JAX
CommunityAccelerate ML with JAX: fast, differentiable.
Authoryonesuke
Version1.0.0
Installs0
System Documentation
What problem does it solve?
JAX solves the need for high-performance, differentiable computing for ML research by providing automatic differentiation, JIT compilation, and scalable parallelism across devices.
Core Features & Use Cases
- Automatic differentiation: gradient computation for Python code using grad/jacrev, enabling end-to-end differentiation over models.
- Transformations & Acceleration: jit, vmap, and pmap for speed and parallelism across hardware.
- Pytrees & Functional Programming: containers for model parameters and state with functional APIs.
- Use Case: Rapid prototyping of ML models with deterministic execution and hardware acceleration.
Quick Start
Run a tiny experiment using jax.grad and jax.jit to train a simple linear model on a small dataset.
Dependency Matrix
Required Modules
jaxjaxlib
Components
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: JAX Download link: https://github.com/yonesuke/skills/archive/main.zip#jax Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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