Creates a training pipeline using the create_training_pipeline method.
Code sample
Java
Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
import
com.google.cloud.aiplatform.v1. DeployedModelRef
;
import
com.google.cloud.aiplatform.v1. EnvVar
;
import
com.google.cloud.aiplatform.v1. FilterSplit
;
import
com.google.cloud.aiplatform.v1. FractionSplit
;
import
com.google.cloud.aiplatform.v1. InputDataConfig
;
import
com.google.cloud.aiplatform.v1. LocationName
;
import
com.google.cloud.aiplatform.v1. Model
;
import
com.google.cloud.aiplatform.v1. Model
. ExportFormat
;
import
com.google.cloud.aiplatform.v1. ModelContainerSpec
;
import
com.google.cloud.aiplatform.v1. PipelineServiceClient
;
import
com.google.cloud.aiplatform.v1. PipelineServiceSettings
;
import
com.google.cloud.aiplatform.v1. Port
;
import
com.google.cloud.aiplatform.v1. PredefinedSplit
;
import
com.google.cloud.aiplatform.v1. PredictSchemata
;
import
com.google.cloud.aiplatform.v1. TimestampSplit
;
import
com.google.cloud.aiplatform.v1. TrainingPipeline
;
import
com.google.protobuf. Value
;
import
com.google.protobuf.util. JsonFormat
;
import
com.google.rpc. Status
;
import
java.io.IOException
;
public
class
CreateTrainingPipelineSample
{
public
static
void
main
(
String
[]
args
)
throws
IOException
{
// TODO(developer): Replace these variables before running the sample.
String
trainingPipelineDisplayName
=
"YOUR_TRAINING_PIPELINE_DISPLAY_NAME"
;
String
project
=
"YOUR_PROJECT_ID"
;
String
datasetId
=
"YOUR_DATASET_ID"
;
String
trainingTaskDefinition
=
"YOUR_TRAINING_TASK_DEFINITION"
;
String
modelDisplayName
=
"YOUR_MODEL_DISPLAY_NAME"
;
createTrainingPipelineSample
(
project
,
trainingPipelineDisplayName
,
datasetId
,
trainingTaskDefinition
,
modelDisplayName
);
}
static
void
createTrainingPipelineSample
(
String
project
,
String
trainingPipelineDisplayName
,
String
datasetId
,
String
trainingTaskDefinition
,
String
modelDisplayName
)
throws
IOException
{
PipelineServiceSettings
pipelineServiceSettings
=
PipelineServiceSettings
.
newBuilder
()
.
setEndpoint
(
"us-central1-aiplatform.googleapis.com:443"
)
.
build
();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try
(
PipelineServiceClient
pipelineServiceClient
=
PipelineServiceClient
.
create
(
pipelineServiceSettings
))
{
String
location
=
"us-central1"
;
LocationName
locationName
=
LocationName
.
of
(
project
,
location
);
String
jsonString
=
"{\"multiLabel\": false, \"modelType\": \"CLOUD\", \"budgetMilliNodeHours\": 8000,"
+
" \"disableEarlyStopping\": false}"
;
Value
.
Builder
trainingTaskInputs
=
Value
.
newBuilder
();
JsonFormat
.
parser
().
merge
(
jsonString
,
trainingTaskInputs
);
InputDataConfig
trainingInputDataConfig
=
InputDataConfig
.
newBuilder
().
setDatasetId
(
datasetId
).
build
();
Model
model
=
Model
.
newBuilder
().
setDisplayName
(
modelDisplayName
).
build
();
TrainingPipeline
trainingPipeline
=
TrainingPipeline
.
newBuilder
()
.
setDisplayName
(
trainingPipelineDisplayName
)
.
setTrainingTaskDefinition
(
trainingTaskDefinition
)
.
setTrainingTaskInputs
(
trainingTaskInputs
)
.
setInputDataConfig
(
trainingInputDataConfig
)
.
setModelToUpload
(
model
)
.
build
();
TrainingPipeline
trainingPipelineResponse
=
pipelineServiceClient
.
createTrainingPipeline
(
locationName
,
trainingPipeline
);
System
.
out
.
println
(
"Create Training Pipeline Response"
);
System
.
out
.
format
(
"Name: %s\n"
,
trainingPipelineResponse
.
getName
());
System
.
out
.
format
(
"Display Name: %s\n"
,
trainingPipelineResponse
.
getDisplayName
());
System
.
out
.
format
(
"Training Task Definition %s\n"
,
trainingPipelineResponse
.
getTrainingTaskDefinition
());
System
.
out
.
format
(
"Training Task Inputs: %s\n"
,
trainingPipelineResponse
.
getTrainingTaskInputs
());
System
.
out
.
format
(
"Training Task Metadata: %s\n"
,
trainingPipelineResponse
.
getTrainingTaskMetadata
());
System
.
out
.
format
(
"State: %s\n"
,
trainingPipelineResponse
.
getState
());
System
.
out
.
format
(
"Create Time: %s\n"
,
trainingPipelineResponse
.
getCreateTime
());
System
.
out
.
format
(
"StartTime %s\n"
,
trainingPipelineResponse
.
getStartTime
());
System
.
out
.
format
(
"End Time: %s\n"
,
trainingPipelineResponse
.
getEndTime
());
System
.
out
.
format
(
"Update Time: %s\n"
,
trainingPipelineResponse
.
getUpdateTime
());
System
.
out
.
format
(
"Labels: %s\n"
,
trainingPipelineResponse
.
getLabelsMap
());
InputDataConfig
inputDataConfig
=
trainingPipelineResponse
.
getInputDataConfig
();
System
.
out
.
println
(
"Input Data Config"
);
System
.
out
.
format
(
"Dataset Id: %s"
,
inputDataConfig
.
getDatasetId
());
System
.
out
.
format
(
"Annotations Filter: %s\n"
,
inputDataConfig
.
getAnnotationsFilter
());
FractionSplit
fractionSplit
=
inputDataConfig
.
getFractionSplit
();
System
.
out
.
println
(
"Fraction Split"
);
System
.
out
.
format
(
"Training Fraction: %s\n"
,
fractionSplit
.
getTrainingFraction
());
System
.
out
.
format
(
"Validation Fraction: %s\n"
,
fractionSplit
.
getValidationFraction
());
System
.
out
.
format
(
"Test Fraction: %s\n"
,
fractionSplit
.
getTestFraction
());
FilterSplit
filterSplit
=
inputDataConfig
.
getFilterSplit
();
System
.
out
.
println
(
"Filter Split"
);
System
.
out
.
format
(
"Training Filter: %s\n"
,
filterSplit
.
getTrainingFilter
());
System
.
out
.
format
(
"Validation Filter: %s\n"
,
filterSplit
.
getValidationFilter
());
System
.
out
.
format
(
"Test Filter: %s\n"
,
filterSplit
.
getTestFilter
());
PredefinedSplit
predefinedSplit
=
inputDataConfig
.
getPredefinedSplit
();
System
.
out
.
println
(
"Predefined Split"
);
System
.
out
.
format
(
"Key: %s\n"
,
predefinedSplit
.
getKey
());
TimestampSplit
timestampSplit
=
inputDataConfig
.
getTimestampSplit
();
System
.
out
.
println
(
"Timestamp Split"
);
System
.
out
.
format
(
"Training Fraction: %s\n"
,
timestampSplit
.
getTrainingFraction
());
System
.
out
.
format
(
"Validation Fraction: %s\n"
,
timestampSplit
.
getValidationFraction
());
System
.
out
.
format
(
"Test Fraction: %s\n"
,
timestampSplit
.
getTestFraction
());
System
.
out
.
format
(
"Key: %s\n"
,
timestampSplit
.
getKey
());
Model
modelResponse
=
trainingPipelineResponse
.
getModelToUpload
();
System
.
out
.
println
(
"Model To Upload"
);
System
.
out
.
format
(
"Name: %s\n"
,
modelResponse
.
getName
());
System
.
out
.
format
(
"Display Name: %s\n"
,
modelResponse
.
getDisplayName
());
System
.
out
.
format
(
"Description: %s\n"
,
modelResponse
.
getDescription
());
System
.
out
.
format
(
"Metadata Schema Uri: %s\n"
,
modelResponse
.
getMetadataSchemaUri
());
System
.
out
.
format
(
"Metadata: %s\n"
,
modelResponse
.
getMetadata
());
System
.
out
.
format
(
"Training Pipeline: %s\n"
,
modelResponse
.
getTrainingPipeline
());
System
.
out
.
format
(
"Artifact Uri: %s\n"
,
modelResponse
.
getArtifactUri
());
System
.
out
.
format
(
"Supported Deployment Resources Types: %s\n"
,
modelResponse
.
getSupportedDeploymentResourcesTypesList
());
System
.
out
.
format
(
"Supported Input Storage Formats: %s\n"
,
modelResponse
.
getSupportedInputStorageFormatsList
());
System
.
out
.
format
(
"Supported Output Storage Formats: %s\n"
,
modelResponse
.
getSupportedOutputStorageFormatsList
());
System
.
out
.
format
(
"Create Time: %s\n"
,
modelResponse
.
getCreateTime
());
System
.
out
.
format
(
"Update Time: %s\n"
,
modelResponse
.
getUpdateTime
());
System
.
out
.
format
(
"Labels: %sn\n"
,
modelResponse
.
getLabelsMap
());
PredictSchemata
predictSchemata
=
modelResponse
.
getPredictSchemata
();
System
.
out
.
println
(
"Predict Schemata"
);
System
.
out
.
format
(
"Instance Schema Uri: %s\n"
,
predictSchemata
.
getInstanceSchemaUri
());
System
.
out
.
format
(
"Parameters Schema Uri: %s\n"
,
predictSchemata
.
getParametersSchemaUri
());
System
.
out
.
format
(
"Prediction Schema Uri: %s\n"
,
predictSchemata
.
getPredictionSchemaUri
());
for
(
ExportFormat
exportFormat
:
modelResponse
.
getSupportedExportFormatsList
())
{
System
.
out
.
println
(
"Supported Export Format"
);
System
.
out
.
format
(
"Id: %s\n"
,
exportFormat
.
getId
());
}
ModelContainerSpec
modelContainerSpec
=
modelResponse
.
getContainerSpec
();
System
.
out
.
println
(
"Container Spec"
);
System
.
out
.
format
(
"Image Uri: %s\n"
,
modelContainerSpec
.
getImageUri
());
System
.
out
.
format
(
"Command: %s\n"
,
modelContainerSpec
.
getCommandList
());
System
.
out
.
format
(
"Args: %s\n"
,
modelContainerSpec
.
getArgsList
());
System
.
out
.
format
(
"Predict Route: %s\n"
,
modelContainerSpec
.
getPredictRoute
());
System
.
out
.
format
(
"Health Route: %s\n"
,
modelContainerSpec
.
getHealthRoute
());
for
(
EnvVar
envVar
:
modelContainerSpec
.
getEnvList
())
{
System
.
out
.
println
(
"Env"
);
System
.
out
.
format
(
"Name: %s\n"
,
envVar
.
getName
());
System
.
out
.
format
(
"Value: %s\n"
,
envVar
.
getValue
());
}
for
(
Port
port
:
modelContainerSpec
.
getPortsList
())
{
System
.
out
.
println
(
"Port"
);
System
.
out
.
format
(
"Container Port: %s\n"
,
port
.
getContainerPort
());
}
for
(
DeployedModelRef
deployedModelRef
:
modelResponse
.
getDeployedModelsList
())
{
System
.
out
.
println
(
"Deployed Model"
);
System
.
out
.
format
(
"Endpoint: %s\n"
,
deployedModelRef
.
getEndpoint
());
System
.
out
.
format
(
"Deployed Model Id: %s\n"
,
deployedModelRef
.
getDeployedModelId
());
}
Status
status
=
trainingPipelineResponse
.
getError
();
System
.
out
.
println
(
"Error"
);
System
.
out
.
format
(
"Code: %s\n"
,
status
.
getCode
());
System
.
out
.
format
(
"Message: %s\n"
,
status
.
getMessage
());
}
}
}
Python
Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .
To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .
from
google.cloud
import
aiplatform
from
google.protobuf
import
json_format
from
google.protobuf.struct_pb2
import
Value
def
create_training_pipeline_sample
(
project
:
str
,
display_name
:
str
,
training_task_definition
:
str
,
dataset_id
:
str
,
model_display_name
:
str
,
location
:
str
=
"us-central1"
,
api_endpoint
:
str
=
"us-central1-aiplatform.googleapis.com"
,
):
# The AI Platform services require regional API endpoints.
client_options
=
{
"api_endpoint"
:
api_endpoint
}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client
=
aiplatform
.
gapic
.
PipelineServiceClient
(
client_options
=
client_options
)
training_task_inputs_dict
=
{
"multiLabel"
:
True
,
"modelType"
:
"CLOUD"
,
"budgetMilliNodeHours"
:
8000
,
"disableEarlyStopping"
:
False
,
}
training_task_inputs
=
json_format
.
ParseDict
(
training_task_inputs_dict
,
Value
())
training_pipeline
=
{
"display_name"
:
display_name
,
"training_task_definition"
:
training_task_definition
,
"training_task_inputs"
:
training_task_inputs
,
"input_data_config"
:
{
"dataset_id"
:
dataset_id
},
"model_to_upload"
:
{
"display_name"
:
model_display_name
},
}
parent
=
f
"projects/
{
project
}
/locations/
{
location
}
"
response
=
client
.
create_training_pipeline
(
parent
=
parent
,
training_pipeline
=
training_pipeline
)
print
(
"response:"
,
response
)
What's next
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