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# 创建要查询的推理端点
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推理端点允许您查询模型训练过程构造的一个特定模型。端点附加到训练过程能够生成的给定类型中性能最佳的模型。然后，端点能够接受来自 Neptune 的 Gremlin 查询，并返回该模型对查询中输入的预测。创建推理端点后，它会一直处于活动状态，直到您将其删除。

## 管理 Neptune ML 的推理端点
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对从 Neptune 导出的数据完成模型训练后，可以使用如下命令创建推理端点：

------
#### [ Amazon CLI ]

```
aws neptunedata create-ml-endpoint \
  --endpoint-url https://{{your-neptune-endpoint}}:{{port}} \
  --id "{{(a unique ID for the new endpoint)}}" \
  --ml-model-training-job-id "{{(the model-training job-id of a completed job)}}"
```

有关更多信息，请参阅《 Amazon CLI 命令参考》[create-ml-endpoint](https://docs.amazonaws.cn/cli/latest/reference/neptunedata/create-ml-endpoint.html)中的。

------
#### [ 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.create_ml_endpoint(
    id='{{(a unique ID for the new endpoint)}}',
    mlModelTrainingJobId='{{(the model-training job-id of a completed job)}}'
)

print(response)
```

------
#### [ awscurl ]

```
awscurl https://{{your-neptune-endpoint}}:{{port}}/ml/endpoints \
  --region {{us-east-1}} \
  --service neptune-db \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "{{(a unique ID for the new endpoint)}}",
        "mlModelTrainingJobId": "{{(the model-training job-id of a completed job)}}"
      }'
```

**注意**  
此示例假设您的 Amazon 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/endpoints \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "{{(a unique ID for the new endpoint)}}",
        "mlModelTrainingJobId": "{{(the model-training job-id of a completed job)}}"
      }'
```

------

您也可以通过已完成的模型转换任务创建的模型创建推理端点，方法大致相同：

------
#### [ Amazon CLI ]

```
aws neptunedata create-ml-endpoint \
  --endpoint-url https://{{your-neptune-endpoint}}:{{port}} \
  --id "{{(a unique ID for the new endpoint)}}" \
  --ml-model-transform-job-id "{{(the model-transform job-id of a completed job)}}"
```

有关更多信息，请参阅《 Amazon CLI 命令参考》[create-ml-endpoint](https://docs.amazonaws.cn/cli/latest/reference/neptunedata/create-ml-endpoint.html)中的。

------
#### [ 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.create_ml_endpoint(
    id='{{(a unique ID for the new endpoint)}}',
    mlModelTransformJobId='{{(the model-transform job-id of a completed job)}}'
)

print(response)
```

------
#### [ awscurl ]

```
awscurl https://{{your-neptune-endpoint}}:{{port}}/ml/endpoints \
  --region {{us-east-1}} \
  --service neptune-db \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "{{(a unique ID for the new endpoint)}}",
        "mlModelTransformJobId": "{{(the model-transform job-id of a completed job)}}"
      }'
```

**注意**  
此示例假设您的 Amazon 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/endpoints \
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "{{(a unique ID for the new endpoint)}}",
        "mlModelTransformJobId": "{{(the model-transform job-id of a completed job)}}"
      }'
```

------

[endpoints 命令](machine-learning-api-endpoints.md)中解释了如何使用这些命令的详细信息，以及有关如何获取端点状态、如何删除端点以及如何列出所有推理端点的信息。