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Add scaling usecase

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17
docs/getting_started/scaling_resources.md
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@ -1,4 +1,3 @@
---
title: Autoscaling Resources
---
@ -19,7 +18,7 @@ ClearML offers the following resource autoscaling solutions:
* [Kubernetes integration](#kubernetes-integration) - Deploy agents in Kubernetes for automated resource allocation and scaling
* [Custom autoscaler implementation](#custom-autoscaler-implementation) using the `AutoScaler` class
### GUI Autoscaler Applications
## GUI Autoscaler Applications
For users on Pro and Enterprise plans, ClearML provides a UI applications to configure autoscaling for cloud
resources. These applications include:
* [AWS Autoscaler](../webapp/applications/apps_aws_autoscaler.md): Automatically provisions and shuts down AWS EC2 instances based on workload demand.
@ -27,7 +26,7 @@ resources. These applications include:
These applications allow users to set up autoscaling with minimal configuration, defining compute budgets and resource limits directly through the UI.
### Kubernetes Integration
## Kubernetes Integration
You can install `clearml-agent` through a Helm chart.
@ -51,15 +50,15 @@ The ClearML Enterprise plan supports K8S servicing multiple ClearML queues, as w
queue for describing the resources for each pod to use. See [ClearML Helm Charts](https://github.com/clearml/clearml-helm-charts/tree/main).
:::
### Custom Autoscaler Implementation
Users can build their own autoscaler using the [`clearml.automation.auto_scaler.AutoScaler`](https://github.com/clearml/clearml/blob/master/clearml/automation/auto_scaler.py#L77) class which enables:
* Direct control over instance scaling logic.
* Custom rules for resource allocation.
## Custom Autoscaler Implementation
You can build their own autoscaler using the [`clearml.automation.auto_scaler.AutoScaler`](https://github.com/clearml/clearml/blob/master/clearml/automation/auto_scaler.py#L77) class which enables:
* Direct control over instance scaling logic
* Custom rules for resource allocation
An `AutoScaler` instance monitors ClearML task queues and dynamically adjusts the number of cloud instances based on workload demand.
By integrating with a [CloudDriver](https://github.com/clearml/clearml/blob/master/clearml/automation/cloud_driver.py#L62),
it supports multiple cloud providers like AWS and GCP.
it supports cloud providers like AWS and GCP.
See the [AWS Autoscaler Example](../guides/services/aws_autoscaler.md) for a practical implementation using the
AutoScaler class. The script can be adapted for GCP autoscaling as well.
`AutoScaler` class. The script can be adapted for GCP autoscaling as well.

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sidebars.js
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@ -64,6 +64,7 @@ module.exports = {
'getting_started/clearml_agent_docker_exec',
'getting_started/clearml_agent_base_docker',
'getting_started/clearml_agent_scheduling',
'getting_started/scaling_resources',
{"Deploying Model Endpoints": [
{
type: 'category',