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GPU Compute

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.

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


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

GPU Compute dashboard

The GPU Compute dashboard shows:

  • Service status indicator
    • Working server - Cloud service is available
    • Not working server - 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.
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
  2. Hover over the section > Click Edit > Click +ADD PARAMETER
  3. Input log_level as the key and DEBUG as the value of the new parameter.

Autoscaler debugging

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


You can embed plots from the app instance dashboard into ClearML Reports. 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 Embed code to copy the embed code, and navigate to a report to paste the embed code.