Represents the spec of a CustomJob.
persistentResourceId
string
Optional. The id of the PersistentResource in the same Project and Location which to run
If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.
workerPoolSpecs[]
object (WorkerPoolSpec
)
Required. The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
scheduling
object (Scheduling
)
Scheduling options for a CustomJob.
serviceAccount
string
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 for the CustomJob's project is used.
network
string
Optional. 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.
reservedIpRanges[]
string
Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job.
If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network.
Example: ['vertex-ai-ip-range'].
baseOutputDirectory
object (GcsDestination
)
The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id
under its parent HyperparameterTuningJob's baseOutputDirectory.
The following Vertex AI environment variables will be passed to containers or python modules when this field is set:
For CustomJob:
- AIP_MODEL_DIR =
<baseOutputDirectory>/model/
- AIP_CHECKPOINT_DIR =
<baseOutputDirectory>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<baseOutputDirectory>/logs/
For CustomJob backing a Trial of HyperparameterTuningJob:
- AIP_MODEL_DIR =
<baseOutputDirectory>/<trial_id>/model/
- AIP_CHECKPOINT_DIR =
<baseOutputDirectory>/<trial_id>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<baseOutputDirectory>/<trial_id>/logs/
protectedArtifactLocationId
string
The id of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
tensorboard
string
Optional. The name of a Vertex AI Tensorboard
resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
enableWebAccess
boolean
Optional. Whether you want Vertex AI to enable interactive shell access to training containers.
If set to true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris
or Trial.web_access_uris
(within HyperparameterTuningJob.trials
).
enableDashboardAccess
boolean
Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container.
If set to true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris
or Trial.web_access_uris
(within HyperparameterTuningJob.trials
).
experiment
string
Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experimentRun
string
Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
models[]
string
Optional. The name of the Model resources for which to generate a mapping to artifact URIs. Applicable only to some of the Google-provided custom jobs. Format: projects/{project}/locations/{location}/models/{model}
In order to retrieve a specific version of the model, also provide the version id or version alias. Example: projects/{project}/locations/{location}/models/{model}@2
or projects/{project}/locations/{location}/models/{model}@golden
If no version id or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version.
JSON representation |
---|
{ "persistentResourceId": string, "workerPoolSpecs": [ { object ( |
WorkerPoolSpec
Represents the spec of a worker pool in a job.
machineSpec
object (MachineSpec
)
Optional. Immutable. The specification of a single machine.
replicaCount
string (int64 format)
Optional. The number of worker replicas to use for this worker pool.
nfsMounts[]
object (NfsMount
)
Optional. List of NFS mount spec.
diskSpec
object (DiskSpec
)
Disk spec.
task
. The custom task to be executed in this worker pool. task
can be only one of the following:
containerSpec
object (ContainerSpec
)
The custom container task.
pythonPackageSpec
object (PythonPackageSpec
)
The Python packaged task.
JSON representation |
---|
{ "machineSpec": { object ( |
ContainerSpec
The spec of a Container.
imageUri
string
Required. The URI of a container image in the Container Registry that is to be run on each worker replica.
command[]
string
The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
args[]
string
The arguments to be passed when starting the container.
env[]
object (EnvVar
)
Environment variables to be passed to the container. Maximum limit is 100.
JSON representation |
---|
{
"imageUri": string,
"command": [
string
],
"args": [
string
],
"env": [
{
object ( |
PythonPackageSpec
The spec of a Python packaged code.
executorImageUri
string
Required. The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
packageUris[]
string
Required. 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.
pythonModule
string
Required. The Python module name to run after installing the packages.
args[]
string
Command line arguments to be passed to the Python task.
env[]
object (EnvVar
)
Environment variables to be passed to the python module. Maximum limit is 100.
JSON representation |
---|
{
"executorImageUri": string,
"packageUris": [
string
],
"pythonModule": string,
"args": [
string
],
"env": [
{
object ( |
NfsMount
Represents a mount configuration for Network File System (NFS) to mount.
server
string
Required. IP address of the NFS server.
path
string
Required. Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path
mountPoint
string
Required. Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
JSON representation |
---|
{ "server": string, "path": string, "mountPoint": string } |
Scheduling
All parameters related to queuing and scheduling of custom jobs.
timeout
string (Duration
format)
The maximum job running time. The default is 7 days.
A duration in seconds with up to nine fractional digits, ending with 's
'. Example: "3.5s"
.
restartJobOnWorkerRestart
boolean
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.
strategy
enum (Strategy
)
Optional. This determines which type of scheduling strategy to use.
disableRetries
boolean
Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart
to false.
JSON representation |
---|
{
"timeout": string,
"restartJobOnWorkerRestart": boolean,
"strategy": enum ( |
Strategy
Optional. This determines which type of scheduling strategy to use. Right now users have two options such as STANDARD which will use regular on demand resources to schedule the job, the other is SPOT which would leverage spot resources alongwith regular resources to schedule the job.
Enums | |
---|---|
STRATEGY_UNSPECIFIED |
Strategy will default to STANDARD. |
ON_DEMAND |
Regular on-demand provisioning strategy. |
LOW_COST |
Low cost by making potential use of spot resources. |
STANDARD |
Standard provisioning strategy uses regular on-demand resources. |
SPOT |
Spot provisioning strategy uses spot resources. |