JAX
NumPy-like framework for composable function transformations with automatic differentiation, JIT compilation, and GPU/TPU acceleration
46.5+
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Overview
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more - jax-ml/jax
The Verdict
Who Should Use JAX?
Best For
- [Add best use case 1]
- [Add best use case 2]
- [Add best use case 3]
Not Ideal For
- [Add limitation 1]
- [Add limitation 2]
What's Great
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- Fork3.6k
- Star35.7k
- Transformations
- Scaling
Pricing
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Key Features
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- Fork3.6k
- Star35.7k
- Transformations
- Scaling
- Current gotchas
Platforms
- [Add supported platforms]
How It Compares
| Feature | JAX | Competitor 1 | Competitor 2 |
|---|---|---|---|
| Key Feature | — | — | — |
| Pricing | — | — | — |
| Best For | — | — | — |
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