ncnn is a high-performance neural network inference framework optimized for mobile, embedded, and desktop deployment. It has no third-party runtime dependencies, runs across CPU and Vulkan GPU backends, and provides tools such as pnnx for converting PyTorch and ONNX models to ncnn. Developers can deploy deep learning models efficiently on phones, PCs, browsers, and edge devices. ncnn is currently being used in many Tencent applications, such as QQ, Qzone, WeChat, Pitu, and so on.
ncnn 是一个面向移动端、嵌入式和桌面端部署优化的高性能神经网络推理框架。 ncnn 无第三方运行时依赖，支持 CPU 和 Vulkan GPU 后端，并提供 pnnx 等工具将 PyTorch 和 ONNX 模型转换为 ncnn 模型。 基于 ncnn，开发者可以将深度学习模型高效部署到手机、PC、浏览器和边缘设备上。 ncnn 目前已在腾讯多款应用中使用，如：QQ，Qzone，微信，天天 P 图等。
The recommended beginner path is PyTorch -> pnnx -> ncnn.
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Install pnnx in a PyTorch environment pip3 install pnnxExport a PyTorch model to ncnn import torch
import torch.nn as nn
import pnnx
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 8, 1)
self.relu = nn.ReLU()
self.fc = nn.Linear(8, 4)
def forward(self, x):
x = self.conv(x)
x = self.relu(x)
x = x.mean((2, 3))
return self.fc(x)
model = Model().eval()
x = torch.rand(1, 3, 224, 224)
pnnx.export(model, "model.pt", (x,))This generates |
Run with ncnn C++ API #include "net.h"
ncnn::Net net;
net.load_param("model.ncnn.param");
net.load_model("model.ncnn.bin");
ncnn::Mat in(224, 224, 3);
auto ex = net.create_extractor();
ex.input("in0", in);
ncnn::Mat out;
ex.extract("out0", out);Or use Python import numpy as np
import ncnn
net = ncnn.Net()
net.load_param("model.ncnn.param")
net.load_model("model.ncnn.bin")
x = np.zeros((3, 224, 224), np.float32)
mat = ncnn.Mat(x)
ex = net.create_extractor()
ex.input("in0", mat)
ret, out = ex.extract("out0")
print(np.array(out).shape) |
See pnnx, use ncnn with PyTorch or ONNX, Python API, and examples for complete workflows.
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技术交流 QQ 群 637093648 (超多大佬) 答案：卷卷卷卷卷（已满） |
Telegram Group | Discord Channel |
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Pocky QQ 群（MLIR YES!） 677104663 (超多大佬) 答案：multi-level intermediate representation |
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他们都不知道 pnnx 有多好用群 818998520 (新群！) |
https://github.com/Tencent/ncnn/releases/latest
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how to build ncnn library on Linux / Windows / macOS / Raspberry Pi3, Pi4 / POWER / Android / NVIDIA Jetson / iOS / WebAssembly / AllWinner D1 / Loongson 2K1000 |
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| Android |
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| Android shared |
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| HarmonyOS |
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| HarmonyOS shared |
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| iOS |
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| iOS-Simulator |
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| macOS |
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| Mac-Catalyst |
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| watchOS |
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| watchOS-Simulator |
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| tvOS |
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| tvOS-Simulator |
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| visionOS |
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| visionOS-Simulator |
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| Apple xcframework |
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| Ubuntu 22.04 |
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| Ubuntu 24.04 |
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| Ubuntu 26.04 |
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| VS2015 |
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| VS2017 |
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| VS2019 |
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| VS2022 |
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| VS2026 |
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| WebAssembly |
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| WebAssembly64 |
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| Linux (arm) |
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| Linux (aarch64) |
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| Linux (mips) |
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| Linux (mips64) |
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| Linux (riscv64) |
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| Linux (loongarch64) |
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Use the prebuilt packages above when possible. To build from source, see the full how to build ncnn library guide for Linux, Windows, macOS, Android, iOS, WebAssembly, HarmonyOS, Raspberry Pi, Jetson, and embedded targets.
Common Linux build:
git clone --recursive https://github.com/Tencent/ncnn.git
cd ncnn
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=ON -DNCNN_BUILD_EXAMPLES=ON ..
cmake --build . -j$(nproc)