@@ -266,27 +266,7 @@ Run your scripts on ModelArts. For details, see [Using MindSpore on Cloud](https
### Inference Phase
Models trained on the Ascend 910 AI processor can be used for inference on different hardware platforms.
1. Inference on the Ascend 910 AI processor
Similar to the `estimator.evaluate()` API of TensorFlow, MindSpore provides the `model.eval()` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see <https://gitee.com/mindspore/mindspore/blob/master/example/resnet50_cifar10/eval.py>.
```python
res=model.eval(dataset)
```
2. Inference on the Ascend 310 AI processor
1. Export the ONNX or GEIR model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
2. For performing inference in the cloud environment, see the [Ascend 910 training and Ascend 310 inference samples](https://support.huaweicloud.com/bestpractice-modelarts/modelarts_10_0026.html). For details about the bare-metal environment (compared with the cloud environment where the Ascend 310 AI processor is deployed locally), see the description document of the Ascend 310 AI processor software package.
3. Inference on a GPU
1. Export the ONNX model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
2. Perform inference on the NVIDIA GPU by referring to [TensorRT backend for ONNX](https://github.com/onnx/onnx-tensorrt).
Models trained on the Ascend 910 AI processor can be used for inference on different hardware platforms. Refer to the [Multi-platform Inference Tutorial](https://www.mindspore.cn/tutorial/en/master/use/multi_platform_inference.html) for detailed steps.
Models based on MindSpore training can be used for inference on different hardware platforms. This document introduces the inference process on each platform.
1. Inference on the Ascend 910 AI processor
MindSpore provides the `model.eval()` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see <https://gitee.com/mindspore/mindspore/blob/master/example/resnet50_cifar10/eval.py>.
```python
res=model.eval(dataset)
```
In addition, the` model.predict ()` interface can be used for inference. For detailed usage, please refer to API description.
2. Inference on the Ascend 310 AI processor
1. Export the ONNX or GEIR model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
2. For performing inference in the cloud environment, see the [Ascend 910 training and Ascend 310 inference samples](https://support.huaweicloud.com/bestpractice-modelarts/modelarts_10_0026.html). For details about the bare-metal environment (compared with the cloud environment where the Ascend 310 AI processor is deployed locally), see the description document of the Ascend 310 AI processor software package.
3. Inference on a GPU
1. Export the ONNX model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
2. Perform inference on the NVIDIA GPU by referring to [TensorRT backend for ONNX](https://github.com/onnx/onnx-tensorrt).
## On-Device Inference
The On-Device Inference is based on the MindSpore Predict. Please refer to [On-Device Inference Tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/on_device_inference.html) for details.
