LightGBM
A fast, distributed, high-performance gradient boosting framework based on decision tree algorithms.
The universal AI bridge for transpiling models and optimizing cross-framework inference.
Ivy is a high-performance, unified AI framework designed to solve the fragmentation in the machine learning ecosystem. In the 2026 landscape, Ivy serves as the critical 'translation layer' that allows developers to write code in one framework (like PyTorch) and run it on any other backend (JAX, TensorFlow, or PaddlePaddle) with zero overhead. Technically, Ivy achieves this through a graph-to-graph transpilation process and a unified functional API that abstracts framework-specific operations into a common intermediate representation. This architecture enables seamless model migration, cross-backend performance benchmarking, and hardware-agnostic deployment. Beyond its core transpilation engine, the Unify platform integrates an intelligent LLM routing layer, which dynamically selects the most cost-effective or highest-performing model endpoint based on real-time telemetry. As enterprises increasingly adopt multi-cloud and multi-model strategies, Ivy's role as a vendor-neutral infrastructure component positions it as an essential tool for avoiding framework lock-in and optimizing the full lifecycle of neural network development and deployment.
A source-to-source transpiler that converts code between PyTorch, JAX, and TensorFlow by mapping functional signatures.
A fast, distributed, high-performance gradient boosting framework based on decision tree algorithms.
The high-level deep learning API for JAX, PyTorch, and TensorFlow.
A minimalist, PyTorch-based Neural Machine Translation toolkit for streamlined research and education.
The high-performance deep learning framework for flexible and efficient distributed training.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A standardized set of over 500+ math and ML operations that execute natively on the active backend.
An orchestration layer that redirects prompts to the optimal LLM based on latency, cost, or quality benchmarks.
Captures operations in a symbolic graph before execution, enabling cross-framework graph optimizations.
A repository of pre-transpiled models ready for immediate deployment in any major framework.
Enables backpropagation through a pipeline that mixes modules from different frameworks.
Automatically profiles transpiled code on specific hardware (NVIDIA, TPU, Apple Silicon) to select the fastest backend.
Technical debt from old TF1.x/2.x models that need to be integrated into modern PyTorch pipelines.
Registry Updated:2/7/2026
PyTorch models often lack the native performance of JAX on TPU hardware.
High costs associated with using GPT-4 for all queries, including simple ones.