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112 lines
6.1 KiB
Markdown
112 lines
6.1 KiB
Markdown
---
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title: Overview
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---
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<div class="vid" >
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<iframe style={{position: 'absolute', top: '0', left: '0', bottom: '0', right: '0', width: '100%', height: '100%'}}
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src="https://www.youtube.com/embed/j4XVMAaUt3E"
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title="YouTube video player"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; fullscreen"
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allowfullscreen>
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</iframe>
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</div>
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<br/>
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Using [ClearML Agent](../clearml_agent.md) and [queues](../fundamentals/agents_and_queues.md#what-is-a-queue), you can
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easily run your code remotely on more powerful machines, including cloud instances.
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Manually spinning up new virtual machines and setting up ClearML Agents on them can become a recurring task as your
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workload grows, not to mention avoiding paying for running machines that aren’t being used, which can become pricey.
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This is where autoscaling comes into the picture.
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ClearML provides the following options to automate your resource scaling, while optimizing machine usage:
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* [ClearML autoscaler applications](#autoscaler-applications) - Use the apps to define your compute resource budget,
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and have the apps automatically manage your resource consumption as needed–with no code!
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* [Kubernetes integration](#kubernetes) - Deploy agents through Kubernetes, which handles resource management and scaling
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## Autoscaler Applications
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ClearML provides the following GUI autoscaler applications:
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* [GPU Compute](../webapp/applications/apps_gpu_compute.md) (powered by Genesis Cloud)
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* [AWS Autoscaler](../webapp/applications/apps_aws_autoscaler.md)
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* [GCP Autoscaler](../webapp/applications/apps_gcp_autoscaler.md)
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The autoscalers automatically spin up or down cloud instances as needed and according to a budget that you set, so you
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pay only for the time that you actually use the machines.
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The **AWS** and **GCP** autoscaler applications will manage instances on your behalf in your cloud account. When
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launching an app instance, you will provide your cloud service credentials so the autoscaler can access your account.
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The **GPU Compute** application provides on-demand GPU instances powered by Genesis. All you need to do is define your compute resource budget, and you’re good to go.
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## How ClearML Autoscaler Apps Work
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The diagram above demonstrates a typical flow for executing tasks through an autoscaler app:
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1. [Create a queue](../webapp/webapp_workers_queues.md#queues) to attach the autoscaler to
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1. Set up an autoscaler app instance: assign it to a queue and define a compute resource budget (see the specific
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autoscaler pages for further setup details)
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1. Launch the autoscaler app instance
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1. Enqueue a task to the queue the autoscaler has been assigned to
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1. The autoscaler attached to the queue spins up and prepares a new compute resource to execute the enqueued task
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1. Enqueue additional tasks: if there are not enough machines to execute the tasks, the autoscaler spins up additional
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machines to execute the tasks (until the maximum number specified in the budget is reached)
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1. If a machine becomes idle since there are no tasks to execute, the autoscaler automatically spins it down
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### Utilizing Multiple Compute Resource Types
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You can work with multiple compute resources through the autoscalers, where each compute resource is associated with a
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different queue. When a queue detects a task, the autoscaler spins up the appropriate resource to execute the task.
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The diagram above demonstrates an example where an autoscaler app instance is attached to two queues. Each queue is
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associated with a different resource, CPU and GPU, and each queue has two enqueued tasks. In order to execute the tasks,
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the autoscaler spins up four machines, two CPU machines to execute the tasks in the CPU queue and two GPU machines to
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execute the tasks in the GPU queue.
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:::note
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The GPU Compute app spins up a single compute resource, so you can launch multiple app instances in order to work with
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multiple resources.
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:::
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### Task Execution Configuration
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#### Docker
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Every task a cloud instance pulls will be run inside a docker container. When setting up an autoscaler app instance,
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you can specify a default container to run the tasks inside. If the task has its own container configured, it will
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override the autoscaler’s default docker image (see [Base Docker Image](../clearml_agent.md#base-docker-container)).
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#### Git Configuration
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If your code is saved in a private repository, you can add your Git credentials so the ClearML Agents running on your
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cloud instances will be able to retrieve the code from your repos.
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#### Cloud Storage Access
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If your tasks need to access data stored in cloud storage, you can provide your cloud storage credentials, so the
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executed tasks will have access to your storage service.
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#### Additional Configuration
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Go to a specific app’s documentation page to view all configuration options
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* [GPU Compute](../webapp/applications/apps_gpu_compute.md)
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* [AWS Autoscaler](../webapp/applications/apps_aws_autoscaler.md)
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* [GCP Autoscaler](../webapp/applications/apps_gcp_autoscaler.md)
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## Kubernetes
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You can install `clearml-agent` through a Helm chart.
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The Clearml Agent deployment is set to service a queue(s). When tasks are added to the queues, the agent pulls the task
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and creates a pod to execute the task. Kubernetes handles resource management. Your task pod will remain pending until
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enough resources are available.
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You can set up Kubernetes' cluster autoscaler to work with your cloud providers, which automatically adjusts the size of
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your Kubernetes cluster as needed; increasing the amount of nodes when there aren't enough to execute pods and removing
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underutilized nodes. See [charts](https://github.com/kubernetes/autoscaler/tree/master/charts) for specific cloud providers.
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:::note Enterprise features
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The ClearML Enterprise plan supports K8S servicing multiple ClearML queues, as well as providing a pod template for each
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queue for describing the resources for each pod to use. See [ClearML Helm Charts](https://github.com/allegroai/clearml-helm-charts/tree/main).
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:::
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