What is Ivy?
Navigating the diverse landscape of Machine Learning frameworks can be challenging. You might find the perfect model or library, only to realize it's built in PyTorch when your project relies on TensorFlow or JAX. Manually rewriting code is often tedious, time-consuming, and prone to subtle errors that are hard to debug.
Ivy is designed specifically to bridge these gaps. It acts as a universal translator for your ML code, allowing you to convert models, tools, and even entire libraries between popular frameworks. The goal is to preserve functionality while streamlining your development process, giving you the freedom to use the best resources available, regardless of their original framework.
Key Features
Here's how Ivy helps you work more effectively across different ML ecosystems:
🔁 Convert Code with
ivy.transpile: Translate ML models, functions, or entire libraries from a source framework (currently PyTorch) to a target framework (TensorFlow, JAX, or NumPy) often using just a single line of code. This significantly reduces the manual effort involved in adapting code.🔧 Retain Full Functionality & Modifiability: Because Ivy performs source-to-source conversion, the transpiled code isn't a black box. It remains readable, functional, and fully editable, allowing you to inspect, debug, or extend it within your chosen target framework.
⚡ Optimize with
ivy.trace_graph: Generate efficient, framework-native computational graphs from Python functions containing Ivy or native framework code. This process removes Python overhead and optimizes the execution path.↔️ Unify Major Frameworks: Ivy currently supports conversion from PyTorch to TensorFlow, JAX, and NumPy. Support for additional source frameworks is actively under development, aiming to create broad interoperability.
🧩 Integrate Seamlessly: Use Ivy to pull components from one framework's ecosystem into another. Libraries like Kornia have already integrated Ivy to offer multi-framework support out-of-the-box.
Use Cases
Adopt a Cutting-Edge Model: You discover a novel algorithm implemented in PyTorch on GitHub, complete with pre-trained weights. Your team's production environment uses TensorFlow. Instead of a lengthy manual porting process, you use
ivy.transpile(pytorch_model_class, source='torch', target='tensorflow')to generate an equivalent TensorFlow model structure, ready for weight loading and integration.Benchmark Across Backends: You've developed a custom JAX function for a specific mathematical operation and need to understand its performance characteristics compared to TensorFlow or even a pure NumPy implementation on different hardware. Use
ivy.transpileto create equivalent versions for each backend, enabling consistent and fair benchmarking from a single codebase.Leverage Specialized Libraries: Your main project is in TensorFlow, but you need advanced image augmentation functions available only in the PyTorch-based Kornia library. By using
tf_kornia = ivy.transpile(kornia, source='torch', target='tensorflow'), you can directly call Kornia functions within your TensorFlow code, treating the transpiled library like a native TensorFlow module.
Conclusion
Ivy empowers you to break free from framework silos in your Machine Learning work. By simplifying the conversion of models and libraries between PyTorch, TensorFlow, JAX, and NumPy, Ivy saves valuable development time and unlocks new possibilities. You gain the flexibility to use the best available tools and code, regardless of their origin, allowing you to focus more on innovation and less on translation. Its source-to-source approach ensures you retain control and understanding of your codebase throughout the process.
More information on Ivy
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Ivy was manually vetted by our editorial team and was first featured on 2025-04-25.
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