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138 lines
6.3 KiB
Markdown
138 lines
6.3 KiB
Markdown
---
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title: Orchestration
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---
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With the **Orchestration** page, you can:
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* Use Cloud autoscaling apps to define your compute resource budget, and have the apps automatically manage your resource
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consumption as needed–-with no code (available under the ClearML Pro plan)
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* Monitor resources (CPU and GPU, memory, video memory, and network usage) used by the experiments / Tasks that workers
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execute
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* View workers and the queues they listen to
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* Manage worker queues
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* Create and rename queues
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* Delete empty queues
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* Monitor queue utilization
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* Reorder, move, and remove experiments from queues
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* Monitor all of your available and in-use compute resources (available in the ClearML Enterprise plan. See [Orchestration Dashboard](webapp_orchestration_dash.md))
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## Autoscalers
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:::info Pro Plan Offering
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The ClearML Autoscaler apps are available under the ClearML Pro plan
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:::
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Use the **AUTOSCALERS** tab to access ClearML's cloud autoscaling applications:
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* GPU Compute (powered by Genesis Cloud)
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* AWS Autoscaler
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* GCP Autoscaler
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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
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compute resource budget, and you're good to go.
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Once you launch an autoscaler app instance, you can monitor the autoscaler's activity and your cloud usage in the instance's
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dashboard.
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For more information about how autoscalers work, see the [Cloud Autoscaling Overview](../cloud_autoscaling/autoscaling_overview.md).
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For more information about a specific autoscaler, see [GPU Compute](applications/apps_gpu_compute.md), [AWS Autoscaler](applications/apps_aws_autoscaler.md),
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and/or [GCP Autoscaler](applications/apps_gcp_autoscaler.md).
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## Workers
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Use the **WORKERS** tab to track worker activity and monitor worker utilization.
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The page shows a worker activity graph and a worker details table. The graph time span can be controlled through the menu
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at its top right corner. Hover over any plot point to see its data. By default, the **WORKER UTILIZATION** graph displays the
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number of active and total workers over time.
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The worker table shows the currently available workers and their current execution information:
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* Current running experiment
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* Current execution time
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* Training iteration.
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Clicking on a worker will open the worker's details panel and replace the graph with that worker's resource utilization
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information. The resource metric being monitored can be selected through the menu at the graph's top left corner:
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* CPU and GPU Usage
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* Memory Usage
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* Video Memory Usage
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* Network Usage.
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The worker's details panel includes the following two tabs:
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* **INFO** - worker information:
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* Worker Name
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* Update time - The last time the worker reported data
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* Current Experiment - The experiment currently being executed by the worker
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* Experiment Runtime - How long the currently executing experiment has been running
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* Experiment iteration - The last reported training iteration for the experiment
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* **QUEUES** - Information about the queues that the worker is assigned to:
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* Queue - The name of the Queue
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* Next experiment - The next experiment available in this queue
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* In Queue - The number of experiments currently enqueued
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## Queues
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Use the **QUEUES** tab to manage queues and monitor their statistics. The page shows graphs of the average experiment
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wait time and the number of queued experiments, and a queue details table. Hover over any plot point to view its data.
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By default, the graphs display the overall information of all queues.
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The queue table shows the following queue information:
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* Queue - Queue name
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* Workers - Number of workers servicing the queue
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* Next Experiment - The next experiment available in this queue
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* Last Updated - The last time queue contents were modified
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* In Queue - Number of experiments currently enqueued in the queue
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To create a new queue - Click **+ NEW QUEUE** (top left).
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Hover over a queue and click <img src="/docs/latest/icons/ico-copy-to-clipboard.svg" alt="Copy" className="icon size-md space-sm" />
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to copy the queue's ID.
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Right-click on a queue or hover and click its action button <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" />
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to access queue actions:
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* Delete - Delete the queue. Any pending tasks will be dequeued.
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* Rename - Change the queue's name
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* Clear - Remove all pending tasks from the queue
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* Custom action - The ClearML Enterprise Server provides a mechanism to define your own custom actions, which will
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appear in the context menu. See [Custom UI Context Menu Actions](../deploying_clearml/clearml_server_config.md#custom-ui-context-menu-actions)
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Clicking on a queue will open the queue's details panel and replace the graphs with that queue's statistics.
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The queue's details panel includes the following two tabs:
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* **EXPERIMENTS** - A list of experiments in the queue. You can reorder and remove enqueued experiments. See
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[Controlling Queue Contents](#controlling-queue-contents).
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* **WORKERS** - Information about the workers assigned to the queue:
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* Name - Worker name
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* IP - Worker's IP
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* Currently Executing - The experiment currently being executed by the worker
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### Controlling Queue Contents
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Click on an experiment's menu button <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" />
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in the **EXPERIMENTS** tab to reorganize your queue:
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* Move a task to the top or bottom of the queue
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* Move the task to a different queue
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* Dequeue the task
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You can also reorder experiments in a queue by dragging an experiment to a new position in the queue.
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