- 1.73.0 (latest)
- 1.72.0
- 1.71.1
- 1.70.0
- 1.69.0
- 1.68.0
- 1.67.1
- 1.66.0
- 1.65.0
- 1.63.0
- 1.62.0
- 1.60.0
- 1.59.0
- 1.58.0
- 1.57.0
- 1.56.0
- 1.55.0
- 1.54.1
- 1.53.0
- 1.52.0
- 1.51.0
- 1.50.0
- 1.49.0
- 1.48.0
- 1.47.0
- 1.46.0
- 1.45.0
- 1.44.0
- 1.43.0
- 1.39.0
- 1.38.1
- 1.37.0
- 1.36.4
- 1.35.0
- 1.34.0
- 1.33.1
- 1.32.0
- 1.31.1
- 1.30.1
- 1.29.0
- 1.28.1
- 1.27.1
- 1.26.1
- 1.25.0
- 1.24.1
- 1.23.0
- 1.22.1
- 1.21.0
- 1.20.0
- 1.19.1
- 1.18.3
- 1.17.1
- 1.16.1
- 1.15.1
- 1.14.0
- 1.13.1
- 1.12.1
- 1.11.0
- 1.10.0
- 1.9.0
- 1.8.1
- 1.7.1
- 1.6.2
- 1.5.0
- 1.4.3
- 1.3.0
- 1.2.0
- 1.1.1
- 1.0.1
- 0.9.0
- 0.8.0
- 0.7.1
- 0.6.0
- 0.5.1
- 0.4.0
- 0.3.1
TensorboardTimeSeries(
tensorboard_time_series_name: str,
tensorboard_id: Optional[str] = None,
tensorboard_experiment_id: Optional[str] = None,
tensorboard_run_id: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
Managed tensorboard resource for Vertex AI.
Inheritance
builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.tensorboard.tensorboard_resource._TensorboardServiceResource > TensorboardTimeSeriesProperties
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.
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.
resource_name
Full qualified resource name.
update_time
Time this resource was last updated.
Methods
TensorboardTimeSeries
TensorboardTimeSeries(
tensorboard_time_series_name: str,
tensorboard_id: Optional[str] = None,
tensorboard_experiment_id: Optional[str] = None,
tensorboard_run_id: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
Retrieves an existing tensorboard time series given a tensorboard time series name or ID.
Example Usage:
tb_ts = aiplatform.TensorboardTimeSeries(
tensorboard_time_series_name="projects/123/locations/us-central1/tensorboards/456/experiments/789/run/1011/timeSeries/mse"
)
tb_ts = aiplatform.TensorboardTimeSeries(
tensorboard_time_series_name= "mse",
tensorboard_id = "456",
tensorboard_experiment_id = "789"
tensorboard_run_id = "1011"
)
Name | Description |
tensorboard_time_series_name |
str
Required. A fully-qualified tensorboard time series resource name or resource ID. Example: "projects/123/locations/us-central1/tensorboards/456/experiments/789/run/1011/timeSeries/mse" or "mse" when tensorboard_id, tensorboard_experiment_id, tensorboard_run_id are passed and project and location are initialized or passed. |
tensorboard_id |
str
Optional. A tensorboard resource ID. |
tensorboard_experiment_id |
str
Optional. A tensorboard experiment resource ID. |
tensorboard_run_id |
str
Optional. A tensorboard run resource ID. |
project |
str
Optional. Project to retrieve tensorboard from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve tensorboard from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to retrieve this Tensorboard. Overrides credentials set in aiplatform.init. |
Type | Description |
ValueError |
if only one of tensorboard_id or tensorboard_experiment_id is provided. |
create
create(
display_name: str,
tensorboard_run_name: str,
tensorboard_id: Optional[str] = None,
tensorboard_experiment_id: Optional[str] = None,
value_type: Union[
google.cloud.aiplatform_v1.types.tensorboard_time_series.TensorboardTimeSeries.ValueType,
str,
] = "SCALAR",
plugin_name: str = "scalars",
plugin_data: Optional[bytes] = None,
description: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
Creates a new tensorboard time series.
Example Usage:
tb_ts = aiplatform.TensorboardTimeSeries.create(
display_name='my display name',
tensorboard_run_name='my-run'
tensorboard_id='456'
tensorboard_experiment_id='my-experiment'
description='my description',
labels={
'key1': 'value1',
'key2': 'value2'
}
)
Name | Description |
display_name |
str
Optional. User provided name of this TensorboardTimeSeries. This value should be unique among all TensorboardTimeSeries resources belonging to the same TensorboardRun resource (parent resource). |
tensorboard_run_name |
str
Required. The resource name or ID of the TensorboardRun to create the TensorboardTimeseries in. Resource name format: |
tensorboard_id |
str
Optional. The resource ID of the Tensorboard to create the TensorboardTimeSeries in. |
tensorboard_experiment_id |
str
Optional. The ID of the TensorboardExperiment to create the TensorboardTimeSeries in. |
value_type |
Union[gca_tensorboard_time_series.TensorboardTimeSeries.ValueType, str]
Optional. Type of TensorboardTimeSeries value. One of 'SCALAR', 'TENSOR', 'BLOB_SEQUENCE'. |
plugin_name |
str
Optional. Name of the plugin this time series pertain to. |
plugin_data |
bytes
Optional. Data of the current plugin, with the size limited to 65KB. |
description |
str
Optional. Description of this TensorboardTimeseries. |
project |
str
Optional. Project to upload this model to. Overrides project set in aiplatform.init. |
location |
str
Optional. Location to upload this model to. Overrides location set in aiplatform.init. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. |
Type | Description |
TensorboardTimeSeries |
The TensorboardTimeSeries resource. |
delete
delete(sync: bool = True)
Deletes this Vertex AI resource. WARNING: This deletion is permanent.
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. |
list
list(
tensorboard_run_name: str,
tensorboard_id: Optional[str] = None,
tensorboard_experiment_id: Optional[str] = None,
filter: Optional[str] = None,
order_by: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
List all instances of TensorboardTimeSeries in TensorboardRun.
Example Usage:
aiplatform.TensorboardTimeSeries.list(
tensorboard_run_name='projects/my-project/locations/us-central1/tensorboards/123/experiments/my-experiment/runs/my-run'
)
Name | Description |
tensorboard_run_name |
str
Required. The resource name or ID of the TensorboardRun to list the TensorboardTimeseries from. Resource name format: |
tensorboard_id |
str
Optional. The resource ID of the Tensorboard to list the TensorboardTimeSeries from. |
tensorboard_experiment_id |
str
Optional. The ID of the TensorboardExperiment to list the TensorboardTimeSeries from. |
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. |
to_dict
to_dict()
Returns the resource proto as a dictionary.
wait
wait()
Helper method that blocks until all futures are complete.