/branch/main/graph/badge.svg
:target: https://codecov.io/gh/iterative/dvc
:alt: Codecov
.. |Snap| image:: https://img.shields.io/badge/snap-install-82BEA0.svg?logo=snapcraft
:target: https://snapcraft.io/dvc
:alt: Snapcraft
.. |Choco| image:: https://img.shields.io/chocolatey/v/dvc?label=choco
:target: https://chocolatey.org/packages/dvc
:alt: Chocolatey
.. |Brew| image:: https://img.shields.io/homebrew/v/dvc?label=brew
:target: https://formulae.brew.sh/formula/dvc
:alt: Homebrew
.. |Conda| image:: https://img.shields.io/conda/v/conda-forge/dvc.svg?label=conda&logo=conda-forge
:target: https://anaconda.org/conda-forge/dvc
:alt: Conda-forge
.. |PyPI| image:: https://img.shields.io/pypi/v/dvc.svg?label=pip&logo=PyPI&logoColor=white
:target: https://pypi.org/project/dvc
:alt: PyPI
.. |PyPI Downloads| image:: https://img.shields.io/pypi/dm/dvc.svg?color=blue&label=Downloads&logo=pypi&logoColor=gold
:target: https://pypi.org/project/dvc
:alt: PyPI Downloads
.. |Packages| image:: https://img.shields.io/badge/deb|pkg|rpm|exe-blue
:target: https://dvc.org/doc/install
:alt: deb|pkg|rpm|exe
.. |DOI| image:: https://img.shields.io/badge/DOI-10.5281/zenodo.3677553-blue.svg
:target: https://doi.org/10.5281/zenodo.3677553
:alt: DOI
.. |Flowchart| image:: https://dvc.org/img/flow.gif
:target: https://dvc.org/img/flow.gif
:alt: how_dvc_works
.. |Contribs| image:: https://contrib.rocks/image?repo=iterative/dvc
:target: https://github.com/iterative/dvc/graphs/contributors
:alt: Contributors
.. |VS Code| image:: https://img.shields.io/visual-studio-marketplace/v/Iterative.dvc?color=blue&label=VSCode&logo=visualstudiocode&logoColor=blue
:target: https://marketplace.visualstudio.com/items?itemName=Iterative.dvc
:alt: VS Code Extension