Python

defcreate_training_pipeline_custom_job_sample(project:str,location:str,staging_bucket:str,display_name:str,script_path:str,container_uri:str,model_serving_container_image_uri:str,dataset_id:Optional[str]=None,model_display_name:Optional[str]=None,args:Optional[List[Union[str, float, int]]]=None,replica_count:int=0,machine_type:str="n1-standard-4",accelerator_type:str="ACCELERATOR_TYPE_UNSPECIFIED",accelerator_count:int=0,training_fraction_split:float=0.8,validation_fraction_split:float=0.1,test_fraction_split:float=0.1,sync:bool=True,tensorboard_resource_name:Optional[str]=None,service_account:Optional[str]=None,):aiplatform.init(project=project,location=location,staging_bucket=staging_bucket)job=aiplatform.CustomTrainingJob(display_name=display_name,script_path=script_path,container_uri=container_uri,model_serving_container_image_uri=model_serving_container_image_uri,)#ThisexampleusesanImageDataset,butyoucanuseanothertypedataset=aiplatform.ImageDataset(dataset_id)ifdataset_idelseNonemodel=job.run(dataset=dataset,model_display_name=model_display_name,args=args,replica_count=replica_count,machine_type=machine_type,accelerator_type=accelerator_type,accelerator_count=accelerator_count,training_fraction_split=training_fraction_split,validation_fraction_split=validation_fraction_split,test_fraction_split=test_fraction_split,sync=sync,tensorboard=tensorboard_resource_name,service_account=service_account,)model.wait()print(model.display_name)print(model.resource_name)print(model.uri)returnmodel

• project: . You can find these IDs in the Google Cloud consolewelcome page.
  
• location: The location to run the CustomJob in. This should be the same location as the provided TensorBoard instance.
• staging_bucket: The Cloud Storage bucket to stage artifacts during API calls, including TensorBoard logs.
• display_name: Display name of the custom training job.
• script_path: The path, relative to the working directory on your local file system, to the script that is the entry point for your training code.
• container_uri: The URI of the training container image can be Vertex AI.prebuilt training container or acustom container.
• model_serving_container_image_uri: The URI of the model serving container suitable for serving the model produced by the training script.
• dataset_id: The ID number for the dataset to use for training.
• model_display_name: Display name of the trained model.
• args: Command line arguments to be passed to the Python script.
• replica_count: The number of worker replicas to use. In most cases, set this to 1 for yourfirst worker pool.
• machine_type: The type of VM to use. For a list of supported VMs, seeMachine types
• accelerator_type: The type of GPU to attach to each VM in the resource pool. For a list of supported GPUs, seeGPUs.
• accelerator_count The number of GPUs to attach to each VM in the resource pool. The default the value is1.
• training_fraction_split: The fraction of the dataset to use to train your model.
• validation_fraction_split: The fraction of the dataset to use to validate your model.
• test_fraction_split: The fraction of the dataset to use to evaluate your model.
• sync: Whether to execute this method synchronously.
• tensorboard_resource_name: The resource name of the Vertex TensorBoard instance to which CustomJob will upload TensorBoard logs.
• service_account: Required when running with TensorBoard. SeeCreate a service account with required permissions.

curl (Linux, macOS, or Cloud Shell)

Save the request body in a file namedrequest.json. Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json<< 'EOF'{"displayName":JOB_NAME,"jobSpec":{"workerPoolSpecs":[  {    "replicaCount": "1",     "machineSpec": {        "machineType": "n1-standard-8",      },      "containerSpec": {        "imageUri":TRAINING_CONTAINER,      }    }  ],    "base_output_directory": {  "output_uri_prefix":BASE_OUTPUT_DIR,   },  "serviceAccount":USER_SA_EMAIL,  "tensorboard":TENSORBOARD_INSTANCE_NAME,  }}EOF

Then execute the following command to send your REST request:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/customJobs"

PowerShell (Windows)

Save the request body in a file namedrequest.json. Run the following command in the terminal to create or overwrite this file in the current directory:

@'{"displayName":JOB_NAME,"jobSpec":{"workerPoolSpecs":[  {    "replicaCount": "1",     "machineSpec": {        "machineType": "n1-standard-8",      },      "containerSpec": {        "imageUri":TRAINING_CONTAINER,      }    }  ],    "base_output_directory": {  "output_uri_prefix":BASE_OUTPUT_DIR,   },  "serviceAccount":USER_SA_EMAIL,  "tensorboard":TENSORBOARD_INSTANCE_NAME,  }}'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/customJobs" | Select-Object -Expand Content