Torchcode Pytorch Interview Practice
Practice implementing PyTorch primitives and transformer blocks from scratch with an automated judge before interviews or before trusting generated model code.
Overview
torchcode-pytorch-interview-practice is an agent skill for the Validate phase that runs LeetCode-style PyTorch scratch problems with an automated grading judge.
Install
npx skills add https://github.com/aradotso/trending-skills --skill torchcode-pytorch-interview-practiceWhat is this skill?
- 40 curated PyTorch problems from fundamentals through GPT-2-style components
- Automated judge with pass/fail, gradient verification, and timing feedback
- Four setup paths: Hugging Face Spaces, Colab, pip torch-judge, and Docker on port 8888
- Triggers cover softmax, LayerNorm, MultiHeadAttention, and transformer-from-scratch prep
- Jupyter-based TorchCode environment modeled on interview coding loops
- 4 installation options including Docker port 8888
Adoption & trust: 702 installs on skills.sh; 31 GitHub stars; 1/3 security scanners passed (skills.sh audits).
What problem does it solve?
You can talk through transformers in interviews but lack a repeatable environment that grades your from-scratch PyTorch implementations.
Who is it for?
Solo ML engineers prepping for PyTorch interviews or validating that an agent-generated kernel matches reference behavior.
Skip if: End-to-end model training at scale, MLOps deployment, or teams that only need high-level Hugging Face pipelines without scratch math.
When should I use this skill?
User wants to implement PyTorch operators from scratch, practice interview questions, run TorchCode judge, or verify softmax, LayerNorm, attention, or transformer components.
What do I get? / Deliverables
You complete judged notebook problems with verified gradients and timing so you know which operators you can implement without copying nn.Module shortcuts.
- Passed or failed judge results with gradient and timing feedback
- Completed scratch implementations for targeted TorchCode problems
Recommended Skills
Journey fit
Validate prototype is the canonical shelf: you are proving you can implement core tensor ops without a library crutch, not operating production training jobs. Prototype fits LeetCode-style scratch implementations (softmax, attention, GPT-2 pieces) with instant pass/fail feedback rather than shipping a trained product.
How it compares
Think LeetCode-style tensor drills with a judge, not a full fine-tuning or experiment-tracking platform.
Common Questions / FAQ
Who is torchcode-pytorch-interview-practice for?
Individual ML builders and interview candidates who want scratch PyTorch practice with automated grading in Jupyter or Colab.
When should I use torchcode-pytorch-interview-practice?
During Validate prototyping when you need to implement softmax, attention, or GPT-2 blocks from scratch, run the torchcode judge, or check a handwritten implementation.
Is torchcode-pytorch-interview-practice safe to install?
Review the Security Audits panel on this page; Docker and pip installs execute local code—use isolated environments for untrusted notebooks.
SKILL.md
READMESKILL.md - Torchcode Pytorch Interview Practice
# TorchCode — PyTorch Interview Practice > Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection. TorchCode is a Jupyter-based, self-hosted coding practice environment for ML engineers. It provides 40 curated problems covering PyTorch fundamentals and architectures (softmax, LayerNorm, MultiHeadAttention, GPT-2, etc.) with an automated judge that gives instant pass/fail feedback, gradient verification, and timing — like LeetCode but for tensors. --- ## Installation & Setup ### Option 1: Online (zero install) - **Hugging Face Spaces**: https://huggingface.co/spaces/duoan/TorchCode - **Google Colab**: Every notebook has an "Open in Colab" badge ### Option 2: pip (for use inside Colab or existing environment) ```bash pip install torch-judge ``` ### Option 3: Docker (pre-built image) ```bash docker run -p 8888:8888 -e PORT=8888 ghcr.io/duoan/torchcode:latest # Open http://localhost:8888 ``` ### Option 4: Build locally ```bash git clone https://github.com/duoan/TorchCode.git cd TorchCode make run # Open http://localhost:8888 ``` `make run` auto-detects Docker or Podman and falls back to local build if the registry image is unavailable (common on Apple Silicon/arm64). --- ## Judge API The `torch_judge` package provides the core API used in every notebook. ```python from torch_judge import check, status, hint, reset_progress # List all 40 problems and your progress status() # Run tests for a specific problem check("relu") check("softmax") check("layernorm") check("attention") check("gpt2") # Get a hint without spoilers hint("softmax") # Reset progress for a problem reset_progress("relu") ``` ### `check()` return values - Colored pass/fail per test case - Correctness check against PyTorch reference implementation - Gradient verification (autograd compatibility) - Timing measurement --- ## Problem Set Overview ### Difficulty levels: Easy → Medium → Hard | # | Problem | Key Concepts | |---|---------|--------------| | 1 | ReLU | Activation functions, element-wise ops | | 2 | Softmax | Numerical stability, exp/log tricks | | 3 | Linear Layer | `y = xW^T + b`, Kaiming init, `nn.Parameter` | | 4 | LayerNorm | Normalization, affine transform | | 5 | Self-Attention | QKV projections, scaled dot-product | | 6 | Multi-Head Attention | Head splitting, concatenation | | 7 | BatchNorm | Batch vs layer statistics, train/eval | | 8 | RMSNorm | LLaMA-style norm | | 16 | Cross-Entropy Loss | Log-softmax, logsumexp trick | | 17 | Dropout | Train/eval mode, inverted scaling | | 18 | Embedding | Lookup table, `weight[indices]` | | 19 | GELU | `torch.erf`, Gaussian error linear unit | | 20 | Kaiming Init | `std = sqrt(2/fan_in)` | | 21 | Gradient Clipping | Norm-based clipping | | 31 | Gradient Accumulation | Micro-batching, loss scaling | | 40 | Linear Regression | Normal equation, GD from scratch | --- ## Working Through a Problem Each problem notebook has the same structure: ``` templates/ 01_relu.ipynb # Blank template — your workspace 02_softmax.ipynb ... solutions/ 01_relu.ipynb # Reference solution (study after attempt) ``` ### Typical notebook workflow ```python # Cell 1: Import judge from torch_judge import check, hint import torch import torch.nn as nn # Cell 2: Your implementation def my_relu(x: torch.Tensor) -> torch.Tensor: # TODO: implement ReLU without using torch.relu or F.relu raise NotImplementedError # Cell 3: Run the judge check("relu") ``` --- ## Real Impl