f2ac76df5a
* [MLP]: create using core TensortRT python APIs * [MLP]: README.md added with chart * [MLP]: C++ TensorRT APIs added * [MLP]: Updated Docs * [MLP]: basic .wts added with APIs * [MLP]: Updated minor details * [MLP]: fix
58 lines
1.5 KiB
Markdown
58 lines
1.5 KiB
Markdown
# MLP
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MLP is the most basic net in this tensorrtx project for starters. You can learn the basic procedures of building
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TensorRT app from the provided APIs. The process of building a TensorRT engine explained in the chart below.
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## Helper Files
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`logging.h` : A logger file for using NVIDIA TRT API (mostly same for all models)
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`mlp.wts` : Converted weight file (simple file, you can open and check it)
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## TensorRT C++ API
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```
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// 1. generate mlp.wts from https://github.com/wang-xinyu/pytorchx/tree/master/mlp -- or use the given .wts file
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// 2. put mlp.wts into tensorrtx/mlp (if using the generated weights)
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// 3. build and run
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cd tensorrtx/mlp
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mkdir build
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cd build
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cmake ..
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make
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sudo ./mlp -s // serialize model to plan file i.e. 'mlp.engine'
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sudo ./mlp -d // deserialize plan file and run inference
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```
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## TensorRT Python API
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```
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# 1. Generate mlp.wts from https://github.com/wang-xinyu/pytorchx/tree/master/mlp -- or use the given .wts file
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# 2. Put mlp.wts into tensorrtx/mlp (if using the generated weights)
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# 3. Install Python dependencies (tensorrt/pycuda/numpy)
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# 4. Run
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cd tensorrtx/mlp
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python mlp.py -s # serialize model to plan file, i.e. 'mlp.engine'
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python mlp.py -d # deserialize plan file and run inference
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```
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## Note
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It also supports the latest CUDA-11.4 and TensorRT-8.2.x
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