Ivy

Visit Tool

Ivy is a Coding & Development tool that converts machine learning code between frameworks. It allows users to transpile ML models, tools, and libraries across PyTorch, TensorFlow, JAX, and NumPy.

Claim this tool

2Views

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is ivy?

Ivy is an open-source tool designed to facilitate the conversion of machine learning code between various popular frameworks. It enables developers to seamlessly transpile ML models, tools, and libraries, supporting conversions to and from PyTorch, TensorFlow, JAX, and NumPy. Key functionalities include `ivy.transpile()` for converting framework-specific code to a target framework, and `ivy.trace_graph()` for tracing efficient computational graphs. Ivy supports both eager and lazy transpilation, adapting to whether a class/function or a module is provided. This flexibility makes it a valuable resource for developers working in multi-framework environments, simplifying code portability and integration.

Best used for

Ideal for developers who need to port machine learning models between different frameworks, integrate code from various ML libraries, and optimize computational graphs. Especially valuable for those working in multi-framework environments or migrating existing models to new backends.

Common actions

convert machine learning models

transpile ML code

trace computational graphs

integrate ML frameworks

face swapping"AI Agents"open-sourcecollaborationgithub copilotworkflowsdeepfakeautomated workflowlow-code/no-code

Capabilities

Key features

Convert ML code frameworks
Transpile PyTorch, TensorFlow
Transpile JAX, NumPy
Trace computational graphs
Eager and lazy transpilation

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

Which machine learning frameworks does Ivy support for conversion?

Ivy currently supports conversions to and from PyTorch, TensorFlow, JAX, and NumPy. This allows developers to seamlessly transpile their machine learning models and code between these popular frameworks, enhancing interoperability and flexibility in their projects.

How does Ivy handle transpilation for different types of code?

Ivy offers both eager and lazy transpilation. If a class or function is provided, transpilation happens eagerly. If a module (library) is provided, transpilation occurs lazily, initializing the conversion when the function is first called, ensuring efficient execution.

Can Ivy optimize or trace computational graphs?

Yes, Ivy includes the `ivy.trace_graph()` function, which allows users to trace an efficient fully-functional graph from a given function. This feature helps in removing wrapping and redundant code, leading to more optimized and streamlined computational processes.

Trending

Subcategories trending in Coding & Development

Open Source & Models DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

Trending

Also listed in

This tool also appears in

AI Agents & Automation › AI Frameworks & Infra

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce