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* [Android] Add VisSegmentation NEON support * [ARM] change vqaddq_u8 -> vaddq_u8 * [ARM] change vqaddq_u8 -> vaddq_u8 * [Bug Fix] add FDASSERT * update assert info * add QuantizeBlendingWeight8 * Update QuantizeBlendingWeight8 * Update VisSegmentation * [Visualize] add DefaultVisualizeType and EnableFastVisuzlie * fix typos * fix typo * Update VisSegmentation * [Android] Add omp parallel support for Android * Add omp schedule(static) * [Bug Fix] fix libomp.so not found error * [Doc] Update download_prebuilt_libraries.md * Update download_prebuilt_libraries.md Co-authored-by: Jason <928090362@qq.com>
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How to Install Prebuilt Library
FastDeploy provides pre-built libraries for developers to download and install directly. Meanwhile, FastDeploy also offers easy access to compile so that developers can compile FastDeploy according to their own needs.
This article is divided into two parts:
GPU Deployment Environment
Environment Requirement
- CUDA >= 11.2
- cuDNN >= 8.0
- python >= 3.6
- OS: Linux(x64)/Windows 10(x64)
FastDeploy supports Computer Vision, Text and NLP model deployment on CPU and Nvidia GPU with Paddle Inference, ONNX Runtime, OpenVINO and TensorRT inference backends.
Python SDK
Install the released version（the newest 0.8.0 for now）
pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
Install the Develop version（Nightly build）
pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
We recommend users to use Conda to configure the development environment.
conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
C++ SDK
Install the released version（Latest 0.8.0）
| Platform | File | Description |
|---|---|---|
| Linux x64 | fastdeploy-linux-x64-gpu-0.8.0.tgz | g++ 8.2, CUDA 11.2, cuDNN 8.2 |
| Windows x64 | fastdeploy-win-x64-gpu-0.8.0.zip | Visual Studio 16 2019, CUDA 11.2, cuDNN 8.2 |
Install the Develop version（Nightly build）
| Platform | File | Description |
|---|---|---|
| Linux x64 | fastdeploy-linux-x64-gpu-0.0.0.tgz | g++ 8.2, CUDA 11.2, cuDNN 8.2 |
| Windows x64 | fastdeploy-win-x64-gpu-0.0.0.zip | Visual Studio 16 2019, CUDA 11.2, cuDNN 8.2 |
CPU Deployment Environment
Environment Requirement
- python >= 3.6
- OS: Linux(x64/aarch64)/Windows 10 x64/Mac OSX(x86/aarm64)
FastDeploy supports computer vision, text and NLP model deployment on CPU with Paddle Inference, ONNX Runtime, OpenVINO inference backends. It should be noted that under Linux aarch64 and Mac OSX, only the ONNX Runtime is supported for now.
Python SDK
Install the released version（Latest 0.8.0 for now）
pip install fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
Install the Develop version（Nightly build）
pip install fastdeploy-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html
C++ SDK
Install the released version（Latest 0.8.0 for now, Android is 1.0.0 pre-release）
| Platform | File | Description |
|---|---|---|
| Linux x64 | fastdeploy-linux-x64-0.8.0.tgz | g++ 8.2 |
| Windows x64 | fastdeploy-win-x64-0.8.0.zip | Visual Studio 16 2019 |
| Mac OSX x64 | fastdeploy-osx-x86_64-0.8.0.tgz | clang++ 10.0.0 |
| Mac OSX arm64 | fastdeploy-osx-arm64-0.8.0.tgz | clang++ 13.0.0 |
| Linux aarch64 | - | - |
| Android armv7&v8 | fastdeploy-android-1.0.0-shared.tgz | NDK 25, clang++, support arm64-v8a及armeabi-v7a |
Java SDK
Install the released version（Android is 1.0.0 pre-release）
| Platform | File | Description |
|---|---|---|
| Android Java SDK | fastdeploy-android-sdk-1.0.0.aar | NDK 20, minSdkVersion 15, targetSdkVersion 28 |
Install the Develop version（Nightly build）
| Platform | File | Description |
|---|---|---|
| Linux x64 | fastdeploy-linux-x64-0.0.0.tgz | g++ 8.2 |
| Windows x64 | fastdeploy-win-x64-0.0.0.zip | Visual Studio 16 2019 |
| Mac OSX x64 | fastdeploy-osx-arm64-0.0.0.tgz | - |
| Mac OSX arm64 | fastdeploy-osx-arm64-0.0.0.tgz | clang++ 13.0.0编译产出 |
| Linux aarch64 | - | - |
| Android armv7&v8 | - | - |