ML Explosion

The explosion of different machine learning frameworks such as PyTorch, TensorFlow, JAX and NumPy (just to name a few) has many pros and cons for the development and deployment of machine learning models.

Each framework brings unique features and strengths to the table, catering to different needs and preferences of developers and organizations. PyTorch, for instance, is renowned for its dynamic computation graph and intuitive interface, making it a favorite among ML researchers. TensorFlow, with its static computation graph and robust ecosystem, is preferred in production environments and large-scale deployments. And JAX, leveraging just-in-time compilation and automatic differentiation, is gaining traction for its performance efficiency.

However, this division also presents significant challenges. Each framework has its own syntax, APIs, and paradigms, requiring developers to invest time and effort to learn and switch between them. This lack of standardization means switching between frameworks, whether just a single model or an entire codebase, an incredibly challenging task - this can hinder the efficiency of research and development, and often lead to sub-optimal deployment options. These challenges continue to worsen over time, as the emergence of new industry-leading frameworks like MLX exemplifies the continuing expansion and diversification of the machine learning framework ecosystem, ultimately leading to greater fragmentation.

This is the problem we’re addressing at Ivy with our source-to-source transpiler - allowing simple and painless conversion of any machine learning models or code between any of these cutting-edge frameworks.

Round Up

Hopefully, this has painted a clear picture of how many different ML frameworks have exploded onto the scene 🙂

Feel free to reach out on discord if you have any questions!