JAX is a machine learning library with a NumPy-like interface designed to run on accelerators such as GPUs and TPUs. Its distinguishing feature are arbitrarily composable function transformations, enabling JIT compilation, higher order gradients, automatic batching, simple multi-device parallelism, and more.
What's the difference between this Python project and similar ones?
Enumerate comparisons.
PyTorch: JAX provides higher order gradients, automatic batching, and JIT compilation at higher performance than PyTorch
TensorFlow: JAX is much simpler and narrower in scope, which means all its components are better integrated, easier to use, and has fewer bugs. It also exclusively leverages the XLA compiler instead of pre-compiled kernels.
Anyone who agrees with this pull request could submit an Approve review to it.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/1769
**Author:** [@n2cholas](https://github.com/n2cholas)
**Created:** 5/29/2021
**Status:** ❌ Closed
**Base:** `master` ← **Head:** `patch-1`
---
### 📝 Commits (1)
- [`218c133`](https://github.com/vinta/awesome-python/commit/218c1339fc43027a92cfdebdc4fa59a6906a734c) Add JAX
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## What is this Python project?
JAX is a machine learning library with a NumPy-like interface designed to run on accelerators such as GPUs and TPUs. Its distinguishing feature are arbitrarily composable function transformations, enabling JIT compilation, higher order gradients, automatic batching, simple multi-device parallelism, and more.
## What's the difference between this Python project and similar ones?
Enumerate comparisons.
- PyTorch: JAX provides higher order gradients, automatic batching, and JIT compilation at higher performance than PyTorch
- TensorFlow: JAX is much simpler and narrower in scope, which means all its components are better integrated, easier to use, and has fewer bugs. It also exclusively leverages the XLA compiler instead of pre-compiled kernels.
Anyone who agrees with this pull request could submit an *Approve* review to it.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/1769
Author: @n2cholas
Created: 5/29/2021
Status: ❌ Closed
Base:
master← Head:patch-1📝 Commits (1)
218c133Add JAX📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
What is this Python project?
JAX is a machine learning library with a NumPy-like interface designed to run on accelerators such as GPUs and TPUs. Its distinguishing feature are arbitrarily composable function transformations, enabling JIT compilation, higher order gradients, automatic batching, simple multi-device parallelism, and more.
What's the difference between this Python project and similar ones?
Enumerate comparisons.
Anyone who agrees with this pull request could submit an Approve review to it.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.