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BatchPredictionJob
(
batch_prediction_job_name
:
str
,
project
:
typing
.
Optional
[
str
]
=
None
,
location
:
typing
.
Optional
[
str
]
=
None
,
credentials
:
typing
.
Optional
[
google
.
auth
.
credentials
.
Credentials
]
=
None
,
)
Retrieves a BatchPredictionJob resource and instantiates its representation.
Parameters
batch_prediction_job_name
str
Required. A fully-qualified BatchPredictionJob resource name or ID. Example: "projects/.../locations/.../batchPredictionJobs/456" or "456" when project and location are initialized or passed.
project
typing.Optional[str]
Optional[str] = None, Optional. project to retrieve BatchPredictionJob from. If not set, project set in aiplatform.init will be used.
location
typing.Optional[str]
Optional[str] = None, Optional. location to retrieve BatchPredictionJob from. If not set, location set in aiplatform.init will be used.
credentials
typing.Optional[google.auth.credentials.Credentials]
Optional[auth_credentials.Credentials] = None, Custom credentials to use. If not set, credentials set in aiplatform.init will be used.
Properties
completion_stats
Statistics on completed and failed prediction instances.
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.
output_info
Information describing the output of this job, including output location into which prediction output is written.
This is only available for batch prediction jobs that have run successfully.
partial_failures
Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard GCP error details.
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.
state (job_state.JobState)
update_time
Time this resource was last updated.
Methods
cancel
cancel
()
-
> None
Cancels this Job.
Success of cancellation is not guaranteed. Use Job.state
property to verify if cancellation was successful.
create
create
(
job_display_name
:
str
,
model_name
:
typing
.
Union
[
str
,
aiplatform
.
Model
],
instances_format
:
str
=
" jsonl
" ,
predictions_format
:
str
=
" jsonl
" ,
gcs_source
:
typing
.
Optional
[
typing
.
Union
[
str
,
typing
.
Sequence
[
str
]]]
=
None
,
bigquery_source
:
typing
.
Optional
[
str
]
=
None
,
gcs_destination_prefix
:
typing
.
Optional
[
str
]
=
None
,
bigquery_destination_prefix
:
typing
.
Optional
[
str
]
=
None
,
model_parameters
:
typing
.
Optional
[
typing
.
Dict
]
=
None
,
machine_type
:
typing
.
Optional
[
str
]
=
None
,
accelerator_type
:
typing
.
Optional
[
str
]
=
None
,
accelerator_count
:
typing
.
Optional
[
int
]
=
None
,
starting_replica_count
:
typing
.
Optional
[
int
]
=
None
,
max_replica_count
:
typing
.
Optional
[
int
]
=
None
,
generate_explanation
:
typing
.
Optional
[
bool
]
=
False
,
explanation_metadata
:
typing
.
Optional
[
aiplatform
.
explain
.
ExplanationMetadata
]
=
None
,
explanation_parameters
:
typing
.
Optional
[
aiplatform
.
explain
.
ExplanationParameters
]
=
None
,
labels
:
typing
.
Optional
[
typing
.
Dict
[
str
,
str
]]
=
None
,
project
:
typing
.
Optional
[
str
]
=
None
,
location
:
typing
.
Optional
[
str
]
=
None
,
credentials
:
typing
.
Optional
[
google
.
auth
.
credentials
.
Credentials
]
=
None
,
encryption_spec_key_name
:
typing
.
Optional
[
str
]
=
None
,
sync
:
bool
=
True
,
create_request_timeout
:
typing
.
Optional
[
float
]
=
None
,
batch_size
:
typing
.
Optional
[
int
]
=
None
,
model_monitoring_objective_config
:
typing
.
Optional
[
aiplatform
.
model_monitoring
.
ObjectiveConfig
]
=
None
,
model_monitoring_alert_config
:
typing
.
Optional
[
aiplatform
.
model_monitoring
.
AlertConfig
]
=
None
,
analysis_instance_schema_uri
:
typing
.
Optional
[
str
]
=
None
,
service_account
:
typing
.
Optional
[
str
]
=
None
,
)
-
> BatchPredictionJob
Create a batch prediction job.
job_display_name
str
Required. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.
model_name
Union[str, aiplatform.Model]
Required. A fully-qualified model resource name or model ID. Example: "projects/123/locations/us-central1/models/456" or "456" when project and location are initialized or passed. May optionally contain a version ID or alias in {model_name}@{version} form. Or an instance of aiplatform.Model.
instances_format
str
Required. The format in which instances are provided. Must be one of the formats listed in Model.supported_input_storage_formats
. Default is "jsonl" when using gcs_source
. If a bigquery_source
is provided, this is overridden to "bigquery".
predictions_format
str
Required. The format in which Vertex AI outputs the predictions, must be one of the formats specified in Model.supported_output_storage_formats
. Default is "jsonl" when using gcs_destination_prefix
. If a bigquery_destination_prefix
is provided, this is overridden to "bigquery".
gcs_source
Optional[Sequence[str]]
Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match instances_format
.
bigquery_source
Optional[str]
BigQuery URI to a table, up to 2000 characters long. For example: bq://projectId.bqDatasetId.bqTableId
gcs_destination_prefix
Optional[str]
The Google Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction-
, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001.
, predictions_0002.
, ..., predictions_N.
are created where
depends on chosen
predictions_format
, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance
and prediction
schemata defined then each such file contains predictions as per the predictions_format
. If prediction for any instance failed (partially or completely), then an additional errors_0001.
, errors_0002.
,..., errors_N.
files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error
field which as value has `` google.rpc.Status
__ containing only
code
and message
fields.
bigquery_destination_prefix
Optional[str]
The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction_
where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions
, and errors
. If the Model has both instance
and prediction
schemata defined then the tables have columns as follows: The predictions
table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors
table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status][google.rpc.Status]
represented as a STRUCT, and containing only code
and message
.
model_parameters
Optional[Dict]
The parameters that govern the predictions. The schema of the parameters may be specified via the Model's parameters_schema_uri
.
machine_type
Optional[str]
The type of machine for running batch prediction on dedicated resources. Not specifying machine type will result in batch prediction job being run with automatic resources.
accelerator_type
Optional[str]
The type of accelerator(s) that may be attached to the machine as per accelerator_count
. Only used if machine_type
is set.
accelerator_count
Optional[int]
The number of accelerators to attach to the machine_type
. Only used if machine_type
is set.
starting_replica_count
Optional[int]
The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
. Only used if machine_type
is set.
max_replica_count
Optional[int]
The maximum number of machine replicas the batch operation may be scaled to. Only used if machine_type
is set. Default is 10.
generate_explanation
bool
Optional. Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations based on the prediction_format
: - bigquery
: output includes a column named explanation
. The value is a struct that conforms to the [aiplatform.gapic.Explanation] object. - jsonl
: The JSON objects on each line include an additional entry keyed explanation
. The value of the entry is a JSON object that conforms to the [aiplatform.gapic.Explanation] object. - csv
: Generating explanations for CSV format is not supported.
explanation_metadata
aiplatform.explain.ExplanationMetadata
Optional. Explanation metadata configuration for this BatchPredictionJob. Can be specified only if generate_explanation
is set to True
. This value overrides the value of Model.explanation_metadata
. All fields of explanation_metadata
are optional in the request. If a field of the explanation_metadata
object is not populated, the corresponding field of the Model.explanation_metadata
object is inherited. For more details, see Ref docs http://tinyurl.com/1igh60kt
explanation_parameters
aiplatform.explain.ExplanationParameters
Optional. Parameters to configure explaining for Model's predictions. Can be specified only if generate_explanation
is set to True
. This value overrides the value of Model.explanation_parameters
. All fields of explanation_parameters
are optional in the request. If a field of the explanation_parameters
object is not populated, the corresponding field of the Model.explanation_parameters
object is inherited. For more details, see Ref docs http://tinyurl.com/1an4zake
labels
Dict[str, str]
Optional. The labels with user-defined metadata to organize your BatchPredictionJobs. 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.
credentials
Optional[auth_credentials.Credentials]
Custom credentials to use to create this batch prediction job. Overrides credentials set in aiplatform.init.
encryption_spec_key_name
Optional[str]
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the job. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key
. The key needs to be in the same region as where the compute resource is created. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key. Overrides encryption_spec_key_name set in aiplatform.init.
sync
bool
Whether to execute this method 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.
create_request_timeout
float
Optional. The timeout for the create request in seconds.
batch_size
int
Optional. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
model_monitoring_objective_config
aiplatform.model_monitoring.ObjectiveConfig
Optional. The objective config for model monitoring. Passing this parameter enables monitoring on the model associated with this batch prediction job.
model_monitoring_alert_config
aiplatform.model_monitoring.EmailAlertConfig
Optional. Configures how model monitoring alerts are sent to the user. Right now only email alert is supported.
analysis_instance_schema_uri
str
Optional. Only applicable if model_monitoring_objective_config is also passed. This parameter specifies the YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri, meaning that TFDV will use the data in the exact format as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
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.
(jobs.BatchPredictionJob)
delete
delete
(
sync
:
bool
=
True
)
-
> None
Deletes this Vertex AI resource. WARNING: This deletion is permanent.
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.
iter_outputs
iter_outputs
(
bq_max_results
:
typing
.
Optional
[
int
]
=
100
,
)
-
> typing
.
Union
[
typing
.
Iterable
[
storage
.
Blob
],
typing
.
Iterable
[
bigquery
.
table
.
RowIterator
]
]
Returns an Iterable object to traverse the output files, either a list of GCS Blobs or a BigQuery RowIterator depending on the output config set when the BatchPredictionJob was created.
bq_max_results
typing.Optional[int]
Optional[int] = 100 Limit on rows to retrieve from prediction table in BigQuery dataset. Only used when retrieving predictions from a bigquery_destination_prefix. Default is 100.
RuntimeError
NotImplementedError
Union[Iterable[storage.Blob], Iterable[bigquery.table.RowIterator]]
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"', )
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: display_name
, create_time
, update_time
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
()
-
> 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_resource_creation
wait_for_resource_creation
()
-
> None
Waits until resource has been created.