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authoredJan 8, 2021
Update deployment.md
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‎docs/en/tutorials/deployment.md

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To infer and deploy a model, we need export an inference model, or called to_static: `convert dygraph model to static model`, at first.
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```python
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python3 tools/export_model.py -c config_file -o output_path -p params_file
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python3.7 tools/export_model.py -c config_file -o output_path -p params_file
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```
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Note: In `export_model.py`, It will build a model again, and then loading the prarams. But some init params in the infer phase is different from the train phase.
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PaddleVideo supports a test script to test the exported model.
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```python
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python3.7 tools/test_export_model.py -p params_file -i inference_folder -c config_file
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```
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PaddleVideo supports ```tools/predict.py``` to infer
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```python
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python3.7 tools/predict.py -v example.avi --model_file "./inference/example.pdmodel" --params_file "./inference/example.pdiparams" --enable_benchmark=False --model="example" --num_seg=8
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python3.7 tools/predict.py -v example.avi --model_file "./inference/example.pdmodel" --params_file "./inference/example.pdiparams" --enable_benchmark=False --model="example" --num_seg=8
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```
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## How to test inference speed?
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PaddleVideo support a script to test inference speed
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```
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```python
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python3.7 tools/predict.py --enable_benchmark=True --model_file=模型文件 --params_file=参数文件
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```
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## How to use C++ infer?

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