40 lines
1.7 KiB
Markdown
40 lines
1.7 KiB
Markdown
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### Installation instructions for Ubuntu 16.04
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* Make sure <a href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html">CUDA</a> and <a href="https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html">cuDNN</a> are installed. Three configurations have been tested:
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- TensorFlow 1.4.1, CUDA 8.0 and cuDNN 6.0
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- TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.4
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- ~~TensorFlow 1.13.0, CUDA 10.0 and cuDNN 7.5~~ (bug found only with this version).
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* Ensure all python packages are installed :
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sudo apt update
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sudo apt install python3-dev python3-pip python3-tk
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* Follow <a href="https://www.tensorflow.org/install/pip">Tensorflow installation procedure</a>.
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* Install the other dependencies with pip:
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- numpy
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- scikit-learn
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- psutil
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- matplotlib (for visualization)
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- mayavi (for visualization)
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- PyQt5 (for visualization)
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* Compile the customized Tensorflow operators located in `tf_custom_ops`. Open a terminal in this folder, and run:
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sh compile_op.sh
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N.B. If you installed Tensorflow in a virtual environment, it needs to be activated when running these scripts
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* Compile the C++ extension module for python located in `cpp_wrappers`. Open a terminal in this folder, and run:
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sh compile_wrappers.sh
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You should now be able to train Kernel-Point Convolution models
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### Installation instructions for Ubuntu 18.04 (Thank to @noahtren)
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* Remove the `-D_GLIBCXX_USE_CXX11_ABI=0` flag for each line in `tf_custom_ops/compile_op.sh` (problem with the version of gcc). One configuration has been tested:
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- TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.3.1
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