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apache · Jul 4, 2019 · 93837aa · 93837aa
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 ## Inference with AMP
 
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 To do inference with mixed precision for a trained model in FP32, you can use the conversion APIs: `amp.convert_model` for symbolic model and `amp.convert_hybrid_block` for gluon models. The conversion APIs will take the FP32 model as input and will return a mixed precision model, which can be used to run inference.
 Below, we demonstrate for a gluon model and a symbolic model:
 - Conversion from FP32 model to mixed precision model.
 - Run inference on the mixed precision model.
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-To do inference with mixed precision for a trained model in FP32, you can use the conversion APIs: `amp.convert_model` for symbolic model and `amp.convert_hybrid_block` for gluon models. The conversion APIs will take the FP32 model as input and will return a mixed precision model, which can be used to run inference. Below, we demonstrate for a gluon model and a symbolic model: 1. Conversion from FP32 model to mixed precision model 2. Run inference on the mixed precision model.
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 ```python
 with mx.Context(mx.gpu(0)):