jittor

Introducing Jittor: A Cutting-edge Just-in-time Deep Learning Framework

What is Jittor?

Jittor, which stands for "Just-in-time Tensor Operations for Rendering," is an innovative deep learning framework designed to harness the power of Just-in-time (JIT) compiling and meta-operators to deliver high performance. This framework excels in generating efficient code tailored to individual models, thanks to its integrated op compiler and tuner.

Key Features

• High Performance: Jittor's standout feature is its ability to compile the entire framework and meta-operators just in time, providing optimized code execution performance.
• Rich Model Libraries: Users can tap into a diverse collection of high-performance model libraries, covering areas such as image recognition, detection, segmentation, and more, including reinforcement learning and geometric learning.
• Support for Various Platforms: While its front-end is based in Python, a popular choice for its dynamic graph execution and module design, the back-end leverages high-performance languages like CUDA and C++ to boost efficiency.

Why Choose Jittor?

Jittor is a competitive choice for developers seeking a cutting-edge deep learning framework that is both versatile and powerful. Its unique approach using just-in-time compilation provides distinct advantages in performance optimizations which are crucial for complex models.

Getting Started with Jittor

1. Ease of Use: Jittor is designed with user-friendliness in mind, especially for Python developers. Here is a simple example to build and train a neural network:

   import jittor as jt
   from jittor import Module, 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 _ in range(n):
           x = np.random.rand(50, 1)
           y = x * x
           yield jt.float32(x), jt.float32(y)
   
   learning_rate = 0.1
   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()}")
   

2. Installation: Jittor can be installed using pip, docker, or manually. For most Python enthusiasts and developers, pip offers a straightforward installation pathway.

   sudo apt install python3.7-dev libomp-dev
   python3.7 -m pip install jittor
   

3. Compatibility: Jittor supports multiple operating systems including Linux, macOS, and Windows. Additionally, it leverages CUDA for enhanced performance on compatible NVIDIA GPUs.

4. Docker Support: For users keen on hassle-free setup, Jittor provides a Docker option which simplifies the environment configuration process.

Documentation and Community Resources

Jittor provides extensive documentation and learning resources including tutorials to guide users from beginner to advanced levels. Users can visit the official Jittor Website and engage with the community through forums and GitHub for support and collaboration.

Conclusion

Jittor represents the future of deep learning frameworks by offering unparalleled performance using trailing-edge technologies like Just-in-time compilation combined with the flexibility of Python. As a tool maintained by the Tsinghua CSCG Group, it's not just an asset for current projects but also a pioneer setting trends in computational research and applied machine learning.

For those invested in exploring deep learning's potential, Jittor is a compelling choice that merits attention and consideration in modern AI development portfolios.