Gets a training pipeline using the get_training_pipeline method.
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For detailed documentation that includes this code sample, see the following:
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. Model
;
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.cloud.aiplatform.v1. TrainingPipelineName
;
import
com.google.rpc. Status
;
import
java.io.IOException
;
public
class
GetTrainingPipelineSample
{
public
static
void
main
(
String
[]
args
)
throws
IOException
{
// TODO(developer): Replace these variables before running the sample.
String
project
=
"YOUR_PROJECT_ID"
;
String
trainingPipelineId
=
"YOUR_TRAINING_PIPELINE_ID"
;
getTrainingPipeline
(
project
,
trainingPipelineId
);
}
static
void
getTrainingPipeline
(
String
project
,
String
trainingPipelineId
)
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"
;
TrainingPipelineName
trainingPipelineName
=
TrainingPipelineName
.
of
(
project
,
location
,
trainingPipelineId
);
TrainingPipeline
trainingPipelineResponse
=
pipelineServiceClient
.
getTrainingPipeline
(
trainingPipelineName
);
System
.
out
.
println
(
"Get Training Pipeline Response"
);
System
.
out
.
format
(
"\tName: %s\n"
,
trainingPipelineResponse
.
getName
());
System
.
out
.
format
(
"\tDisplay Name: %s\n"
,
trainingPipelineResponse
.
getDisplayName
());
System
.
out
.
format
(
"\tTraining Task Definition: %s\n"
,
trainingPipelineResponse
.
getTrainingTaskDefinition
());
System
.
out
.
format
(
"\tTraining Task Inputs: %s\n"
,
trainingPipelineResponse
.
getTrainingTaskInputs
());
System
.
out
.
format
(
"\tTraining Task Metadata: %s\n"
,
trainingPipelineResponse
.
getTrainingTaskMetadata
());
System
.
out
.
format
(
"\tState: %s\n"
,
trainingPipelineResponse
.
getState
());
System
.
out
.
format
(
"\tCreate Time: %s\n"
,
trainingPipelineResponse
.
getCreateTime
());
System
.
out
.
format
(
"\tStart Time: %s\n"
,
trainingPipelineResponse
.
getStartTime
());
System
.
out
.
format
(
"\tEnd Time: %s\n"
,
trainingPipelineResponse
.
getEndTime
());
System
.
out
.
format
(
"\tUpdate Time: %s\n"
,
trainingPipelineResponse
.
getUpdateTime
());
System
.
out
.
format
(
"\tLabels: %s\n"
,
trainingPipelineResponse
.
getLabelsMap
());
InputDataConfig
inputDataConfig
=
trainingPipelineResponse
.
getInputDataConfig
();
System
.
out
.
println
(
"\tInput Data Config"
);
System
.
out
.
format
(
"\t\tDataset Id: %s\n"
,
inputDataConfig
.
getDatasetId
());
System
.
out
.
format
(
"\t\tAnnotations Filter: %s\n"
,
inputDataConfig
.
getAnnotationsFilter
());
FractionSplit
fractionSplit
=
inputDataConfig
.
getFractionSplit
();
System
.
out
.
println
(
"\t\tFraction Split"
);
System
.
out
.
format
(
"\t\t\tTraining Fraction: %s\n"
,
fractionSplit
.
getTrainingFraction
());
System
.
out
.
format
(
"\t\t\tValidation Fraction: %s\n"
,
fractionSplit
.
getValidationFraction
());
System
.
out
.
format
(
"\t\t\tTest Fraction: %s\n"
,
fractionSplit
.
getTestFraction
());
FilterSplit
filterSplit
=
inputDataConfig
.
getFilterSplit
();
System
.
out
.
println
(
"\t\tFilter Split"
);
System
.
out
.
format
(
"\t\t\tTraining Filter: %s\n"
,
filterSplit
.
getTrainingFilter
());
System
.
out
.
format
(
"\t\t\tValidation Filter: %s\n"
,
filterSplit
.
getValidationFilter
());
System
.
out
.
format
(
"\t\t\tTest Filter: %s\n"
,
filterSplit
.
getTestFilter
());
PredefinedSplit
predefinedSplit
=
inputDataConfig
.
getPredefinedSplit
();
System
.
out
.
println
(
"\t\tPredefined Split"
);
System
.
out
.
format
(
"\t\t\tKey: %s\n"
,
predefinedSplit
.
getKey
());
TimestampSplit
timestampSplit
=
inputDataConfig
.
getTimestampSplit
();
System
.
out
.
println
(
"\t\tTimestamp Split"
);
System
.
out
.
format
(
"\t\t\tTraining Fraction: %s\n"
,
timestampSplit
.
getTrainingFraction
());
System
.
out
.
format
(
"\t\t\tTest Fraction: %s\n"
,
timestampSplit
.
getTestFraction
());
System
.
out
.
format
(
"\t\t\tValidation Fraction: %s\n"
,
timestampSplit
.
getValidationFraction
());
System
.
out
.
format
(
"\t\t\tKey: %s\n"
,
timestampSplit
.
getKey
());
Model
modelResponse
=
trainingPipelineResponse
.
getModelToUpload
();
System
.
out
.
println
(
"\t\tModel to upload"
);
System
.
out
.
format
(
"\t\tName: %s\n"
,
modelResponse
.
getName
());
System
.
out
.
format
(
"\t\tDisplay Name: %s\n"
,
modelResponse
.
getDisplayName
());
System
.
out
.
format
(
"\t\tDescription: %s\n"
,
modelResponse
.
getDescription
());
System
.
out
.
format
(
"\t\tMetadata Schema Uri: %s\n"
,
modelResponse
.
getMetadataSchemaUri
());
System
.
out
.
format
(
"\t\tMeta Data: %s\n"
,
modelResponse
.
getMetadata
());
System
.
out
.
format
(
"\t\tTraining Pipeline: %s\n"
,
modelResponse
.
getTrainingPipeline
());
System
.
out
.
format
(
"\t\tArtifact Uri: %s\n"
,
modelResponse
.
getArtifactUri
());
System
.
out
.
format
(
"\t\tSupported Deployment Resources Types: %s\n"
,
modelResponse
.
getSupportedDeploymentResourcesTypesList
().
toString
());
System
.
out
.
format
(
"\t\tSupported Input Storage Formats: %s\n"
,
modelResponse
.
getSupportedInputStorageFormatsList
().
toString
());
System
.
out
.
format
(
"\t\tSupported Output Storage Formats: %s\n"
,
modelResponse
.
getSupportedOutputStorageFormatsList
().
toString
());
System
.
out
.
format
(
"\t\tCreate Time: %s\n"
,
modelResponse
.
getCreateTime
());
System
.
out
.
format
(
"\t\tUpdate Time: %s\n"
,
modelResponse
.
getUpdateTime
());
System
.
out
.
format
(
"\t\tLabels: %s\n"
,
modelResponse
.
getLabelsMap
());
PredictSchemata
predictSchemata
=
modelResponse
.
getPredictSchemata
();
System
.
out
.
println
(
"\tPredict Schemata"
);
System
.
out
.
format
(
"\t\tInstance Schema Uri: %s\n"
,
predictSchemata
.
getInstanceSchemaUri
());
System
.
out
.
format
(
"\t\tParameters Schema Uri: %s\n"
,
predictSchemata
.
getParametersSchemaUri
());
System
.
out
.
format
(
"\t\tPrediction Schema Uri: %s\n"
,
predictSchemata
.
getPredictionSchemaUri
());
for
(
Model
.
ExportFormat
supportedExportFormat
:
modelResponse
.
getSupportedExportFormatsList
())
{
System
.
out
.
println
(
"\tSupported Export Format"
);
System
.
out
.
format
(
"\t\tId: %s\n"
,
supportedExportFormat
.
getId
());
}
ModelContainerSpec
containerSpec
=
modelResponse
.
getContainerSpec
();
System
.
out
.
println
(
"\tContainer Spec"
);
System
.
out
.
format
(
"\t\tImage Uri: %s\n"
,
containerSpec
.
getImageUri
());
System
.
out
.
format
(
"\t\tCommand: %s\n"
,
containerSpec
.
getCommandList
());
System
.
out
.
format
(
"\t\tArgs: %s\n"
,
containerSpec
.
getArgsList
());
System
.
out
.
format
(
"\t\tPredict Route: %s\n"
,
containerSpec
.
getPredictRoute
());
System
.
out
.
format
(
"\t\tHealth Route: %s\n"
,
containerSpec
.
getHealthRoute
());
for
(
EnvVar
envVar
:
containerSpec
.
getEnvList
())
{
System
.
out
.
println
(
"\t\tEnv"
);
System
.
out
.
format
(
"\t\t\tName: %s\n"
,
envVar
.
getName
());
System
.
out
.
format
(
"\t\t\tValue: %s\n"
,
envVar
.
getValue
());
}
for
(
Port
port
:
containerSpec
.
getPortsList
())
{
System
.
out
.
println
(
"\t\tPort"
);
System
.
out
.
format
(
"\t\t\tContainer Port: %s\n"
,
port
.
getContainerPort
());
}
for
(
DeployedModelRef
deployedModelRef
:
modelResponse
.
getDeployedModelsList
())
{
System
.
out
.
println
(
"\tDeployed Model"
);
System
.
out
.
format
(
"\t\tEndpoint: %s\n"
,
deployedModelRef
.
getEndpoint
());
System
.
out
.
format
(
"\t\tDeployed Model Id: %s\n"
,
deployedModelRef
.
getDeployedModelId
());
}
Status
status
=
trainingPipelineResponse
.
getError
();
System
.
out
.
println
(
"\tError"
);
System
.
out
.
format
(
"\t\tCode: %s\n"
,
status
.
getCode
());
System
.
out
.
format
(
"\t\tMessage: %s\n"
,
status
.
getMessage
());
}
}
}
Node.js
Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js 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 .
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const trainingPipelineId = 'YOUR_MODEL_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// Imports the Google Cloud Model Service Client library
const
{
PipelineServiceClient
}
=
require
(
' @google-cloud/aiplatform
'
);
// Specifies the location of the api endpoint
const
clientOptions
=
{
apiEndpoint
:
'us-central1-aiplatform.googleapis.com'
,
};
// Instantiates a client
const
pipelineServiceClient
=
new
PipelineServiceClient
(
clientOptions
);
async
function
getTrainingPipeline
()
{
// Configure the parent resource
const
name
=
pipelineServiceClient
.
trainingPipelinePath
(
project
,
location
,
trainingPipelineId
);
const
request
=
{
name
,
};
// Get and print out a list of all the endpoints for this resource
const
[
response
]
=
await
pipelineServiceClient
.
getTrainingPipeline
(
request
);
console
.
log
(
'Get training pipeline response'
);
console
.
log
(
`\tTraining pipeline name:
${
response
.
displayName
}
`
);
console
.
log
(
`\tTraining pipeline state:
${
response
.
state
}
`
);
}
getTrainingPipeline
();
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
def
get_training_pipeline_sample
(
project
:
str
,
training_pipeline_id
:
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
)
name
=
client
.
training_pipeline_path
(
project
=
project
,
location
=
location
,
training_pipeline
=
training_pipeline_id
)
response
=
client
.
get_training_pipeline
(
name
=
name
)
print
(
"response:"
,
response
)
What's next
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