metadata
title: EasyDeL
emoji: 🔮
colorFrom: purple
colorTo: blue
sdk: static
pinned: false
EasyDeL
EasyDeL is an open-source framework for building, training, fine-tuning, converting, and serving modern ML models in JAX at scale. It is designed for people who want the performance benefits of JAX without giving up the practical ergonomics of the Hugging Face ecosystem.
Purpose
JAX is extremely powerful, but scaling real training/inference workloads can still feel fragmented: model code, sharding, kernels, training loops, serving, and conversions often live in separate places. EasyDeL’s goal is to provide a cohesive toolkit where these pieces work together—while still staying readable and hackable.
What EasyDeL focuses on
- Scale-first: multi-device training/inference across GPU/TPU with sharding-aware utilities.
- Production inference: a dedicated serving stack built for throughput and low latency.
- Interoperability: straightforward workflows with Hugging Face models and assets.
- Hackability: implementations you can actually read, debug, and modify.