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
YESThe job display name.
YESThe GCP region.
NOThe job specification.
YEStrueYEStrueYESThe GCP service account to impersonate.
YESThe GCP project ID.
YES["https://www.googleapis.com/auth/cloud-platform"]The GCP scopes to be used.
YESThe GCP service account.
date-timeTime when the CustomJob was created.
date-timeTime when the CustomJob was ended.
Resource name of a CustomJob.
JOB_STATE_UNSPECIFIEDJOB_STATE_QUEUEDJOB_STATE_PENDINGJOB_STATE_RUNNINGJOB_STATE_SUCCEEDEDJOB_STATE_FAILEDJOB_STATE_CANCELLINGJOB_STATE_CANCELLEDJOB_STATE_PAUSEDJOB_STATE_EXPIREDJOB_STATE_UPDATINGJOB_STATE_PARTIALLY_SUCCEEDEDUNRECOGNIZEDThe detailed state of the CustomJob.
date-timeTime when the CustomJob was updated.
YESThe 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 }}
YESThe arguments to be passed when starting the container.
YESThe command to be invoked when the container is started.
It overrides the entrypoint instruction in Dockerfile when provided.
YESEnvironment variables to be passed to the container.
Maximum limit is 100.
NOThe Cloud Storage location to store the output of this job.
YESYESThe 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.
NOScheduling options for a CustomJob.
YESSpecifies 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.
YESThe name of a Vertex AI Tensorboard resource to which this CustomJob
will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
YESGoogle 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.
NOThe custom container task.
NOThe specification of a single machine.
NOThe specification of the disk.
NOThe python package specs.
YESYESThe 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.
YESEnvironment variables to be passed to the python module.
Maximum limit is 100.
YESThe 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.
YES100YESPD_SSDPD_SSDPD_STANDARDType of the boot disk.
YESThe type of the machine.
YESYESACCELERATOR_TYPE_UNSPECIFIEDNVIDIA_TESLA_K80NVIDIA_TESLA_P100NVIDIA_TESLA_V100NVIDIA_TESLA_P4NVIDIA_TESLA_T4NVIDIA_TESLA_A100NVIDIA_A100_80GBNVIDIA_L4NVIDIA_H100_80GBNVIDIA_H100_MEGA_80GBTPU_V2TPU_V3TPU_V4_PODTPU_V5_LITEPODUNRECOGNIZEDThe type of accelerator(s) that may be attached to the machine.
YESYESdurationThe maximum job running time. The default is 7 days.