CustomJob
Start a Vertex AI custom job.
type: "io.kestra.plugin.gcp.vertexai.CustomJob"id: gcp_vertexai_custom_job
namespace: company.team
tasks:
- id: custom_job
type: io.kestra.plugin.gcp.vertexai.CustomJob
projectId: my-gcp-project
region: europe-west1
displayName: Start Custom Job
spec:
workerPoolSpecs:
- containerSpec:
imageUri: gcr.io/my-gcp-project/my-dir/my-image:latest
machineSpec:
machineType: n1-standard-4
replicaCount: 1
The job display name.
The GCP region.
The job specification.
Wait for the end of the job.
Allowing to capture job status & logs.
Delete the job at the end.
The GCP service account to impersonate.
The GCP project ID.
The GCP scopes to be used.
The GCP service account.
Time when the CustomJob was created.
Time when the CustomJob was ended.
Resource name of a CustomJob.
The detailed state of the CustomJob.
Time when the CustomJob was updated.
The URI of a container image in the Container Registry that is to be run on each worker replica.
Must be on google container registry, example: gcr.io/{{ project }}/{{ dir }}/{{ image }}:{{ tag }}
The arguments to be passed when starting the container.
The command to be invoked when the container is started.
It overrides the entrypoint instruction in Dockerfile when provided.
Environment variables to be passed to the container.
Maximum limit is 100.
The Cloud Storage location to store the output of this job.
Whether you want Vertex AI to enable interactive shell access to training containers.
The full name of the Compute Engine network to which the Job should be peered.
For example, projects/12345/global/networks/myVPC.
Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name.
To specify this field, you must have already configured VPC Network Peering for Vertex AI.
If this field is left unspecified, the job is not peered with any network.
Scheduling options for a CustomJob.
Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.
If unspecified, the [Vertex AI Custom Code Service
Agent](https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents)
for the CustomJob's project is used.
The name of a Vertex AI Tensorboard resource to which this CustomJob
will upload Tensorboard logs. Format:projects/{project}/locations/{location}/tensorboards/{tensorboard}
Google Cloud Storage URI to output directory.
If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
The custom container task.
The specification of a single machine.
The specification of the disk.
The python package specs.
The specification of the disk.
The Google Cloud Storage location of the Python package files which are the training program and its dependent packages.
The maximum number of package URIs is 100.
Environment variables to be passed to the python module.
Maximum limit is 100.
The Google Cloud Storage location of the Python package files which are the training program and its dependent packages.
The maximum number of package URIs is 100.
Size in GB of the boot disk.
Type of the boot disk.
The type of the machine.
The number of accelerators to attach to the machine.
The type of accelerator(s) that may be attached to the machine.
Restarts the entire CustomJob if a worker gets restarted.
This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
The maximum job running time. The default is 7 days.