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The Holoscan SDK: building high-performance AI streaming applications

Project description

Holoscan SDK

The Holoscan SDK Python Wheel is part of NVIDIA Holoscan, the AI sensor processing platform that combines hardware systems for low-latency sensor and network connectivity, optimized libraries for data processing and AI, and core microservices to run streaming, imaging, and other applications, from embedded to edge to cloud. It can be used to build streaming AI pipelines for a variety of domains, including Medical Devices, High Performance Computing at the Edge, Industrial Inspection and more.

Visit the NGC demo website for a live demonstration of some of Holoscan capabilities.

System Requirements

Troubleshooting

ERROR gxf/std/extension_loader.cpp@114: Failed to load extension ... Error: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found

The current version of the wheels were built and tested on Ubuntu 20.04 with libc 2.31 instead of manylinux. They were named as such to make them available early on PyPI. You'll need to use a system with a more recent version of libc to use the Holoscan SDK python wheels.

ERROR gxf/std/extension_loader.cpp@114: Failed to load extension ... Error: libnvonnxparser.so.8: cannot open shared object file: No such file or directory

TensorRT is missing from your system. Follow links in the section above to install TensorRT, or install it through the python wheels which will also install cuda-runtime:

python3 -m pip install nvidia-tensorrt==8.2.3.0 --index https://pypi.ngc.nvidia.com

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holoscan-0.4.0-cp311-cp311-manylinux2014_x86_64.whl (24.7 MB view details)

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holoscan-0.4.0-cp311-cp311-manylinux2014_aarch64.whl (22.7 MB view details)

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holoscan-0.4.0-cp310-cp310-manylinux2014_x86_64.whl (24.7 MB view details)

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holoscan-0.4.0-cp310-cp310-manylinux2014_aarch64.whl (22.7 MB view details)

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holoscan-0.4.0-cp39-cp39-manylinux2014_x86_64.whl (24.7 MB view details)

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holoscan-0.4.0-cp39-cp39-manylinux2014_aarch64.whl (22.7 MB view details)

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holoscan-0.4.0-cp38-cp38-manylinux2014_x86_64.whl (24.7 MB view details)

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