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Clemens Wolff
6882a781a4
This change adds a new cloud remote based on Azure Blob Storage [1] and the azure-storage-blob SDK [2]. The implementation closely follows the patterns laid out by the existing cloud remotes (AWS and GCP). Notable differences include: - No use of a lock in the progress-bar callback and no requirement to look up total transfer size ahead of time: the Azure Storage SDK provides the total transfer bytes and current transfer offset in its progress callback. - When `exists` is called, check the existence of every blob instead of listing the entire container. Container list operations can be expensive so unless the number of checked files is very large, making separate requests is likely going to be more efficient. - For increased flexibility, access credentials for the Azure Storage backend can be provided either connection-string style via the remote URL or via environment variables. The Azure Storage backend can be emulated via Azurite [3]. [1] https://azure.microsoft.com/en-us/services/storage/blobs/ [2] https://github.com/Azure/azure-storage-python [3] https://github.com/Azure/azurite This change was created together with @EricSchles.
.. image:: https://img.shields.io/travis/iterative/dvc/master.svg?label=Linux%20%26%20Mac%20OS :target: https://travis-ci.org/iterative/dvc .. image:: https://img.shields.io/appveyor/ci/iterative/dvc/master.svg?label=Windows :target: https://ci.appveyor.com/project/iterative/dvc/branch/master .. image:: https://codeclimate.com/github/iterative/dvc/badges/gpa.svg :target: https://codeclimate.com/github/iterative/dvc .. image:: https://codecov.io/gh/iterative/dvc/branch/master/graph/badge.svg :target: https://codecov.io/gh/iterative/dvc Data Version Control or DVC is an open source tool for data science projects. It helps data scientists manage their code and data together in a simple form of Git-like commands. Get started =========== +-----------------------------------+-------------------------------------------------------------------+ | Step | Command | +===================================+===================================================================+ | Track code and data together | | ``$ git add train.py`` | | | | ``$ dvc add images.zip`` | +-----------------------------------+-------------------------------------------------------------------+ | Connect code and data by commands | | ``$ dvc run -d images.zip -o images/ unzip -q images.zip`` | | | | ``$ dvc run -d images/ -d train.py -o model.p python train.py`` | +-----------------------------------+-------------------------------------------------------------------+ | Make changes and reproduce | | ``$ vi train.py`` | | | | ``$ dvc repro model.p.dvc`` | +-----------------------------------+-------------------------------------------------------------------+ | Share code | | ``$ git add .`` | | | | ``$ git commit -m 'The baseline model'`` | | | | ``$ git push`` | +-----------------------------------+-------------------------------------------------------------------+ | Share data and ML models | | ``$ dvc remote add myremote s3://mybucket/image_cnn`` | | | | ``$ dvc core.remote myremote`` | | | | ``$ dvc push`` | +-----------------------------------+-------------------------------------------------------------------+ See more in `tutorial <https://dvc.org/doc/tutorial>`_. Installation ============ Packages -------- Operating system dependent packages are the recommended way to install DVC. The latest version of the packages can be found at GitHub releases page: https://github.com/iterative/dvc/releases Python Pip ---------- DVC could be installed via the Python Package Index (PyPI). .. code-block:: bash pip install dvc Homebrew (Mac OS) ----------------- Formula: ^^^^^^^^ .. code-block:: bash brew install iterative/homebrew-dvc/dvc Cask: ^^^^^ .. code-block:: bash brew cask install iterative/homebrew-dvc/dvc Links ===== Website: https://dvc.org Tutorial: https://dvc.org/doc/tutorial Documentation: http://dvc.org/doc Discussion: https://discuss.dvc.org/ 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-LFS <https://git-lfs.github.com/>`_. #. Makefile (and it's analogues). DVC tracks dependencies (DAG). #. `Workflow Management Systems <https://en.wikipedia.org/wiki/Workflow_management_system>`_. DVC is workflow management system designed specificaly to manage machine learning experiments. DVC was built on top of Git. DVC is compatible with Git for storing code and the dependency graph (DAG), but not data files cache. Data files caches can be transferred separately - now data cache transfer throught AWS S3, Azure Blob Storage and GCP storge are supported. How DVC works ============= .. image:: https://dvc.org/static/img/flow.png :target: https://dvc.org/static/img/flow.png :alt: how_dvc_works 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 2.0 to this project.