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@@ -82,7 +82,7 @@ To use Hugot as a library in your application, you can directly import it and fo
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#### Backends
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- if using Onnx Runtime, the onnxruntime.so file should be obtained from the releases section of this page. If you want to use alternative architectures from `linux/amd64` you will have to download it from [the ONNX Runtime releases page](https://github.com/microsoft/onnxruntime/releases/), see the [dockerfile](./Dockerfile) as an example. Hugot looks for this file at /usr/lib/onnxruntime.so or /usr/lib64/onnxruntime.so by default. A different location can be specified by passing the `WithOnnxLibraryPath()` option to `NewORTSession()`, e.g:
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- if using Onnx Runtime, the onnxruntime.so file should be obtained from the releases section of this page. If you want to use alternative architectures from `linux/amd64` you will have to download it from [the ONNX Runtime releases page](https://github.com/microsoft/onnxruntime/releases/), see the [dockerfile](./Dockerfile) as an example. Hugot looks for this file at /usr/lib/libonnxruntime.so by default. A different location can be specified by passing the `WithOnnxLibraryPath()` option to `NewORTSession()`, e.g:
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```
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session, err := NewORTSession(
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- The Nvidia driver for your graphics card (if running in Docker and WSL2, starting with --gpus all should inherit the drivers from the host OS)
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- ONNX Runtime:
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- The cuda gpu version of ONNX Runtime on the machine/docker container. You can see how we get that by looking at the [Dockerfile](./Dockerfile). You can also get the ONNX Runtime libraries that we use for testing from the release. Just download the gpu .so libraries and put them in /usr/lib64.
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- The cuda gpu version of ONNX Runtime on the machine/docker container. You can see how we get that by looking at the [Dockerfile](./Dockerfile). You can also get the ONNX Runtime libraries that we use for testing from the release. Just download the gpu .so libraries and put them in /usr/lib.
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- The required CUDA libraries installed on your system that are compatible with the ONNX Runtime gpu version you use. See [here](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html). For instance, for onnxruntime-gpu 19.0, we need CUDA 12.x (any minor version should be compatible) and cuDNN 9.x.
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