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term: review usage of "control"
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content/docs/command-reference/fetch.md

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@@ -35,7 +35,7 @@ Fetching is performed automatically by `dvc pull` (when the data is not already
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in the <abbr>cache</abbr>), along with `dvc checkout`:
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```
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content/docs/command-reference/init.md

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@@ -116,11 +116,11 @@ In rare cases, the `--no-scm` option might be desirable: to initialize DVC in a
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directory that is not part of a Git repo, or to make DVC ignore Git. Examples
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include:
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- Version control other than Git is being used. Even though there are DVC
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features that require DVC to be run in the Git repo, DVC can work well with
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other version control systems. Since DVC relies on simple `dvc.yaml` files to
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manage <abbr>pipelines</abbr>, data, etc, they can be added into any version
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control system, thus providing large data files and directories versioning.
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- SCM other than Git is being used. Even though there are DVC features that
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require DVC to be run in the Git repo, DVC can work well with other version
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control systems. Since DVC relies on simple `dvc.yaml` files to manage
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<abbr>pipelines</abbr>, data, etc, they can be added into any version control
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system, thus providing large data files and directories versioning.
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- There is no need to keep the history at all, e.g. having a deployment
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automation like running a data pipeline using `cron`.

content/docs/command-reference/plots/index.md

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This type of metrics files are created by users, or generated by user data
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processing code, and get defined with the `-p` (`--plots`) and
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`--plots-no-cache`) options of `dvc run`. `dvc plots` subcommands can work with
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plots files committed to a Git repo history, data files controlled by DVC, or
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any other file in system.
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plots files committed to a Git repo history, data files tracked by DVC, or any
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other file in system.
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DVC generates plots as HTML files that can be open with a web browser. These
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HTML files use [Vega-Lite](https://vega.github.io/vega-lite/). Vega is a

content/docs/command-reference/pull.md

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It has the same effect as running `dvc fetch` and `dvc checkout`:
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```
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content/docs/command-reference/run.md

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- `-O <path>`, `--outs-no-cache <path>` - the same as `-o` except that outputs
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are not tracked by DVC. It means that they are not cached, and it's up to a
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user to save and version control them. This is useful if the outputs are small
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enough to be tracked by Git directly, or if these files are not of future
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interest.
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user to manage them separately. This is useful if the outputs are small enough
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to be tracked by Git directly, or if these files are not of future interest.
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- `--outs-persist <path>` - declare output file or directory that will not be
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removed upon `dvc repro`.
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- `-M <path>`, `--metrics-no-cache <path>` - the same as `-m` except that DVC
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does not track the metrics file. This means that the file is not cached, so
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it's up to the user to save and version control it. This is typically
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desirable with _metrics_ because they are small enough to be tracked with Git
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directly. See also the difference between `-o` and `-O`.
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it's up to the user to manage them separately. This is typically desirable
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with _metrics_ because they are small enough to be tracked with Git directly.
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See also the difference between `-o` and `-O`.
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- `--plots <path>` - specify a plot metrics file produces by this stage. This
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option behaves like `-o` but registers the file in a `plots` field inside the
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- `--plots-no-cache <path>` - the same as `--plots` except that DVC does not
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track the plots metrics file. This means that the file is not cached, so it's
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up to the user to save and version control it. See also the difference between
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`-o` and `-O`.
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up to the user to manage them separately. See also the difference between `-o`
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and `-O`.
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- `-w <path>`, `--wdir <path>` - specifies a working directory for the `command`
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to run in (uses the `wdir` field in `dvc.yaml`). Dependency and output files

content/docs/use-cases/index.md

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If you store and process data files or datasets to produce other data or machine
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learning models, and you want to
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- capture and save <abbr>data artifacts</abbr> the same way you capture code;
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- track, control, and switch between different versions of data or models
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easily;
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- track and save <abbr>data artifacts</abbr> the same way you capture code;
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- create and switch among different versions of data or models easily;
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- understand how data or ML models were built in the first place;
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- compare machine learning models and metrics to each other;
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- bring software engineering best practices and tools to your data science team

content/docs/user-guide/basic-concepts/dvc-cache.md

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---
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The DVC cache is a hidden storage (by default located in the `.dvc/cache`
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directory) for files that are under DVC control, and their different versions.
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For more details, please refer to this
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directory) for files that are tracked by DVC, and their different versions. For
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more details, please refer to this
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[document](/doc/user-guide/dvc-files-and-directories#structure-of-the-cache-directory).

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