Metadata-Version: 2.1
Name: dvclive
Version: 0.4.2
Summary: Metric logger for ML projects.
Home-page: https://dvc.org/doc/dvclive
Author: Paweł Redzyński
Author-email: pawel@iterative.ai
License: UNKNOWN
Download-URL: https://github.com/iterative/dvclive
Description: DVCLive
        =======
        
        • `Docs <https://dvc.org/doc/dvclive>`_
        
        |CI| |Coverage| |Donate|
        
        |PyPI|
        
        **DVCLive** is an **open-source** library for monitoring the progress of metrics during training of machine learning models. It's built with Git and MLOps principles in mind:
        
        1. **Codification of data**. Tracked metrics are stored in readable text files that can be versioned by Git or other version control tools.
        2. **Distributed**. No services or servers are required. Metrics are stored in a Git repository as text files, or pointers to files in `DVC <https://dvc.org>`_ storage.
        3. **GitOps API**. Plots are generated through `DVC <https://dvc.org>`_ using Git commit SHAs or branch names, e.g.: :code:`dvc plots diff --target logs master`.
        
        .. image:: https://dvc.org/static/cdc4ec4dabed1d7de6b8606667ebfc83/9da93/dvclive-diff-html.png
        
        4. **Automation**. DVCLive metrics are easy to use by any automation, DevOps, or MLOps tool such as CI/CD (including `CML <https://cml.dev>`_), custom scripts, or ML platforms.
        
        **DVCLive** integrates seamlessly with `DVC <https://dvc.org>`_; the logs/summaries it produces can be fed as :code:`dvc plots`/:code:`dvc metrics`. 
        
        However, `DVC <https://dvc.org>`_ is *not required* to work with dvclive logs/summaries, and since they're saved as easily parsable :code:`.tsv`/:code:`.json` files, you can use your preferred visualization method.
        
        .. contents:: **Contents**
          :backlinks: none
        
        Quick Start
        ===========
        
        Please read the `Get Started <https://dvc.org/doc/dvclive/get-started>`_ for a detailed version.
        
        **DVCLive** is a Python library. The interface consists of three main steps:
        
        1. Initialize DVCLive
        
        .. code-block:: python
        
          from dvclive import Live
        
          live = Live()
        
        
        2. Log metrics
        
        
        .. code-block:: python
        
          live.log("metric", 1)
        
        3. Increase the step number
        
        .. code-block:: python
        
          live.next_step()
         
        
        If you are ussing a ML training framework, check the existing `ML Frameworks <https://dvc.org/doc/dvclive/user-guide/ml-frameworks>`_ page.
        
        Installation
        ============
        
        pip (PyPI)
        ----------
        
        |PyPI|
        
        .. code-block:: bash
        
           pip install dvclive
        
        Depending on the *ML framework* you plan to use to train your model, you might need to specify
        one of the optional dependencies: ``mmcv``, ``tf``, ``xgb``. Or ``all`` to include them all.
        The command should look like this: ``pip install dvclive[tf]`` (in this case TensorFlow and it's dependencies
        will be installed automatically).
        
        To install the development version, run:
        
        .. code-block:: bash
        
           pip install git+git://github.com/iterative/dvclive
        
        Comparison to related technologies
        ==================================
        
        **DVCLive** is an *ML Logger*, similar to:
        
        - `MLFlow <https://mlflow.org/>`_
        - `Weights & Biases <https://wandb.ai/site>`_
        - `Neptune <https://neptune.ai/>`_ 
        
        The main difference with those *ML Loggers* is that **DVCLive** does not require any additional services or servers to run. 
        
        Logged metrics are stored as plain text files that can be versioned by version control tools (i.e Git) or tracked as pointers to files in DVC storage. 
        
        Call to collaboration
        =====================
        
        Today only Python is supported (while DVC is language agnostic), along with the following *ML frameworks*:
        
        - `Catalyst <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/catalyst>`_
        - `Fast.ai <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/fastai>`_
        - `Hugging Face <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/huggingface>`_
        - `Keras <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/keras>`_
        - `LightGBM <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/lightgbm>`_
        - `MMCV <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/mmcv>`_
        - `PyTorch <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/pytorch>`_
        - `PyTorch Lightning <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/pytorch-lightning>`_
        - `Tensorflow <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/tensorflow>`_
        - `XGBoost <https://dvc.org/doc/dvclive/user-guide/ml-frameworks/xgboost>`_ 
        
        The DVCLive team is happy to extend the functionality as needed. Please `create an issue <https://github.com/iterative/dvclive/issues>`_ or check the `existing ones <https://github.com/iterative/dvclive/issues?q=is%3Aissue+is%3Aopen+label%3Aintegrations>`_ to start a discussion!
        
        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.
        
        .. |CI| image:: https://github.com/iterative/dvclive/workflows/tests/badge.svg
           :target: https://github.com/iterative/dvclive/actions
           :alt: GHA Tests
        
        .. |Coverage| image:: https://codecov.io/gh/iterative/dvclive/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/iterative/dvclive
           :alt: Codecov
        
        .. |Donate| image:: https://img.shields.io/badge/patreon-donate-green.svg?logo=patreon
           :target: https://www.patreon.com/DVCorg/overview
           :alt: Donate
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/dvclive.svg?label=pip&logo=PyPI&logoColor=white
           :target: https://pypi.org/project/dvclive
           :alt: PyPI
        
Keywords: data-science metrics machine-learning developer-tools ai
Platform: UNKNOWN
Requires-Python: >=3.6
Provides-Extra: tests
Provides-Extra: all
Provides-Extra: tf
Provides-Extra: xgb
Provides-Extra: lgbm
Provides-Extra: huggingface
Provides-Extra: catalyst
Provides-Extra: fastai
Provides-Extra: pytorch_lightning
