clearml-docs/docs/webapp/applications/apps_gpu_compute.md

---
title: GPU Compute
---

:::info Pro Plan Offering
The ClearML GPU Compute App is available under the ClearML Pro plan
::: 

Set up to run your workloads on 100% green cloud machines at optimized costs – no setup required! The ClearML GPU Compute 
Application automatically spins cloud machines up or down based on demand. The app optimizes machine usage according to 
a user defined resource budget: define your budget by specifying the GPU type and number of GPUs you want to use.

Each application instance monitors a ClearML queue: new cloud machines are spun up if there are pending jobs on the 
queue. The app instance automatically terminates idle machines based on a specified maximum idle time.

For more information about how autoscalers work, see [Autoscalers Overview](../../cloud_autoscaling/autoscaling_overview.md#autoscaler-applications).

## GPU Compute Instance Configuration
* **Import Configuration** - Import an app instance configuration file. This will fill the configuration wizard with the 
  values from the file, which can be modified before launching the app instance
* **Machine Specification** 
    * GPU Type - NVIDIA GPU on the machine
    * Number of GPUs - Number of GPUs in the cloud machine
    * The rest of the machine’s available resources are dependent on the number and type of GPUs specified above:  
      * vCPUs - Number of vCPUs in the cloud machine
      * Memory - RAM available to the cloud machine
      * Hourly Price - Machine's hourly rate   
      * Disk - Amount of Disk space available to the cloud machine
    * Monitored Queue - Queue associated with application instance. The tasks enqueued to this queue will be executed on 
      machines of this specification
    * Cloud Machine Limit - Maximum number of concurrent machines to launch
* **Idle Time Limit** (optional) - Maximum time in minutes that a cloud machine can be idle before it is spun down
* **Default Docker Image** - Default Docker image in which the ClearML Agent will run. Provide a Docker stored 
  in a Docker artifactory so instances can automatically fetch it
* **Git Configuration** - Git credentials with which the ClearML Agents running on your cloud instances will access your repositories to retrieve the code for their jobs
    * Git User
    * Git Password / Personal Access Token
* **Cloud Storage Access** (optional) - Access credentials to cloud storage service. Provides ClearML Tasks running on cloud 
  machines access to your storage
* **Additional ClearML Configuration** (optional) - A ClearML configuration file to use by the ClearML Agent when executing your experiments

![GPU Compute wizard](../../img/apps_gpu_compute_wizard.png)

## Dashboard

Once a GPU Compute instance is launched, the dashboard displays a summary of your cloud usage and costs.

![GPU Compute dashboard](../../img/apps_gpu_compute_dashboard.png)

The GPU Compute dashboard shows:
* Service status indicator
  * <img src="/docs/latest/icons/ico-server-ok.svg" alt="Working server" className="icon size-md space-sm" /> - Cloud service is available
  * <img src="/docs/latest/icons/ico-server-alert.svg" alt="Not working server" className="icon size-md space-sm" /> - Cloud service is currently unavailable
* Cloud instance details 
  * GPU type
  * Number of GPUs
  * Number of vCPUs
  * RAM 
  * Storage
* Cost details 
  * Instance rate 
  * Total cost for current billing cycle
* Number of current running cloud instances 
* Instance History - Number of running cloud instances over time  
* Console - The log shows updates of cloud instances being spun up/down. 

:::tip Console Debugging   
To make the autoscaler console log show additional debug information, change an active app instance’s log level to DEBUG:
1. Go to the app instance task’s page > **CONFIGURATION** tab > **USER PROPERTIES** section 
1. Hover over the section > Click `Edit` > Click `+ADD PARAMETER`
1. Input `log_level` as the key and `DEBUG` as the value of the new parameter.

![Autoscaler debugging](../../img/webapp_autoscaler_debug_log.png)

The console’s log level will update in the autoscaler's next iteration.  
:::

:::tip EMBEDDING CLEARML VISUALIZATION
You can embed plots from the app instance dashboard into [ClearML Reports](../webapp_reports.md). These visualizations 
are updated live as the app instance(s) updates. The Enterprise Plan and Hosted Service support embedding resources in 
external tools (e.g. Notion). Hover over the plot and click <img src="/docs/latest/icons/ico-plotly-embed-code.svg" alt="Embed code" className="icon size-md space-sm" /> 
to copy the embed code, and navigate to a report to paste the embed code.
:::