- 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. -
-- Full code and data provenance help track the complete evolution of every - ML model. This guarantees reproducibility and makes it easy to switch - back and forth between experiments. -
-- Harness the full power of Git branches to try different ideas instead of - sloppy file suffixes and comments in code. Use automatic metric-tracking - to navigate instead of paper and pencil. -
-- DVC was designed to keep branching as simple and fast as in Git — no - matter the data file size. Along with first-class citizen metrics and ML - pipelines, it means that a project has cleaner structure. It's easy - to compare ideas and pick the best. Iterations become faster with - intermediate artifact caching. -
-- Instead of ad-hoc scripts, use push/pull commands to move consistent - bundles of ML models, data, and code into production, remote machines, - or a colleague's computer. -
-- DVC introduces lightweight pipelines as a first-class citizen mechanism - in Git. They are language-agnostic and connect multiple steps into a - DAG. These pipelines are used to remove friction from getting code into - production. -
-+ + ▶️ + {' '} + It can be run online: +
++ + 🐛 + {' '} + Found an issue? Let us know! Or fix it: +
+ + + + Edit on GitHub + ++ + ❓ + {' '} + Have a question? Join our chat, we will help you: +
+ + + + Discord Chat + ++ 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. +
++ Full code and data provenance help track the complete evolution of every + ML model. This guarantees reproducibility and makes it easy to switch + back and forth between experiments. +
++ Harness the full power of Git branches to try different ideas instead of + sloppy file suffixes and comments in code. Use automatic metric-tracking + to navigate instead of paper and pencil. +
++ DVC was designed to keep branching as simple and fast as in Git — no + matter the data file size. Along with first-class citizen metrics and ML + pipelines, it means that a project has cleaner structure. It's easy + to compare ideas and pick the best. Iterations become faster with + intermediate artifact caching. +
++ Instead of ad-hoc scripts, use push/pull commands to move consistent + bundles of ML models, data, and code into production, remote machines, + or a colleague's computer. +
++ DVC introduces lightweight pipelines as a first-class citizen mechanism + in Git. They are language-agnostic and connect multiple steps into a + DAG. These pipelines are used to remove friction from getting code into + production. +
++ 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. +
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