GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs
import type { GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs } from "https://googleapis.deno.dev/v1/aiplatform:v1.ts";
§Properties
The ID of the base
model. If it is specified, the new model will be
trained based on the base
model. Otherwise, the new model will be trained
from scratch. The base
model must be in the same Project and Location as
the new Model to train, and have the same modelType.
The training budget of creating this model, expressed in milli node hours
i.e. 1,000 value in this field means 1 node hour. The actual
metadata.costMilliNodeHours will be equal or less than this value. If
further model training ceases to provide any improvements, it will stop
without using the full budget and the metadata.successfulStopReason will be
model-converged
. Note, node_hour = actual_hour *
number_of_nodes_involved. For modelType cloud
(default), the budget must
be between 8,000 and 800,000 milli node hours, inclusive. The default value
is 192,000 which represents one day in wall time, considering 8 nodes are
used. For model types mobile-tf-low-latency-1
, mobile-tf-versatile-1
,
mobile-tf-high-accuracy-1
, the training budget must be between 1,000 and
100,000 milli node hours, inclusive. The default value is 24,000 which
represents one day in wall time on a single node that is used.
Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.
If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable).
Trainer type for Vision TrainRequest.
The ID of base
model for upTraining. If it is specified, the new model
will be upTrained based on the base
model for upTraining. Otherwise, the
new model will be trained from scratch. The base
model for upTraining
must be in the same Project and Location as the new Model to train, and
have the same modelType.