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使用 Neptune ML 訓練模型 - Amazon Neptune

本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

使用 Neptune ML 訓練模型

處理從 Neptune 匯出用於模型訓練的資料之後，您可以使用如下所示的命令啟動模型訓練任務：

AWS CLI
aws neptunedata start-ml-model-training-job \
  --endpoint-url https://your-neptune-endpoint:port \
  --id "(a unique model-training job ID)" \
  --data-processing-job-id "(the data-processing job-id of a completed job)" \
  --train-model-s3-location "s3://(your S3 bucket)/neptune-model-graph-autotrainer"

如需詳細資訊，請參閱《 AWS CLI 命令參考》中的 start-ml-model-training-job。

SDK
import boto3
from botocore.config import Config

client = boto3.client(
    'neptunedata',
    endpoint_url='https://your-neptune-endpoint:port',
    config=Config(read_timeout=None, retries={'total_max_attempts': 1})
)

response = client.start_ml_model_training_job(
    id='(a unique model-training job ID)',
    dataProcessingJobId='(the data-processing job-id of a completed job)',
    trainModelS3Location='s3://(your S3 bucket)/neptune-model-graph-autotrainer'
)

print(response)
awscurl
awscurl https://your-neptune-endpoint:port/ml/modeltraining \
  --region us-east-1 \
  --service neptune-db \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer"
      }'
注意

此範例假設您的 AWS 登入資料已在您的環境中設定。將 us-east-1 取代為 Neptune 叢集的區域。

curl
curl \
  -X POST https://your-neptune-endpoint:port/ml/modeltraining \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer"
      }'

如何使用此命令的詳細資訊會在 modeltraining 指令 中加以說明，伴隨如何取得執行中工作狀態、如何停止執行中工作，以及如何列出所有執行中工作的相關資訊。

您也可以提供一個 previousModelTrainingJobId，使用來自已完成 Neptune ML 模型訓練工作的資訊，以在新的訓練工作中加速超參數搜尋。在對新圖形資料進行模型重新訓練期間，以及在對相同圖形資料進行增量訓練期間，這樣做很有用。使用像這樣的命令：

AWS CLI
aws neptunedata start-ml-model-training-job \
  --endpoint-url https://your-neptune-endpoint:port \
  --id "(a unique model-training job ID)" \
  --data-processing-job-id "(the data-processing job-id of a completed job)" \
  --train-model-s3-location "s3://(your S3 bucket)/neptune-model-graph-autotrainer" \
  --previous-model-training-job-id "(the model-training job-id of a completed job)"

如需詳細資訊，請參閱《 AWS CLI 命令參考》中的 start-ml-model-training-job。

SDK
import boto3
from botocore.config import Config

client = boto3.client(
    'neptunedata',
    endpoint_url='https://your-neptune-endpoint:port',
    config=Config(read_timeout=None, retries={'total_max_attempts': 1})
)

response = client.start_ml_model_training_job(
    id='(a unique model-training job ID)',
    dataProcessingJobId='(the data-processing job-id of a completed job)',
    trainModelS3Location='s3://(your S3 bucket)/neptune-model-graph-autotrainer',
    previousModelTrainingJobId='(the model-training job-id of a completed job)'
)

print(response)
awscurl
awscurl https://your-neptune-endpoint:port/ml/modeltraining \
  --region us-east-1 \
  --service neptune-db \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer",
        "previousModelTrainingJobId" : "(the model-training job-id of a completed job)"
      }'
注意

此範例假設您的 AWS 登入資料已在您的環境中設定。將 us-east-1 取代為 Neptune 叢集的區域。

curl
curl \
  -X POST https://your-neptune-endpoint:port/ml/modeltraining \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your S3 bucket)/neptune-model-graph-autotrainer",
        "previousModelTrainingJobId" : "(the model-training job-id of a completed job)"
      }'

您可以提供 customModelTrainingParameters 物件，在 Neptune ML 訓練基礎結構上訓練自己的模型實作，如下所示：

AWS CLI
aws neptunedata start-ml-model-training-job \
  --endpoint-url https://your-neptune-endpoint:port \
  --id "(a unique model-training job ID)" \
  --data-processing-job-id "(the data-processing job-id of a completed job)" \
  --train-model-s3-location "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer" \
  --model-name "custom" \
  --custom-model-training-parameters '{
    "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)",
    "trainingEntryPointScript": "(your training script entry-point name in the Python module)",
    "transformEntryPointScript": "(your transform script entry-point name in the Python module)"
  }'

如需詳細資訊，請參閱《 AWS CLI 命令參考》中的 start-ml-model-training-job。

SDK
import boto3
from botocore.config import Config

client = boto3.client(
    'neptunedata',
    endpoint_url='https://your-neptune-endpoint:port',
    config=Config(read_timeout=None, retries={'total_max_attempts': 1})
)

response = client.start_ml_model_training_job(
    id='(a unique model-training job ID)',
    dataProcessingJobId='(the data-processing job-id of a completed job)',
    trainModelS3Location='s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer',
    modelName='custom',
    customModelTrainingParameters={
        'sourceS3DirectoryPath': 's3://(your Amazon S3 bucket)/(path to your Python module)',
        'trainingEntryPointScript': '(your training script entry-point name in the Python module)',
        'transformEntryPointScript': '(your transform script entry-point name in the Python module)'
    }
)

print(response)
awscurl
awscurl https://your-neptune-endpoint:port/ml/modeltraining \
  --region us-east-1 \
  --service neptune-db \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer",
        "modelName": "custom",
        "customModelTrainingParameters" : {
          "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)",
          "trainingEntryPointScript": "(your training script entry-point name in the Python module)",
          "transformEntryPointScript": "(your transform script entry-point name in the Python module)"
        }
      }'
注意

此範例假設您的 AWS 登入資料已在您的環境中設定。將 us-east-1 取代為 Neptune 叢集的區域。

curl
curl \
  -X POST https://your-neptune-endpoint:port/ml/modeltraining \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer",
        "modelName": "custom",
        "customModelTrainingParameters" : {
          "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)",
          "trainingEntryPointScript": "(your training script entry-point name in the Python module)",
          "transformEntryPointScript": "(your transform script entry-point name in the Python module)"
        }
      }'

如需詳細資訊，例如有關如何取得執行中工作的狀態、如何停止執行中工作，以及如何列出所有執行中工作，請參閱 modeltraining 指令。如需如何實作並使用自訂模型的相關資訊，請參閱 Neptune ML 中的自訂模型。

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