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CustomJob(
display_name: str,
worker_pool_specs: typing.Union[
typing.List[typing.Dict],
typing.List[google.cloud.aiplatform_v1.types.custom_job.WorkerPoolSpec],
],
base_output_dir: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
encryption_spec_key_name: typing.Optional[str] = None,
staging_bucket: typing.Optional[str] = None,
persistent_resource_id: typing.Optional[str] = None,
)
Vertex AI Custom Job.
Properties
create_time
Time this resource was created.
display_name
Display name of this resource.
encryption_spec
Customer-managed encryption key options for this Vertex AI resource.
If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.
end_time
Time when the Job resource entered the JOB_STATE_SUCCEEDED
,
JOB_STATE_FAILED
, or JOB_STATE_CANCELLED
state.
error
Detailed error info for this Job resource. Only populated when the
Job's state is JOB_STATE_FAILED
or JOB_STATE_CANCELLED
.
gca_resource
The underlying resource proto representation.
labels
User-defined labels containing metadata about this resource.
Read more about labels at https://goo.gl/xmQnxf
name
Name of this resource.
network
The full name of the Google Compute Engine network to which this CustomJob should be peered.
Takes the format projects/{project}/global/networks/{network}
. Where
{project} is a project number, as in 12345
, and {network} is a network name.
Private services access must already be configured for the network. If left unspecified, the CustomJob is not peered with any network.
preview
Exposes features available in preview for this class.
resource_name
Full qualified resource name.
start_time
Time when the Job resource entered the JOB_STATE_RUNNING
for the
first time.
state
Fetch Job again and return the current JobState.
Returns | |
---|---|
Type | Description |
state (job_state.JobState) |
Enum that describes the state of a Vertex AI job. |
update_time
Time this resource was last updated.
web_access_uris
Fetch the runnable job again and return the latest web access uris.
Returns | |
---|---|
Type | Description |
(Dict[str, Union[str, Dict[str, str]]]) |
Web access uris of the runnable job. |
Methods
CustomJob
CustomJob(
display_name: str,
worker_pool_specs: typing.Union[
typing.List[typing.Dict],
typing.List[google.cloud.aiplatform_v1.types.custom_job.WorkerPoolSpec],
],
base_output_dir: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
encryption_spec_key_name: typing.Optional[str] = None,
staging_bucket: typing.Optional[str] = None,
persistent_resource_id: typing.Optional[str] = None,
)
Constructs a Custom Job with Worker Pool Specs.
Example usage:
worker_pool_specs = [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": container_image_uri,
"command": [],
"args": [],
},
}
]
my_job = aiplatform.CustomJob(
display_name='my_job',
worker_pool_specs=worker_pool_specs,
labels={'my_key': 'my_value'},
)
my_job.run()
For more information on configuring worker pool specs please visit: https://cloud.google.com/ai-platform-unified/docs/training/create-custom-job
Parameters | |
---|---|
Name | Description |
display_name |
str
Required. The user-defined name of the HyperparameterTuningJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. |
worker_pool_specs |
Union[List[Dict], List[aiplatform.gapic.WorkerPoolSpec]]
Required. The spec of the worker pools including machine type and Docker image. Can provided as a list of dictionaries or list of WorkerPoolSpec proto messages. |
base_output_dir |
str
Optional. GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. |
project |
str
Optional.Project to run the custom job in. Overrides project set in aiplatform.init. |
location |
str
Optional.Location to run the custom job in. Overrides location set in aiplatform.init. |
credentials |
auth_credentials.Credentials
Optional.Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init. |
labels |
Dict[str, str]
Optional. The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
encryption_spec_key_name |
str
Optional.Customer-managed encryption key name for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key. |
staging_bucket |
str
Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init. |
persistent_resource_id |
str
Optional. The ID of the PersistentResource in the same Project and Location. 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. |
Exceptions | |
---|---|
Type | Description |
RuntimeError |
If staging bucket was not set using aiplatform.init and a staging bucket was not passed in. |
cancel
cancel() -> None
Cancels this Job.
Success of cancellation is not guaranteed. Use Job.state
property to verify if cancellation was successful.
delete
delete(sync: bool = True) -> None
Deletes this Vertex AI resource. WARNING: This deletion is permanent.
Parameter | |
---|---|
Name | Description |
sync |
bool
Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. |
done
done() -> bool
Method indicating whether a job has completed.
from_local_script
from_local_script(
display_name: str,
script_path: str,
container_uri: str,
enable_autolog: bool = False,
args: typing.Optional[typing.Sequence[str]] = None,
requirements: typing.Optional[typing.Sequence[str]] = None,
environment_variables: typing.Optional[typing.Dict[str, str]] = None,
replica_count: int = 1,
machine_type: str = "n1-standard-4",
accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
accelerator_count: int = 0,
boot_disk_type: str = "pd-ssd",
boot_disk_size_gb: int = 100,
reduction_server_replica_count: int = 0,
reduction_server_machine_type: typing.Optional[str] = None,
reduction_server_container_uri: typing.Optional[str] = None,
base_output_dir: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
encryption_spec_key_name: typing.Optional[str] = None,
staging_bucket: typing.Optional[str] = None,
persistent_resource_id: typing.Optional[str] = None,
tpu_topology: typing.Optional[str] = None,
) -> google.cloud.aiplatform.jobs.CustomJob
Configures a custom job from a local script.
Example usage:
job = aiplatform.CustomJob.from_local_script(
display_name="my-custom-job",
script_path="training_script.py",
container_uri="gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest",
requirements=["gcsfs==0.7.1"],
replica_count=1,
args=['--dataset', 'gs://my-bucket/my-dataset',
'--model_output_uri', 'gs://my-bucket/model']
labels={'my_key': 'my_value'},
)
job.run()
Parameters | |
---|---|
Name | Description |
display_name |
str
Required. The user-defined name of this CustomJob. |
script_path |
str
Required. Local path to training script. |
container_uri |
str
Required. Uri of the training container image to use for custom job. Support images in Artifact Registry, Container Registry, or Docker Hub. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of |
enable_autolog |
bool
Optional. If True, the Vertex Experiments autologging feature will be enabled in the CustomJob. Note that this will wrap your training script with some autologging-related code. |
args |
Optional[Sequence[str]]
Optional. Command line arguments to be passed to the Python task. |
requirements |
Sequence[str]
Optional. List of python packages dependencies of script. |
environment_variables |
Dict[str, str]
Optional. Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique. environment_variables = { 'MY_KEY': 'MY_VALUE' } |
replica_count |
int
Optional. The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. |
machine_type |
str
Optional. The type of machine to use for training. |
accelerator_type |
str
Optional. Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 |
accelerator_count |
int
Optional. The number of accelerators to attach to a worker replica. |
boot_disk_type |
str
Optional. Type of the boot disk, default is |
boot_disk_size_gb |
int
Optional. Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000]. |
reduction_server_replica_count |
int
The number of reduction server replicas, default is 0. |
reduction_server_machine_type |
str
Optional. The type of machine to use for reduction server. |
reduction_server_container_uri |
str
Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server |
base_output_dir |
str
Optional. GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. |
project |
str
Optional. Project to run the custom job in. Overrides project set in aiplatform.init. |
location |
str
Optional. Location to run the custom job in. Overrides location set in aiplatform.init. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init. |
labels |
Dict[str, str]
Optional. The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
encryption_spec_key_name |
str
Optional. Customer-managed encryption key name for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key. |
staging_bucket |
str
Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init. |
persistent_resource_id |
str
Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected. |
tpu_topology |
str
Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type. |
Exceptions | |
---|---|
Type | Description |
RuntimeError |
If staging bucket was not set using aiplatform.init and a staging bucket was not passed in. |
get
get(
resource_name: str,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> google.cloud.aiplatform.jobs._RunnableJob
Get a Vertex AI Job for the given resource_name.
Parameters | |
---|---|
Name | Description |
resource_name |
str
Required. A fully-qualified resource name or ID. |
project |
str
Optional. project to retrieve dataset from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. location to retrieve dataset from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. |
list
list(
filter: typing.Optional[str] = None,
order_by: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]
List all instances of this Job Resource.
Example Usage:
aiplatform.BatchPredictionJobs.list( filter='state="JOB_STATE_SUCCEEDED" AND display_name="my_job"', )
Parameters | |
---|---|
Name | Description |
filter |
str
Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. |
order_by |
str
Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: |
project |
str
Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. |
run
run(
service_account: typing.Optional[str] = None,
network: typing.Optional[str] = None,
timeout: typing.Optional[int] = None,
restart_job_on_worker_restart: bool = False,
enable_web_access: bool = False,
experiment: typing.Optional[
typing.Union[
google.cloud.aiplatform.metadata.experiment_resources.Experiment, str
]
] = None,
experiment_run: typing.Optional[
typing.Union[
google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun, str
]
] = None,
tensorboard: typing.Optional[str] = None,
sync: bool = True,
create_request_timeout: typing.Optional[float] = None,
disable_retries: bool = False,
persistent_resource_id: typing.Optional[str] = None,
) -> None
Run this configured CustomJob.
Parameters | |
---|---|
Name | Description |
service_account |
str
Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. |
network |
str
Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network. |
timeout |
int
The maximum job running time in seconds. The default is 7 days. |
restart_job_on_worker_restart |
bool
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. |
enable_web_access |
bool
Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell |
experiment |
Union[aiplatform.Experiment, str]
Optional. The instance or name of an Experiment resource to which this CustomJob will upload training parameters and metrics. |
experiment_run |
Union[aiplatform.ExperimentRun, str]
Optional. The instance or name of an ExperimentRun resource to which this CustomJob will upload training parameters and metrics. This arg can only be set when |
tensorboard |
str
Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: |
sync |
bool
Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
disable_retries |
bool
Indicates if the job should retry for internal errors after the job starts running. If True, overrides |
persistent_resource_id |
str
Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected. |
submit
submit(
*,
service_account: typing.Optional[str] = None,
network: typing.Optional[str] = None,
timeout: typing.Optional[int] = None,
restart_job_on_worker_restart: bool = False,
enable_web_access: bool = False,
experiment: typing.Optional[
typing.Union[
google.cloud.aiplatform.metadata.experiment_resources.Experiment, str
]
] = None,
experiment_run: typing.Optional[
typing.Union[
google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun, str
]
] = None,
tensorboard: typing.Optional[str] = None,
create_request_timeout: typing.Optional[float] = None,
disable_retries: bool = False,
persistent_resource_id: typing.Optional[str] = None
) -> None
Submit the configured CustomJob.
Parameters | |
---|---|
Name | Description |
service_account |
str
Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. |
network |
str
Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. |
timeout |
int
The maximum job running time in seconds. The default is 7 days. |
restart_job_on_worker_restart |
bool
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. |
enable_web_access |
bool
Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell |
experiment |
Union[aiplatform.Experiment, str]
Optional. The instance or name of an Experiment resource to which this CustomJob will upload training parameters and metrics. |
experiment_run |
Union[aiplatform.ExperimentRun, str]
Optional. The instance or name of an ExperimentRun resource to which this CustomJob will upload training parameters and metrics. This arg can only be set when |
tensorboard |
str
Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
disable_retries |
bool
Indicates if the job should retry for internal errors after the job starts running. If True, overrides |
persistent_resource_id |
str
Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected. |
Exceptions | |
---|---|
Type | Description |
ValueError |
If both experiment and tensorboard are specified or if enable_autolog is True in CustomJob.from_local_script but experiment is not specified or the specified experiment doesn't have a backing tensorboard. |
to_dict
to_dict() -> typing.Dict[str, typing.Any]
Returns the resource proto as a dictionary.
wait
wait()
Helper method that blocks until all futures are complete.
wait_for_completion
wait_for_completion() -> None
Waits for job to complete.
Exceptions | |
---|---|
Type | Description |
RuntimeError |
If job failed or cancelled. |
wait_for_resource_creation
wait_for_resource_creation() -> None
Waits until resource has been created.