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](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-training-job.html)。 ------ #### [ 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](machine-learning-api-modeltraining.md) 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](machine-learning-overview-evolving-data-incremental.md#machine-learning-overview-model-retraining)期間，以及在[對相同圖形資料進行增量訓練](machine-learning-overview-evolving-data-incremental.md#machine-learning-overview-incremental)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](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-training-job.html)。 ------ #### [ 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](https://docs.aws.amazon.com/cli/latest/reference/neptunedata/start-ml-model-training-job.html)。 ------ #### [ 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](machine-learning-api-modeltraining.md)。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](machine-learning-custom-models.md)。 **Topics** + [Amazon Neptune ML 中的模型和模型訓練](machine-learning-models-and-training.md) + [在 Neptune ML 中自訂模型超參數組態](machine-learning-customizing-hyperparams.md) + [模型訓練最佳實務](machine-learning-improve-model-performance.md)