Jittor

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Jittor is a high-performance deep learning framework that uses JIT compiling and meta-operators. It integrates a powerful op compiler and tuner to generate specialized high-performance code for models.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is jittor?

Jittor is a high-performance deep learning framework designed for efficiency through Just-in-Time (JIT) compiling and meta-operators. The entire framework and its meta-operators are compiled on-the-fly, ensuring optimized performance. It features a robust op compiler and tuner that automatically generates high-performance code tailored specifically for your deep learning models. Jittor supports a wide array of high-performance model libraries, encompassing image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, and reinforcement learning. Its front-end is Python-based, utilizing a popular module design and dynamic graph execution interface, while the back-end is implemented in high-performance languages like CUDA and C++.

Best used for

Ideal for developers and data scientists who need to build high-performance deep learning models, optimize computational graphs, and accelerate research in areas like image recognition and reinforcement learning. Especially valuable for those requiring fine-grained control over compilation and performance tuning.

Common actions

optimize deep learning

compile models

accelerate AI research

develop neural networks

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Capabilities

Key features

JIT compiling
Meta-operators
Op compiler
Performance tuner
Python front-end
CUDA/C++ back-end

Target Audience

developerdata scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What operating systems does Jittor support?

Jittor supports Linux distributions such as Ubuntu, CentOS, Arch, UOS, and KylinOS, as well as Windows 10 & 11. It also offers Docker images for both CPU and CUDA environments, providing flexible deployment options across various platforms.

What are the hardware requirements for running Jittor?

For CPU, Jittor requires x86, x86_64, ARM, or Loongson processors. For GPU acceleration, it supports Nvidia CUDA (>= 10.0) with cuDNN, AMD ROCm (>= 4.0), or Hygon DCU DTK (>= 22.04). Python 3.7+ is also required.

Can I use Jittor with CUDA on Windows?

Yes, Jittor can be used with CUDA on Windows. It automatically detects and installs CUDA if your NVIDIA driver supports CUDA 10.2 or above. You can also manually trigger CUDA installation via a Jittor utility script if needed.

How does Jittor's JIT compilation benefit deep learning models?

Jittor's Just-in-Time (JIT) compilation compiles the entire framework and meta-operators dynamically. This allows its powerful op compiler and tuner to generate highly specialized and optimized code for your specific model, leading to significant performance improvements and faster execution.

What kind of deep learning models can I build with Jittor?

Jittor provides extensive support for various deep learning applications, including image recognition, object detection, segmentation, generative models, differentiable rendering, geometric learning, and reinforcement learning. Its flexible architecture allows for a wide range of model development.

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