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# Manage DVC-Tracked Files | ||
|
||
> 💡 Read more about managing large files with Git and DVC | ||
> [here](https://dvc.org/doc/start/data-management). DVC provides a few commands | ||
> you can run along with `git` to version large files, directories, or ML model | ||
> using external storages like S3, SSH, Google Cloud, etc. | ||
In the [DVC Tracked View](command:dvc.views.trackedExplorerTree.focus) you can | ||
see the files and directories that are managed by DVC (even if they are not yet | ||
downloaded) in a view similar to the regular `Explorer` view | ||
|
||
<p align="center"> | ||
<img src="images/dvc-tracked-explorer.png" | ||
alt="DVC-Tracked Explorer Integration" /> | ||
</p> | ||
|
||
You can `dvc pull` to download your data or `dvc push` to save it right from | ||
this view. | ||
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||
In the [Source Control View](command:workbench.view.scm) the statuses shown are | ||
designed to integrate with that of the built-in Git extension and give an "at a | ||
glance" state of the entire workspace. The extension will show if DVC-tracked | ||
datasets are changed and you need to _commit_ them. | ||
|
||
<p align="center"> | ||
<img src="images/dvc-tracked-scm.png" alt="DVC-Tracked SCM Integration" /> | ||
</p> | ||
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||
From this view you can trigger data management commands like `dvc checkout`, | ||
`dvc commit`, as well as `dvc pull` and `dvc push`. |
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# Experiments Table | ||
|
||
The extension's equivalent of `dvc exp show`. | ||
> ❗ DVC should be installed and DVC project available in the workspace for this | ||
> and the next steps to work properly. Please refer to first two steps of this | ||
> guide. | ||
Use `DVC: Show Experiments` from the Command Palette to open up the experiments | ||
table for a `DVC` repository. | ||
As you change code, parameters, data and run new experiments this table will | ||
keep all the details about your iterations. It can be manipulated and configured | ||
using the [DVC side panel](command:workbench.view.extension.dvc-views). This is | ||
the extension's equivalent of the `dvc exp show` command. | ||
|
||
The view can be manipulated using the controls shown in the extension's view | ||
container. | ||
<p align="center"> | ||
<img src="images/experiments-table.png" alt="Experiment Table" /> | ||
</p> | ||
|
||
Use `DVC: Show Experiments` from the | ||
[Command Palette](command:workbench.action.quickOpen?%22>DVC:%20Show%20Experiments%22) | ||
to open up the experiments table or access it, using the Columns view: | ||
|
||
<p align="center"> | ||
<img src="images/experiments-table-from-side-bar.png" | ||
alt="Columns View" /> | ||
</p> | ||
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The table provides context menus to access basic operation on experiments and | ||
table headers. You can edit parameters, sort or filter, run new experiments and | ||
more: | ||
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<p align="center"> | ||
<img src="images/experiments-table-context-menus.png" | ||
alt="Experiments Table: Context Menus" /> | ||
</p> |
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# Install DVC | ||
|
||
There are quite a few options for installing `DVC`. This extension supports | ||
various installation types. See the `Setup The Workspace` step for more details. | ||
There are a few [options](https://dvc.org/doc/install) for installing `DVC`. | ||
This extension supports all installation types. | ||
|
||
To install the latest version of `DVC` follow your platform specific guide on | ||
[dvc.org](https://dvc.org/doc/install). | ||
To verify the installation run `dvc -h` in a | ||
[Terminal](command:workbench.action.terminal.new). | ||
|
||
To verify the installation run `dvc -h` in your terminal of choice. | ||
If DVC is installed as a global binary or in a Python virtual environment, the | ||
extension would attempt to detect it automatically. If successful, you'll see a | ||
DVC icon like this in the status bar: | ||
|
||
**Note**: Use `Terminal: Create New Terminal` from the Command Palette to create | ||
a new | ||
[integrated terminal](https://code.visualstudio.com/docs/editor/integrated-terminal). | ||
<p align="center"> | ||
<img src="images/install-dvc-status-bar-detected.png" | ||
alt="DVC icon in the status bar" /> | ||
</p> | ||
|
||
If you see instead the crossed circle icon, use the | ||
[Setup Workspace](command:dvc.setupWorkspace) wizard to manually select the | ||
right environment: | ||
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||
<p align="center"> | ||
<img src="images/install-dvc-setup-wizard.png" alt="DVC Setup Wizard" /> | ||
</p> |
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# Monitor your Experiments | ||
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As you run your experiments you can use the same | ||
[`Plots Dashboard`](command:dvc.showPlots) to watch for the metrics and plot | ||
updates: | ||
|
||
<p align="center"> | ||
<img src="images/live-plots-updates.gif" | ||
alt="Live Metric Updates" /> | ||
</p> | ||
|
||
This functionality comes baked in when you update your plots files in your | ||
training code. For example like this, using the | ||
[DVCLive](https://dvc.org/doc/dvclive) library that also | ||
[supports](https://dvc.org/doc/dvclive/ml-frameworks) many popular ML frameworks | ||
to automate this process: | ||
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```python | ||
from dvclive import Live | ||
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live = Live("training_metrics") | ||
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for epoch in range(NUM_EPOCHS): | ||
train_model(...) | ||
metrics = evaluate_model(...) | ||
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for metric_name, value in metrics.items(): | ||
live.log(metric_name, value) | ||
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live.next_step() | ||
``` | ||
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`DVCLive` is _optional_, and you can just append or modify plot files using any | ||
language and any tool. | ||
|
||
💡 `Trends` section of the plots dashboard is being updated automatically based | ||
on the data in the table. You don't even have to manage or write any special | ||
plot files, but you need to enable | ||
[checkpoints](https://dvc.org/doc/user-guide/experiment-management/checkpoints) | ||
in the project. |
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