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。

# 從部署的服務 (Amazon SageMaker SDK) 請求推論
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使用下列程式碼範例，根據您用來訓練模型的架構，從部署的服務請求推論。不同架構的程式碼範例都很類似。主要差異在於 TensorFlow 需要 `application/json` 做為內容類型。

 

## PyTorch 和 MXNet
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 如果您正在使用 **PyTorch 1.4 版或更新版本**，或使用 **MXNet 1.7.0 或更新版本**，而且您具備一個 Amazon SageMaker AI 端點 `InService`，就可以使用適用於 Python 的 SageMaker AI SDK `predictor` 套件提出推論請求。

**注意**  
API 會根據適用於 Python 版本的 SageMaker AI SDK 而有所不同：  
針對 1.x 版，請使用 [https://sagemaker.readthedocs.io/en/v1.72.0/api/inference/predictors.html#sagemaker.predictor.RealTimePredictor](https://sagemaker.readthedocs.io/en/v1.72.0/api/inference/predictors.html#sagemaker.predictor.RealTimePredictor) 和 [https://sagemaker.readthedocs.io/en/v1.72.0/api/inference/predictors.html#sagemaker.predictor.RealTimePredictor.predict](https://sagemaker.readthedocs.io/en/v1.72.0/api/inference/predictors.html#sagemaker.predictor.RealTimePredictor.predict) API。
針對 2.x 版，請使用 [https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor) 和 [https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor.predict](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor.predict) API。

下列程式碼範例會示範如何使用這些 API 傳送映像進行推論：

------
#### [ SageMaker Python SDK v1.x ]

```
from sagemaker.predictor import RealTimePredictor

endpoint = 'insert name of your endpoint here'

# Read image into memory
payload = None
with open("image.jpg", 'rb') as f:
    payload = f.read()

predictor = RealTimePredictor(endpoint=endpoint, content_type='application/x-image')
inference_response = predictor.predict(data=payload)
print (inference_response)
```

------
#### [ SageMaker Python SDK v2.x ]

```
from sagemaker.predictor import Predictor

endpoint = 'insert name of your endpoint here'

# Read image into memory
payload = None
with open("image.jpg", 'rb') as f:
    payload = f.read()
    
predictor = Predictor(endpoint)
inference_response = predictor.predict(data=payload)
print (inference_response)
```

------

## TensorFlow
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下列程式碼範例會示範如何使用 SageMaker Python SDK API 傳送映像進行推論：

```
from sagemaker.predictor import Predictor
from PIL import Image
import numpy as np
import json

endpoint = 'insert the name of your endpoint here'

# Read image into memory
image = Image.open(input_file)
batch_size = 1
image = np.asarray(image.resize((224, 224)))
image = image / 128 - 1
image = np.concatenate([image[np.newaxis, :, :]] * batch_size)
body = json.dumps({"instances": image.tolist()})
    
predictor = Predictor(endpoint)
inference_response = predictor.predict(data=body)
print(inference_response)
```