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Merge branch 'main' of https://github.com/allegroai/clearml-docs into scale_case

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docs/getting_started/clearml_agent_scheduling.md
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@@ -82,7 +82,7 @@ Currently, these runtime properties can only be set using an ClearML REST API ca
endpoint, as follows:
* The body of the request must contain the `worker-id`, and the runtime property to add.
* An expiry date is optional. Use the format `"expiry":<time>`. For example, `"expiry":86400` will set an expiry of 24 hours.
* An expiry date is optional. Use the format `"expiry":<time>`. For example, `"expiry":86400` will set an expiry of 24 hours.
* To delete the property, set the expiry date to zero, `"expiry":0`.
For example, to force a worker on for 24 hours:

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docs/getting_started/project_progress.md
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@@ -6,9 +6,22 @@ ClearML provides a comprehensive set of monitoring tools to help effectively tra
These tools offer both high-level overviews and detailed insights into task execution, resource
utilization, and project performance.
## Offerings
### Project Dashboard
## Project Overview
A project's **OVERVIEW** tab in the UI presents a general picture of a project:
* Metric Snapshot – A graphical representation of selected metric values across project tasks, offering a quick assessment of progress.
* Task Status Tracking – When a single metric variant is selected for the snapshot, task status is color-coded (e.g.,
Completed, Aborted, Published, Failed) for better visibility.
Use the Metric Snapshot to track project progress and identify trends in task performance.
For more information, see [Project Overview](../webapp/webapp_project_overview.md).
![Project Overview](../img/webapp_project_overview.png#light-mode-only)
![Project Overview](../img/webapp_project_overview_dark.png#dark-mode-only)
## Project Dashboard
:::info Pro Plan Offering
The Project Dashboard app is available under the ClearML Pro plan.
@@ -28,16 +41,22 @@ For more information, see [Project Dashboard](../webapp/applications/apps_dashbo
![Project Dashboard](../img/apps_dashboard.png#light-mode-only)
![Project Dashboard](../img/apps_dashboard_dark.png#dark-mode-only)
### Project Overview
## Task Monitoring
A project's **OVERVIEW** tab in the UI presents a general picture of a project:
* Metric Snapshot – A graphical representation of selected metric values across project tasks, offering a quick assessment of progress.
* Task Status Tracking – When a single metric variant is selected for the snapshot, task status is color-coded (e.g.,
Completed, Aborted, Published, Failed) for better visibility.
ClearML provides task monitoring capabilities through the [`clearml.automation.Monitor`](https://github.com/clearml/clearml/blob/master/clearml/automation/monitor.py)
class. With this class you can implement monitoring workflows such as:
Use the Metric Snapshot to track project progress and identify trends in task performance.
* Send notifications via Slack or other channels
* Trigger automated responses based on specific task conditions
For more information, see [Project Overview](../webapp/webapp_project_overview.md).
For a practical example, see the [Slack Alerts Example](../guides/services/slack_alerts.md), which demonstrates how to:
* Track task status (completion, failure, etc.)
* Send notifications to a specified Slack channel
* Retrieve task details such as status, console logs, and links to the ClearML Web UI
You can also configure filters for task types and projects to reduce unnecessary notifications.
![Slack Alerts](../img/examples_slack_alerts.png#light-mode-only)
![Slack Alerts](../img/examples_slack_alerts_dark.png#dark-mode-only)
![Project Overview](../img/webapp_project_overview.png#light-mode-only)
![Project Overview](../img/webapp_project_overview_dark.png#dark-mode-only)

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docs/getting_started/remote_execution.md
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@@ -14,7 +14,7 @@ powerful remote machine. This is useful for:
* Managing execution through ClearML's queue system.
This guide focuses on transitioning a locally executed process to a remote machine for scalable execution. To learn how
to reproduce a previously executed process on a remote machine, see [Reproducing Tasks](reproduce_tasks.md).
to reproduce a previously executed process on a remote machine, see [Reproducing Task Runs](reproduce_tasks.md).
## Running a Task Remotely

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docs/getting_started/reproduce_tasks.md
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@@ -1,5 +1,5 @@
---
title: Reproducing Tasks
title: Reproducing Task Runs
---
:::note

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docs/getting_started/video_tutorials/core_component_overview.md
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@@ -31,7 +31,7 @@ The pip package also includes `clearml-data`. It can help you keep track of your
Both the 2 magic lines and the data tool will send all of their information to a ClearML server. This server then keeps an overview of your experiment runs and data sets over time, so you can always go back to a previous experiment, see how it was created and even recreate it exactly. Keep track of your best models by creating leaderboards based on your own metrics, and you can even directly compare multiple experiment runs, helping you to figure out the best way forward for your models.
To get started with a server right away, you can make use of the free tier. And when your needs grow, we've got you covered too! Just check out our website to find a tier that fits your organisation best. But, because we're open source, you can also host your own completely for free. We have AWS images, Google Cloud images, you can run it on docker-compose locally or even, if you really hate yourself, run it on a self-hosted kubernetes cluster using our helm charts.
To get started with a server right away, you can make use of the free tier. And when your needs grow, we've got you covered too! Just check out our website to find a tier that fits your organisation best. But, because we're open source, you can also host your own completely for free. We have AWS images, Google Cloud images, you can run it on `docker-compose` locally or even, if you really hate yourself, run it on a self-hosted kubernetes cluster using our helm charts.
So, to recap: to get started, all you need is a pip package and a server to store everything. Easy right? But MLOps is much more than experiment and data management. It's also about automation and orchestration, which is exactly where the `clearml-agent` comes into play.