本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。 # 使用 Neptune ML 训练模型 处理完从 Neptune 导出的用于模型训练的数据后，可以使用如下命令启动模型训练作业： ------ #### [ 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" ``` 有关更多信息，请参阅《 AWS CLI 命令参考》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** 此示例假设您的 AWS 证书是在您的环境中配置的。{{us-east-1}}替换为 Neptune 集群的区域。 ------ #### [ 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" }' ``` ------ [modeltraining 命令](machine-learning-api-modeltraining.md)中解释了如何使用此命令的详细信息，以及有关如何获取正在运行的任务的状态、如何停止正在运行的任务以及如何列出所有正在运行的任务的信息。 您还可以提供 `previousModelTrainingJobId` 以使用已完成的 Neptune ML 模型训练任务中的信息，从而在新的训练任务中加快超参数搜索的速度。这在[对新的图形数据进行模型再训练](machine-learning-overview-evolving-data-incremental.md#machine-learning-overview-model-retraining)以及[对相同图形数据进行增量训练](machine-learning-overview-evolving-data-incremental.md#machine-learning-overview-incremental)期间非常有用。使用如下命令： ------ #### [ 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)}}" ``` 有关更多信息，请参阅《 AWS CLI 命令参考》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** 此示例假设您的 AWS 证书是在您的环境中配置的。{{us-east-1}}替换为 Neptune 集群的区域。 ------ #### [ 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)}}" }' ``` ------ 您可以通过提供 `customModelTrainingParameters` 对象在 Neptune ML 训练基础设施上训练自己的模型实现，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)}}" }' ``` 有关更多信息，请参阅《 AWS CLI 命令参考》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** 此示例假设您的 AWS 证书是在您的环境中配置的。{{us-east-1}}替换为 Neptune 集群的区域。 ------ #### [ 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)}}" } }' ``` ------ 有关更多信息，例如如何获取正在运行的任务的状态、如何停止正在运行的任务以及如何列出所有正在运行的任务，请参阅[modeltraining 命令](machine-learning-api-modeltraining.md)。有关如何实现和使用自定义模型的信息，请参阅[Neptune ML 中的自定义模型](machine-learning-custom-models.md)。 **Topics** + [Amazon Neptune ML 中的模型和模型训练](machine-learning-models-and-training.md) + [在 Neptune ML 中自定义模型超参数配置](machine-learning-customizing-hyperparams.md) + [模型训练最佳实践](machine-learning-improve-model-performance.md)