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162 lines
8.8 KiB
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162 lines
8.8 KiB
Markdown
---
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title: GCP Autoscaler
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---
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:::info Pro Plan Offering
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The ClearML GCP Autoscaler App is available under the ClearML Pro plan
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:::
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The GCP Autoscaler Application optimizes GCP VM instance usage according to a user defined instance budget: Define your
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budget by specifying the type and amount of available compute resources.
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Each resource type is associated with a ClearML [queue](../../fundamentals/agents_and_queues.md#what-is-a-queue) whose
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status determines the need for instances of that resource type (i.e. spin up new instances if there are pending jobs on
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the queue).
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When running, the autoscaler periodically polls your GCP cluster. The autoscaler automatically deletes idle VM instances
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based on a specified maximum idle time, or spins up new VM instances when there aren't enough to execute pending tasks
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in a queue (until reaching the defined maximum number of instances). You can add an init script, which will be executed
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when each VM instance is spun up.
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For more information about how autoscalers work, see [Autoscalers Overview](../../cloud_autoscaling/autoscaling_overview.md#autoscaler-applications).
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## Autoscaler Instance Configuration
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* **Import Configuration** - Import an app instance configuration file. This will fill the configuration wizard with the
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values from the file, which can be modified before launching the app instance
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* **GCP Configuration**
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* GCP Project ID - Project used for spinning up VM instances
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* GCP Zone - The GCP zone where the VM instances will be spun up. See [Regions and zones](https://cloud.google.com/compute/docs/regions-zones)
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* GCP Subnetwork - The GCP subnetwork where the instances will be spun up. GCP setting will be `projects/{project-id}/regions/{region}/subnetworks/{subnetwork}`
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* GCP Credentials - Credentials with which the autoscaler can access your GCP account for spinning VM instances
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up/down. See [Generating GCP Credentials](#generating-gcp-credentials).
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* **Git Configuration** - Git credentials with which the ClearML Agents running on your VM instances will access your
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repositories to retrieve the code for their jobs
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* Git User
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* Git Password / Personal Access Token
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* **Base Docker Image** (optional) - Default Docker image in which the ClearML Agent will run. Provide a Docker stored in a
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Docker artifactory so VM instances can automatically fetch it
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* **Compute Resources**
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* Resource Name - Assign a name to the resource type. This name will appear in the Autoscaler dashboard
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* GCP Machine Type - See list of [machine types](https://cloud.google.com/compute/docs/machine-types)
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* Run in CPU mode - Select to have the autoscaler utilize only CPU VM instances
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* GPU Type - See list of [supported GPUs by instance](https://cloud.google.com/compute/docs/gpus)
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* Use Preemptible Instance - Choose whether VM instances of this type will be [preemptible](https://cloud.google.com/compute/docs/instances/preemptible)
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* Max Number of Instances - Maximum number of concurrent running VM instances of this type allowed
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* Monitored Queue - Queue associated with this VM instance type. The tasks enqueued to this queue will be executed on VM instances of this type
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* Machine Image (optional) - The GCP machine image to launch
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* Disc Size (in GB) (optional)
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* \+ Add Item - Define another resource type
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* **Autoscaler Instance Name** (optional) - Name for the Autoscaler instance. This will appear in the instance list
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* **Max Idle Time** (optional) - Maximum time in minutes that a VM instance can be idle before the autoscaler spins it down
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* **Workers Prefix** (optional) - A Prefix added to workers’ names, associating them with this autoscaler
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* **Polling Interval** (optional) - Time period in minutes at which the designated queue is polled for new tasks
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* **Apply Task Owner Vault Configuration** - Select to apply values from the task owner's [ClearML vault](../webapp_profile.md#configuration-vault) when executing the task
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* **Warn if more than one instance is executing the same task** - Select to print warning to console when multiple
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instances are running the same task. In most cases, this indicates an issue.
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* **Exclude .bashrc script** - Select in order to skip `.bashrc` script execution
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* **Init Script** (optional) - A bash script to execute after launching the VM instance
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* **Additional ClearML Configuration** (optional) - A ClearML configuration file to use by the ClearML Agent when executing your experiments
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* **Export Configuration** - Export the app instance configuration as a JSON file, which you can later import to create
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a new instance with the same configuration.
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:::note Enterprise Feature
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You can utilize the [configuration vault](../../webapp/webapp_profile.md#configuration-vault) to configure GCP
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credentials for the Autoscaler in the following format:
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```
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auto_scaler.v1 {
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gcp {
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gcp_credentials: """
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{
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"type": "service_account",
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...
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}
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"""
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}
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}
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```
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:::
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## Dashboard
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Once an autoscaler is launched, the autoscaler's dashboard provides information about available VM instances and their
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status.
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The autoscaler dashboard shows:
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* Number of Idle Instances
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* Queues and the resource type associated with them
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* Number of current running instances
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* Console: the application log containing everything printed to stdout and stderr appears in the console log. The log
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shows polling results of the autoscaler’s associated queues, including the number of tasks enqueued, and updates VM
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instances being spun up/down
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:::tip Console Debugging
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To make the autoscaler console log show additional debug information, change an active app instance’s log level to DEBUG:
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1. Go to the app instance task’s page > **CONFIGURATION** tab > **USER PROPERTIES** section
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1. Hover over the section > Click `Edit` > Click `+ADD PARAMETER`
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1. Input `log_level` as the key and `DEBUG` as the value of the new parameter.
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The console’s log level will update in the autoscaler's next iteration.
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:::
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* Instance log files - Click to access the app instance's logs. This takes you to the app instance task's ARTIFACTS tab,
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which lists the app instance’s logs. In a log’s `File Path` field, click <img src="/docs/latest/icons/ico-download-json.svg" alt="Download" className="icon size-sm space-sm" />
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to download the complete log.
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:::tip EMBEDDING CLEARML VISUALIZATION
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You can embed plots from the app instance dashboard into [ClearML Reports](../webapp_reports.md). These visualizations
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are updated live as the app instance(s) updates. The Enterprise Plan and Hosted Service support embedding resources in
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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" />
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to copy the embed code, and navigate to a report to paste the embed code.
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:::
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## Generating GCP Credentials
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The autoscaler app accesses your GCP account with the credentials you provide.
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You will need to create a service account with the required access privileges. Then generate credential keys for that
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account to configure the autoscaler app:
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1. In your GCP account, in the project of your choice, go to **APIs & Services** > **Credentials**
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1. Click **+ CREATE CREDENTIALS** and choose the **Service account** option
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1. In the **Create service account** window that is opened, fill out the service account details
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1. Assign the `Service Account User` and `Compute Admin` roles to your service account
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1. Complete creating the account
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1. In the **API & Services** > **Credentials** page, under **Service Accounts**, click on the service account you just
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created and go to its **KEYS** tab
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1. Click **ADD KEY** and create a key in JSON format. Copy the contents of the JSON file.
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1. Go to the GCP Autoscaler wizard **>** open the **GCP Configuration** panel **>** click *Edit* in the
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**GCP Credentials** field.
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Paste the contents of the JSON file from the previous step into the **GCP Credentials** popup.
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