Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
🔥 LeetCode for PyTorch — practice implementing softmax, attention, GPT-2 and more from scratch with instant auto-grading. Jupyter-based, self-hosted or try online.
| Date | Stars |
|---|---|
| 2026-07-24 | 4400 |
| 2026-07-25 | 4400 |
| 2026-07-28 | 4400 |
| 2026-07-30 | 4400 |
| 2026-08-06 | 4400 |
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---
title: TorchCode
emoji: 🔥
colorFrom: red
colorTo: yellow
sdk: docker
app_port: 7860
pinned: false
---
<div align="center">
# 🔥 TorchCode
**Crack the PyTorch interview.**
Practice implementing operators and architectures from scratch — the exact skills top ML teams test for.
*An interactive coding platform, but for tensors. Self-hosted. Jupyter-based. Instant feedback.*
[](https://pytorch.org)
[](https://jupyter.org)
[](https://www.docker.com)
[](https://python.org)
[](LICENSE)
[](https://github.com/duoan/TorchCode)
[](https://ghcr.io/duoan/torchcode)
[](https://huggingface.co/spaces/duoan/TorchCode)
[](https://star-history.com/#duoan/TorchCode&Date)
</div>
---
## 🎯 Why TorchCode?
Top companies (Meta, Google DeepMind, OpenAI, etc.) expect ML engineers to implement core operations **from memory on a whiteboard**. Reading papers isn't enough — you need to write `softmax`, `LayerNorm`, `MultiHeadAttention`, and full Transformer blocks code.
TorchCode gives you a **structured practice environment** with:
| | Feature | |
|---|---|---|
| 🧩 | **40 curated problems** | The most frequently asked PyTorch interview topics |
| ⚖️ | **Automated judge** | Correctness checks, gradient verification, and timing |
| 🎨 | **Instant feedback** | Colored pass/fail per test case, just like competitive programming |
| 💡 | **Hints when stuck** | Nudges without full spoilers |
| 📖 | **Reference solutions** | Study optimal implementations after your attempt |
| 📊 | **Progress tracking** | What you've solved, best times, and attempt counts |
| 🔄 | **One-click reset** | Toolbar button to reset any notebook back to its blank template — practice the same problem as many times as you want |
| [](#) | **Open in Colab** | Every notebook has an "Open in Colab" badge + toolbar button — run problems in Google Colab with zero setup |
No cloud. No signup. No GPU needed. Just `make run` — or try it instantly on Hugging Face.
---
## 🚀 Quick Start
### Option 0 — Try it online (zero install)
**[Launch on Hugging Face Spaces](https://huggingface.co/spaces/duoan/TorchCode)** — opens a full JupyterLab environment in your browser. Nothing to install.
Or open any problem directly in Google Colab — every notebook has an [](https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/01_relu.ipynb) badge.
### Option 0b — Use the judge in Colab (pip)
In Google Colab, install the judge from PyPI so you can run `check(...)` without cloning the repo:
```bash
!pip install torch-judge
```
Then in a notebook cell:
```python
from torch_judge import check, status, hint, reset_progress
status() # list all problems and your progress
check("relu") # run tests for the "relu" task
hint("relu") # show a hint
```
### Option 1 — Pull the pre-built imaExcerpt of 35,504 characters
Read on GitHub
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Haresh Karnan · The University of Texas at Austin · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:892dcdcc4b9c6abe, topic:pytorch