Tune language foundation models with a tuning dataset.
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. CreatePipelineJobRequest
;
import
com.google.cloud.aiplatform.v1. LocationName
;
import
com.google.cloud.aiplatform.v1. PipelineJob
;
import
com.google.cloud.aiplatform.v1. PipelineJob
. RuntimeConfig
;
import
com.google.cloud.aiplatform.v1. PipelineServiceClient
;
import
com.google.cloud.aiplatform.v1. PipelineServiceSettings
;
import
com.google.protobuf. Value
;
import
java.io.IOException
;
import
java.util.HashMap
;
import
java.util.Map
;
public
class
CreatePipelineJobModelTuningSample
{
public
static
void
main
(
String
[]
args
)
throws
IOException
{
// TODO(developer): Replace these variables before running the sample.
String
project
=
"PROJECT"
;
String
location
=
"europe-west4"
;
// europe-west4 and us-central1 are the supported regions
String
pipelineJobDisplayName
=
"PIPELINE_JOB_DISPLAY_NAME"
;
String
modelDisplayName
=
"MODEL_DISPLAY_NAME"
;
String
outputDir
=
"OUTPUT_DIR"
;
String
datasetUri
=
"DATASET_URI"
;
int
trainingSteps
=
300
;
createPipelineJobModelTuningSample
(
project
,
location
,
pipelineJobDisplayName
,
modelDisplayName
,
outputDir
,
datasetUri
,
trainingSteps
);
}
// Create a model tuning job
public
static
void
createPipelineJobModelTuningSample
(
String
project
,
String
location
,
String
pipelineJobDisplayName
,
String
modelDisplayName
,
String
outputDir
,
String
datasetUri
,
int
trainingSteps
)
throws
IOException
{
final
String
endpoint
=
String
.
format
(
"%s-aiplatform.googleapis.com:443"
,
location
);
PipelineServiceSettings
pipelineServiceSettings
=
PipelineServiceSettings
.
newBuilder
().
setEndpoint
(
endpoint
).
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.
try
(
PipelineServiceClient
client
=
PipelineServiceClient
.
create
(
pipelineServiceSettings
))
{
Map<String
,
Value
>
parameterValues
=
new
HashMap
<> ();
parameterValues
.
put
(
"project"
,
stringToValue
(
project
));
parameterValues
.
put
(
"model_display_name"
,
stringToValue
(
modelDisplayName
));
parameterValues
.
put
(
"dataset_uri"
,
stringToValue
(
datasetUri
));
parameterValues
.
put
(
"location"
,
stringToValue
(
"us-central1"
));
// Deployment is only supported in us-central1 for Public Preview
parameterValues
.
put
(
"large_model_reference"
,
stringToValue
(
"text-bison@001"
));
parameterValues
.
put
(
"train_steps"
,
numberToValue
(
trainingSteps
));
parameterValues
.
put
(
"accelerator_type"
,
stringToValue
(
"GPU"
));
// Optional: GPU or TPU
RuntimeConfig
runtimeConfig
=
RuntimeConfig
.
newBuilder
()
.
setGcsOutputDirectory
(
outputDir
)
.
putAllParameterValues
(
parameterValues
)
.
build
();
PipelineJob
pipelineJob
=
PipelineJob
.
newBuilder
()
.
setTemplateUri
(
"https://us-kfp.pkg.dev/ml-pipeline/large-language-model-pipelines/tune-large-model/v2.0.0"
)
.
setDisplayName
(
pipelineJobDisplayName
)
.
setRuntimeConfig
(
runtimeConfig
)
.
build
();
LocationName
parent
=
LocationName
.
of
(
project
,
location
);
CreatePipelineJobRequest
request
=
CreatePipelineJobRequest
.
newBuilder
()
.
setParent
(
parent
.
toString
())
.
setPipelineJob
(
pipelineJob
)
.
build
();
PipelineJob
response
=
client
.
createPipelineJob
(
request
);
System
.
out
.
format
(
"response: %s\n"
,
response
);
System
.
out
.
format
(
"Name: %s\n"
,
response
.
getName
());
}
}
static
Value
stringToValue
(
String
str
)
{
return
Value
.
newBuilder
().
setStringValue
(
str
).
build
();
}
static
Value
numberToValue
(
int
n
)
{
return
Value
.
newBuilder
().
setNumberValue
(
n
).
build
();
}
}
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