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DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. Version control machine learning models, data sets and intermediate files. DVC connects them with code and uses Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store file contents. Version control machine learning models, data sets,...

Tracking a Xcode project in a version control system is annoying. Simply adding files can lead to merge conflicts that have to be solved manually, although it would be possible to resolve the conflict automatically if the merge algorithm would be aware of the structure of Xcode's project file. After long and annoying merge sessions in one of my projects, I came to the conclusion, that writing a merge driver for git which understands the structure of the project file would be a worthwhile...

The DVS Deployable Versioning System is for tracking the development, QA and installation of deployables. It is intended to be used in in-house project environments and uses a CVS style command line interface.