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Copy file name to clipboardExpand all lines: examples/dynamo/auto_generate_plugin.py
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the performance and resource overhead from a graph break.
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Previously this involved a complex process in not only building a performant kernel but setting it up to run in TensorRT (see: `Using Custom Kernels within TensorRT Engines with Torch-TensorRT <https://pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html>`_).
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With TensorRT 10.7, there is a new Python native plugin system which greatly streamlines this process. This
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As of TensorRT 10.7, there is a new Python native plugin system which greatly streamlines this process. This
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plugin system also allows Torch-TensorRT to automatically generate the necessary conversion code to convert the
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