vorushin

vorushin / pallas_puzzles

Public

Puzzles/etudes for JAX/TPU kernel language

vorushin.github.ioblogpallas-puzzles
19
2
100% credibility
Found Mar 02, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

Interactive puzzle notebooks that teach efficient computing kernels through progressive challenges runnable in free online environments.

How It Works

1
📖 Discover Pallas Puzzles

You stumble upon a fun collection of hands-on puzzles that teach clever ways to make computers think faster.

2
🔗 Jump into the playground

Click a simple badge to open everything in a free online notebook—no downloads or setup required.

3
✨ Solve your first puzzle

Fill in a few blanks with easy math, hit run, and watch the green 'PASSED' light up your screen.

4
Pick your learning path
🧠
Attention path

Build up to super-efficient ways AI focuses on important words.

💪
Grouping path

Learn to split work smartly among specialized AI helpers.

5
📈 Conquer puzzle after puzzle

Each challenge builds on the last, with hints and tests guiding you to feel like a pro.

🎉 Become a speed wizard

You've unlocked the secrets to making AI run lightning-fast and can even craft your own puzzles!

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Star Growth

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AI-Generated Review

What is pallas_puzzles?

Pallas puzzles delivers interactive Jupyter notebook etudes that teach Pallas, JAX's kernel language for custom TPU operations. Users solve progressive puzzles filling in kernel code to match reference outputs, starting from basics like tiled matmuls and building to real-world kernels for SplashAttention and grouped matmuls in MoEs. Everything runs on free Google Colab CPU instances—no TPU or setup required.

Why is it gaining traction?

It stands out by turning abstract Pallas concepts into bite-sized, test-driven puzzles that run instantly in notebooks, skipping dense docs or trial-and-error on hardware. Developers get hands-on paths to production kernels used in JAX frameworks like MaxText, with clear prerequisites like solid JAX/NumPy knowledge. The interpret mode on CPU lowers the entry barrier, letting users prototype jax/tpu kernels before deploying.

Who should use this?

JAX users optimizing ML models on TPUs, especially those implementing custom attention or MoE layers. ML engineers at scale who need efficient block-sparse ops without starting from scratch. Researchers prototyping kernel languages for jax/tpu acceleration.

Verdict

Solid learning resource for Pallas despite 19 stars and 1.0% credibility score—docs are clear via notebooks, but watch for limited community support. Try the Colab badges if you're serious about TPU kernels; skip if you need battle-tested prod code.

(198 words)

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