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Re-organize the Studio docs to make it easy for users to get started …
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…with our 2 major features - experiments and model registry
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tapadipti committed Sep 12, 2023
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29 changes: 13 additions & 16 deletions content/docs/sidebar.json
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"slug": "studio",
"icon": "studio",
"children": [
"get-started",
"experiments",
"model-registry",
{
"slug": "user-guide",
"source": false,
"children": [
{
"slug": "projects-and-experiments",
"source": false,
"slug": "experiments",
"children": [
"what-is-a-project",
"create-a-project",
"configure-a-project",
"explore-ml-experiments",
Expand All @@ -648,12 +647,7 @@
},
{
"slug": "model-registry",
"source": false,
"children": [
{
"label": "What is a Model Registry",
"slug": "what-is-a-model-registry"
},
"add-a-model",
"register-version",
"assign-stage",
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},
{
"slug": "team-collaboration",
"source": false,
"children": ["teams", "change-team-plan-and-size"]
"children": ["change-team-plan-and-size"]
},
"account-management",
"billing-and-payment",
"install-github-app",
"connect-custom-gitlab-server"
{
"slug": "account-and-billing",
"children": ["billing-and-payment"]
},
{
"slug": "git-integrations",
"children": ["github-app", "custom-gitlab-server"]
},
"troubleshooting"
]
},
"troubleshooting",
{
"slug": "self-hosting",
"source": false,
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67 changes: 67 additions & 0 deletions content/docs/studio/experiments.md
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# Run and track experiments

1. A project displays your live and complete experiments, including Git commits
and refs. For each experiment, you can compare metrics, parameters, plots,
and other files.

![Project table with live plots](https://static.iterative.ai/img/studio/project-table-with-live-plots.gif)

2. Click on `Add a Project` and connect to the Git repository for your Machine
Learning project.

3. Iterative Studio offers a
[few different ways to track your experiments](/doc/studio/user-guide/experiments#different-ways-to-track-experiments).
To get started quickly, you can track experiments in real-time using
[DVCLive]:

- Install DVCLive:

`pip install dvclive`

- Copy your DVC Studio token and configure your model training environment to
use the token:

`dvc config --global studio.token ***`

- Add DVCLive to your model training code:

```python
from dvclive import Live
with Live(save_dvc_exp=True) as live:
for epoch in range(epochs):
live.log_metric("accuracy", accuracy)
live.log_metric("loss", loss)
live.next_step()
```

- Run the training job:

`python train.py`

4. The metrics will be updated in the project table in real-time.

5. Open the `Plots` pane to view the live updates to your plots.

For more details, check out the
[`experiment management user guide`](/doc/studio/user-guide/experiments).

[project settings]:
/doc/studio/user-guide/projects-and-experiments/configure-a-project
[when do you need project settings]:
/doc/studio/user-guide/projects-and-experiments/configure-a-project#scenarios-where-project-settings-are-required
[create multiple projects from a single git repository]:
/doc/studio/user-guide/projects-and-experiments/create-a-project#create-multiple-projects-from-a-single-git-repository
[explore ml experiments]:
/doc/studio/user-guide/projects-and-experiments/explore-ml-experiments
[create a team]: /doc/studio/user-guide/teams
[sign up for the **basic** or **enterprise** plan]:
/doc/studio/user-guide/change-team-plan-and-size
[make your projects public]:
/doc/studio/user-guide/projects-and-experiments/share-a-project
[train on the cloud, including on your own cloud infrastructure, and submit new experiments]:
/doc/studio/user-guide/projects-and-experiments/run-experiments
[live-metrics-and-plots]:
/doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots
[dvclive]: /doc/dvclive
[monorepo]:
/doc/studio/user-guide/projects-and-experiments/configure-a-project#monorepo
125 changes: 0 additions & 125 deletions content/docs/studio/get-started.md

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47 changes: 29 additions & 18 deletions content/docs/studio/index.md
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Expand Up @@ -6,25 +6,14 @@ track experiments, and visualize and share results. It works on top of
[Git](https://git-scm.com/), and works best with [DVC](https://dvc.org/)
repositories.

<cards>
Sign in to [Iterative Studio](https://studio.iterative.ai/) using your
GitHub.com, GitLab.com, or Bitbucket.org account, or with your email address.
Upon sign-in, the Projects dashboard opens. It includes some demo projects for
you to explore [experiment tracking](/doc/studio/experiments) with DVC Studio.
To explore the [Model Registry](/doc/studio/model-registry), click on the
`Models` tab.

<card href="/doc/studio/get-started" heading="Get started">
A step-by-step tutorial to get started with Iterative Studio
</card>

<card href="/doc/studio/user-guide" heading="User guide">
Non-exhaustive list things you can do with Iterative Studio
</card>

<card href="/doc/studio/troubleshooting" heading="Troubleshooting">
Troubleshoot some common problem scenarios
</card>

</cards>

https://www.youtube.com/watch?v=w-UjAbdpRY4

**Why Iterative Studio?**
## Why Iterative Studio?

- Simplify ML experiment tracking, visualization, and collaboration on top of
Git.
Expand All @@ -37,4 +26,26 @@ https://www.youtube.com/watch?v=w-UjAbdpRY4
- Because your model registry is built on top of Git, you can use [GitOps] for
model deployment.

https://www.youtube.com/watch?v=w-UjAbdpRY4

[gitops]: https://www.gitops.tech/
[project settings]:
/doc/studio/user-guide/projects-and-experiments/configure-a-project
[when do you need project settings]:
/doc/studio/user-guide/projects-and-experiments/configure-a-project#scenarios-where-project-settings-are-required
[create multiple projects from a single git repository]:
/doc/studio/user-guide/projects-and-experiments/create-a-project#create-multiple-projects-from-a-single-git-repository
[explore ml experiments]:
/doc/studio/user-guide/projects-and-experiments/explore-ml-experiments
[create a team]: /doc/studio/user-guide/teams
[sign up for the **basic** or **enterprise** plan]:
/doc/studio/user-guide/change-team-plan-and-size
[make your projects public]:
/doc/studio/user-guide/projects-and-experiments/share-a-project
[train on the cloud, including on your own cloud infrastructure, and submit new experiments]:
/doc/studio/user-guide/projects-and-experiments/run-experiments
[live-metrics-and-plots]:
/doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots
[dvclive]: /doc/dvclive
[monorepo]:
/doc/studio/user-guide/projects-and-experiments/configure-a-project#monorepo
26 changes: 26 additions & 0 deletions content/docs/studio/model-registry.md
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# Manage models

1. Click on the `Models` tab to open the central [Models dashboard]. Iterative
Studio uses your project's `dvc.yaml` files to identify ML models and
specially formatted Git tags to identify model versions and stage
assignments.

[models dashboard]:
/doc/studio/user-guide/model-registry/view-and-compare-models#models-dashboard

2. Click on the model name to
[open its details page](/doc/studio/user-guide/model-registry/view-and-compare-models#model-details-page).

3. You can perform the following actions to manage the lifecycle of models:
- [Add models to the registry](/doc/studio/user-guide/model-registry/add-a-model)
- [Register new versions of the models](/doc/studio/user-guide/model-registry/register-version)
(like `v0.0.1` and `v1.0.0`)
- [Assign stages to model versions](/doc/studio/user-guide/model-registry/assign-stage)
(e.g. `dev`, `testing`, `prod`)
- Use models:
[download them](/doc/studio/user-guide/model-registry/use-models) or
[set up CI/CD](/doc/studio/user-guide/model-registry/use-models) to publish
or deploy models

For more details, check out the
[`model registry user guide`](/doc/studio/user-guide/model-registry/).
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