# Inference
[](https://gitee.com/mindspore/docs/blob/r1.6/docs/mindspore/faq/source_en/inference.md)
**Q: MindSpore 1.3 is installed on the Ascend 310 hardware platform. When I run the `add_model.py` sample in mindspore_serving, an error message is displayed. Why?**
A: Ascend 310 supports model export and Serving inference, but does not support direct inference using the MindSpore frontend Python script. In the `add` sample, the code for direct inference using the MindSpore frontend Python script is added. You only need to comment out the code in the Ascend 310 scenario.
```python
def export_net():
"""Export add net of 2x2 + 2x2, and copy output model `tensor_add.mindir` to directory ../add/1"""
x = np.ones([2, 2]).astype(np.float32)
y = np.ones([2, 2]).astype(np.float32)
add = Net()
# Comment out the MindSpore frontend Python script used for direct inference in the Ascend 310 scenario.
# output = add(ms.Tensor(x), ms.Tensor(y))
ms.export(add, ms.Tensor(x), ms.Tensor(y), file_name='tensor_add', file_format='MINDIR')
dst_dir = '../add/1'
try:
os.mkdir(dst_dir)
except OSError:
pass
dst_file = os.path.join(dst_dir, 'tensor_add.mindir')
copyfile('tensor_add.mindir', dst_file)
print("copy tensor_add.mindir to " + dst_dir + " success")
print(x)
print(y)
# print(output.asnumpy()).
```
**Q: What should I do when error `/usr/bin/ld: warning: libxxx.so, needed by libmindspore.so, not found` prompts during application compiling?**
A: Find the directory where the missing dynamic library file is located, add the path to the environment variable `LD_LIBRARY_PATH`, and refer to [Inference Using the MindIR Model on Ascend 310 AI Processors#Building Inference Code](https://www.mindspore.cn/docs/programming_guide/en/r1.6/multi_platform_inference_ascend_310_mindir.html#building-inference-code) for environment variable settings.
**Q: What should I do when error `error while loading shared libraries: libge_compiler.so: cannot open shared object file: No such file or directory` prompts during application running?**
A: While installing Ascend 310 AI Processor software packages，the `CANN` package should install the full-featured `toolkit` version instead of the `nnrt` version.
**Q: How to set high-precision or high-performance mode when performing inference on Ascend 310 AI Processor?**
A: Set in the inference code through the SetPrecisionMode interface of Ascend310DeviceInfo. Optional: force_fp16，allow_fp32_to_fp16，must_keep_origin_dtype，allow_mix_precision. The default value is force_fp16, which refers to the high-performance mode. High precision mode can be set to allow_fp32_to_fp16 or must_keep_origin_dtype.
**Q: How to configure AIPP files?**
A: AIPP (artistic intelligence pre-processing) AI preprocessing is used to complete image preprocessing on AI core, including changing image size, color gamut conversion (converting image format), subtracting mean / multiplication coefficient (changing image pixels), and real-time inference after data processing. The related configuration introduction is complex. Please refer to [AIPP enable chapter of ATC tool](https://support.huaweicloud.com/atctool-cann502alpha3infer/atlasatc_16_0015.html)
**Q: How to set the log level in the inferenct process of Ascend 310 AI Processor?**
A: Use ASCEND_GLOBAL_LOG_LEVEL to set log level, 0: debug level; 1: Info level; 2: Warning level; 3: Error level; 4: Null level, no log output; Other values are illegal. Configuration example: export ASCEND_GLOBAL_LOG_LEVEL=1. If there are errors in the inference process, you can modify the log level to obtain more detailed log information.