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Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.
| Date | Stars |
|---|---|
| 2026-07-31 | 3226 |
| 2026-08-03 | 3227 |
| 2026-08-06 | 3225 |
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# Jittor: a Just-in-time(JIT) deep learning framework
[Quickstart](#quickstart) | [Install](#install) | [Tutorial](#tutorial) | [简体中文](./README.cn.md)
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just-in-time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code with specialized for your model. Jittor also contains a wealth of high-performance model libraries, including: image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. .
The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
Related Links:
* [Jittor Website](https://cg.cs.tsinghua.edu.cn/jittor/)
* [Jittor Tutorials](https://cg.cs.tsinghua.edu.cn/jittor/tutorial/)
* [Jittor Models](https://cg.cs.tsinghua.edu.cn/jittor/resources/)
* [Jittor Documents](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
* [Github](https://github.com/jittor/jittor), [GitLink](https://www.gitlink.org.cn/jittor/jittor), [Gitee](https://gitee.com/jittor/jittor)
* [Jittor Forum](https://discuss.jittor.org/)
* [Awesome Jittor List (EN)](https://github.com/Jittor/jittor/blob/master/AWESOME-JITTOR-LIST.md)
* [Awesome Jittor List (CN)](https://github.com/Jittor/jittor/blob/master/AWESOME-JITTOR-LIST.cn.md)
* IM: QQ Group(761222083)
The following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.
```python
import jittor as jt
from jittor import Module
from jittor import nn
import numpy as np
class Model(Module):
def __init__(self):
self.layer1 = nn.Linear(1, 10)
self.relu = nn.Relu()
self.layer2 = nn.Linear(10, 1)
def execute (self,x) :
x = self.layer1(x)
x = self.relu(x)
x = self.layer2(x)
return x
def get_data(n): # generate random data for training test.
for i in range(n):
x = np.random.rand(batch_size, 1)
y = x*x
yield jt.float32(x), jt.float32(y)
learning_rate = 0.1
batch_size = 50
n = 1000
model = Model()
optim = nn.SGD(model.parameters(), learning_rate)
for i,(x,y) in enumerate(get_data(n)):
pred_y = model(x)
dy = pred_y - y
loss = dy * dy
loss_mean = loss.mean()
optim.step(loss_mean)
print(f"step {i}, loss = {loss_mean.data.sum()}")
```
## Contents
* [Quickstart](#quickstart)
* [Install](#install)
* [Tutorial](#tutorial)
* [Contributing](#contributing)
* [The Team](#theteam)
* [License](#license)
## Quickstart
We provide some jupyter notebooks to help you quick start with Jittor.
- [Example: Model definition and training][1]
- [Basics: Op, Var][2]
- [Meta-operator: Implement your own convolution with Meta-operator][3]
## Install
Jittor environment requirements:
| OS | CPU | Python | Compiler | (Optional) GPU platform |
|--------------------------------------------------------|-------------------------------------|--------|--------------|---------------------------------------------|
| Linux<br>(Ubuntu, CentOS, Arch, <br>UOS, KylinOS, ...) | x86 <br>x86_64 <br>ARM <br>loongson | >= 3.7 | g++ >=5.4 | Nvidia CUDA >= 10.0, [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar) <br> or [AMD ROCm](https://docs.amd.com/) >= 4.0 <br> or [Hygon DCU DTK](https://tycloud.hpccube.com/doc/1.0.6/11277/general-handbook/software-tutorial/jittor.html) >= 22.04 |
| Windows 10 & 11 Excerpt of 11,367 characters
Read on GitHub
Dun Liang
855
192
Zheng-Ning Liu
159
Haoyang Peng · China
101
99
DongYang Li · Tsinghua University · China
92
76
72
48
46
CHEN Xinsheng · Tsinghua University · China
42
40
20
19
17
15
9
Shizhan Lu · Northeastern University(China)
5
Xin Yao · @NVIDIA
5
5
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matched fp:bf837164e5a3b1e6, topic:gpu, topic:cuda
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