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DVC Experiments Overview

ML experiments can be saved with DVC automatically as they're run or manually after they complete. Each experiment creates and tracks a variation of your data science project based on the changes in your workspace.

Experiments preserve a connection to the latest commit in the current branch (Git HEAD) as their parent or baseline, but do not form part of the regular Git tree. This prevents bloating your repo with temporary commits and branches.

Experiments are custom Git references (found in .git/refs/exps) with one or more commits based on HEAD. These commits are hidden and not checked out by DVC. Note that these are not pushed to Git remotes by default either (see dvc exp push).

Note that DVC Experiments require a unique name to identify them. DVC will auto-generate one by default, such as puffy-daks. A custom name can be set instead, using the --name/-n option of dvc exp run/dvc exp save. These names can be used to reference experiments in other dvc exp subcommands.

Save experiments

See ๐Ÿ‘จโ€๐Ÿ’ป Get Started: Experiments for a hands-on introduction to DVC experiments.

To save an experiment, you can follow one of these roads:

  • If you do not have a DVC pipeline, you can log live results from Python code using DVCLive initialized.
  • If you have a DVC pipeline, use dvc exp run to both run your code pipeline and save experiment results. dvc exp run also enables advanced features like queuing many experiments at once.

Experiments are saved locally by default but you can share them so that anyone can reproduce your work.

Metrics, plots, parameters

DVC can track and compare parameters, metrics, and plots data saved in standard structured files like YAML, JSON, and CSV, and they can be tracked as part of your repo. One way to generate these parameters, metrics, and plots (and to automatically configure them) is with DVCLive. You can also manually generate these files and use dvc.yaml metafiles to specify which files are parameters, metrics, or plots (and to specify how to visualize plots).

Models and datasets

DVC can track models or datasets as part of your repo, and you can manage those models in the model registry. One way to log models or other artifacts is with DVCLive. You can also track them with dvc add and declare metadata for the model registry in dvc.yaml.

Work with DVC Experiments from a GUI

DVC Experiments can be used directly from the VS Code IDE or online with DVC Studio, the web UI that integrates all of our data science tools.

DVC Studio

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VS Code Extension

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