Data Version Control
open-source version control system for ML projects
DVC is a new type of experiment management software that has been built on top of the existing engineering toolset particularly on a source code version control system (currently Git). DVC reduces the gap between existing tools and data science needs, allowing users to take advantage of experiment management software while reusing existing skills and intuition.
Key features:
[0] simple command line Git-like experience. It does not require installing and maintaining any databases. It does not depend on any proprietary online services
[1] management and versioning of datasets and ML models. Data is saved in S3, Google Cloud, Azure, Alibaba cloud, SSH server, HDFS, or even local HDD RAID
[2] makes projects reproducible and shareable; helping to answer questions about how a model was built
[3] helps manage experiments with Git tags/branches and metrics tracking
The main commands :feelsgoodmeme:
webpage: https://dvc.org
docs: https://dvc.org/doc
github: https://github.com/iterative/dvc
:ods: channel: #tool_dvc
#dvc #version #control #ml #projects #system #git
open-source version control system for ML projects
DVC is a new type of experiment management software that has been built on top of the existing engineering toolset particularly on a source code version control system (currently Git). DVC reduces the gap between existing tools and data science needs, allowing users to take advantage of experiment management software while reusing existing skills and intuition.
Key features:
[0] simple command line Git-like experience. It does not require installing and maintaining any databases. It does not depend on any proprietary online services
[1] management and versioning of datasets and ML models. Data is saved in S3, Google Cloud, Azure, Alibaba cloud, SSH server, HDFS, or even local HDD RAID
[2] makes projects reproducible and shareable; helping to answer questions about how a model was built
[3] helps manage experiments with Git tags/branches and metrics tracking
The main commands :feelsgoodmeme:
$ dvc add <name_file>$ dvc run <name_file>$ dvc [push/pull]webpage: https://dvc.org
docs: https://dvc.org/doc
github: https://github.com/iterative/dvc
:ods: channel: #tool_dvc
#dvc #version #control #ml #projects #system #git
