使用 create_custom_job 方法创建自定义作业。
深入探索
如需查看包含此代码示例的详细文档，请参阅以下内容：
代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
import com.google.cloud.aiplatform.v1.AcceleratorType;
import com.google.cloud.aiplatform.v1.ContainerSpec;
import com.google.cloud.aiplatform.v1.CustomJob;
import com.google.cloud.aiplatform.v1.CustomJobSpec;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.MachineSpec;
import com.google.cloud.aiplatform.v1.WorkerPoolSpec;
import java.io.IOException;
// Create a custom job to run machine learning training code in Vertex AI
public class CreateCustomJobSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "PROJECT";
String displayName = "DISPLAY_NAME";
// Vertex AI runs your training application in a Docker container image. A Docker container
// image is a self-contained software package that includes code and all dependencies. Learn
// more about preparing your training application at
// https://cloud.google.com/vertex-ai/docs/training/overview#prepare_your_training_application
String containerImageUri = "CONTAINER_IMAGE_URI";
createCustomJobSample(project, displayName, containerImageUri);
}
static void createCustomJobSample(String project, String displayName, String containerImageUri)
throws IOException {
JobServiceSettings settings =
JobServiceSettings.newBuilder()
.setEndpoint("us-central1-aiplatform.googleapis.com:443")
.build();
String location = "us-central1";
// 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 (JobServiceClient client = JobServiceClient.create(settings)) {
MachineSpec machineSpec =
MachineSpec.newBuilder()
.setMachineType("n1-standard-4")
.setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80)
.setAcceleratorCount(1)
.build();
ContainerSpec containerSpec =
ContainerSpec.newBuilder().setImageUri(containerImageUri).build();
WorkerPoolSpec workerPoolSpec =
WorkerPoolSpec.newBuilder()
.setMachineSpec(machineSpec)
.setReplicaCount(1)
.setContainerSpec(containerSpec)
.build();
CustomJobSpec customJobSpecJobSpec =
CustomJobSpec.newBuilder().addWorkerPoolSpecs(workerPoolSpec).build();
CustomJob customJob =
CustomJob.newBuilder()
.setDisplayName(displayName)
.setJobSpec(customJobSpecJobSpec)
.build();
LocationName parent = LocationName.of(project, location);
CustomJob response = client.createCustomJob(parent, customJob);
System.out.format("response: %s\n", response);
System.out.format("Name: %s\n", response.getName());
}
}
}
Node.js
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const customJobDisplayName = 'YOUR_CUSTOM_JOB_DISPLAY_NAME';
// const containerImageUri = 'YOUR_CONTAINER_IMAGE_URI';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// Imports the Google Cloud Job Service Client library
const {JobServiceClient} = require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const jobServiceClient = new JobServiceClient(clientOptions);
async function createCustomJob() {
// Configure the parent resource
const parent = `projects/${project}/locations/${location}`;
const customJob = {
displayName: customJobDisplayName,
jobSpec: {
workerPoolSpecs: [
{
machineSpec: {
machineType: 'n1-standard-4',
acceleratorType: 'NVIDIA_TESLA_K80',
acceleratorCount: 1,
},
replicaCount: 1,
containerSpec: {
imageUri: containerImageUri,
command: [],
args: [],
},
},
],
},
};
const request = {parent, customJob};
// Create custom job request
const [response] = await jobServiceClient.createCustomJob(request);
console.log('Create custom job response:\n', JSON.stringify(response));
}
createCustomJob();Python
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
from google.cloud import aiplatform
def create_custom_job_sample(
project: str,
display_name: str,
container_image_uri: 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.JobServiceClient(client_options=client_options)
custom_job = {
"display_name": display_name,
"job_spec": {
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": container_image_uri,
"command": [],
"args": [],
},
}
]
},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_custom_job(parent=parent, custom_job=custom_job)
print("response:", response)
后续步骤
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