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🔥 TorchCode

Crack the PyTorch interview.

Practice implementing operators and architectures from scratch — the exact skills top ML teams test for.

Like LeetCode, but for tensors. Self-hosted. Jupyter-based. Instant feedback.

![PyTorch](https://pytorch.org) ![Jupyter](https://jupyter.org) ![Docker](https://www.docker.com) ![Python](https://python.org) ![License: MIT](LICENSE)

![GitHub stars](https://github.com/duoan/TorchCode) ![GitHub Container Registry](https://ghcr.io/duoan/torchcode) ![Hugging Face Spaces](https://huggingface.co/spaces/duoan/TorchCode)

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</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](#)	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 ![Open In Colab](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 image (fastest)

bash

docker run -p 8888:8888 -e PORT=8888 ghcr.io/duoan/torchcode:latest

Option 2 — Build locally

bash

make run

Open <http://localhost:8888> — that's it. Works with both Docker and Podman (auto-detected).

📋 Problem Set

Frequency: 🔥 = very likely in interviews, ⭐ = commonly asked, 💡 = emerging / differentiator

🧱 Fundamentals — "Implement X from scratch"

The bread and butter of ML coding interviews. You'll be asked to write these without torch.nn.

#	Problem	What You'll Implement	Freq	Key Concepts
1	<a href="https://github.com/duoan/TorchCode/blob/master/templates/01relu.ipynb" target="blank">ReLU</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/01relu.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`relu(x)`	🔥	Activation functions, element-wise ops
2	<a href="https://github.com/duoan/TorchCode/blob/master/templates/02softmax.ipynb" target="blank">Softmax</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/02softmax.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`my_softmax(x, dim)`	🔥	Numerical stability, exp/log tricks
16	<a href="https://github.com/duoan/TorchCode/blob/master/templates/16crossentropy.ipynb" target="blank">Cross-Entropy Loss</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/16crossentropy.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`cross_entropy_loss(logits, targets)`	🔥	Log-softmax, logsumexp trick
17	<a href="https://github.com/duoan/TorchCode/blob/master/templates/17dropout.ipynb" target="blank">Dropout</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/17dropout.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`MyDropout` (nn.Module)	🔥	Train/eval mode, inverted scaling
18	<a href="https://github.com/duoan/TorchCode/blob/master/templates/18embedding.ipynb" target="blank">Embedding</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/18embedding.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`MyEmbedding` (nn.Module)	🔥	Lookup table, `weight[indices]`
19	<a href="https://github.com/duoan/TorchCode/blob/master/templates/19gelu.ipynb" target="blank">GELU</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/19gelu.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`my_gelu(x)`	⭐	Gaussian error linear unit, `torch.erf`
20	<a href="https://github.com/duoan/TorchCode/blob/master/templates/20weightinit.ipynb" target="blank">Kaiming Init</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/20weightinit.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`kaiming_init(weight)`	⭐	`std = sqrt(2/fan_in)`, variance scaling
21	<a href="https://github.com/duoan/TorchCode/blob/master/templates/21gradientclipping.ipynb" target="blank">Gradient Clipping</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/21gradientclipping.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`clip_grad_norm(params, max_norm)`	⭐	Norm-based clipping, direction preservation
31	<a href="https://github.com/duoan/TorchCode/blob/master/templates/31gradientaccumulation.ipynb" target="blank">Gradient Accumulation</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/31gradientaccumulation.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`accumulated_step(model, opt, ...)`	💡	Micro-batching, loss scaling
40	<a href="https://github.com/duoan/TorchCode/blob/master/templates/40linearregression.ipynb" target="blank">Linear Regression</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/40linearregression.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`LinearRegression` (3 methods)	🔥	Normal equation, GD from scratch, nn.Linear
3	<a href="https://github.com/duoan/TorchCode/blob/master/templates/03linear.ipynb" target="blank">Linear Layer</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/03linear.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`SimpleLinear` (nn.Module)	🔥	`y = xW^T + b`, Kaiming init, `nn.Parameter`
4	<a href="https://github.com/duoan/TorchCode/blob/master/templates/04layernorm.ipynb" target="blank">LayerNorm</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/04layernorm.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`my_layer_norm(x, γ, β)`	🔥	Normalization, running stats, affine transform
7	<a href="https://github.com/duoan/TorchCode/blob/master/templates/07batchnorm.ipynb" target="blank">BatchNorm</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/07batchnorm.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`my_batch_norm(x, γ, β)`	⭐	Batch vs layer statistics, train/eval behavior
8	<a href="https://github.com/duoan/TorchCode/blob/master/templates/08rmsnorm.ipynb" target="blank">RMSNorm</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/08rmsnorm.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`rms_norm(x, weight)`	⭐	LLaMA-style norm, simpler than LayerNorm
15	<a href="https://github.com/duoan/TorchCode/blob/master/templates/15mlp.ipynb" target="blank">SwiGLU MLP</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/15mlp.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`SwiGLUMLP` (nn.Module)	⭐	Gated FFN, `SiLU(gate) * up`, LLaMA/Mistral-style
22	<a href="https://github.com/duoan/TorchCode/blob/master/templates/22conv2d.ipynb" target="blank">Conv2d</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/22conv2d.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`my_conv2d(x, weight, ...)`	🔥	Convolution, unfold, stride/padding

🧠 Attention Mechanisms — The heart of modern ML interviews

If you're interviewing for any role touching LLMs or Transformers, expect at least one of these.

#	Problem	What You'll Implement	Freq	Key Concepts
23	<a href="https://github.com/duoan/TorchCode/blob/master/templates/23crossattention.ipynb" target="blank">Cross-Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/23crossattention.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`MultiHeadCrossAttention` (nn.Module)	⭐	Encoder-decoder, Q from decoder, K/V from encoder
5	<a href="https://github.com/duoan/TorchCode/blob/master/templates/05attention.ipynb" target="blank">Scaled Dot-Product Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/05attention.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`scaled_dot_product_attention(Q, K, V)`	🔥	`softmax(QK^T/√d_k)V`, the foundation of everything
6	<a href="https://github.com/duoan/TorchCode/blob/master/templates/06multiheadattention.ipynb" target="blank">Multi-Head Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/06multiheadattention.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`MultiHeadAttention` (nn.Module)	🔥	Parallel heads, split/concat, projection matrices
9	<a href="https://github.com/duoan/TorchCode/blob/master/templates/09causalattention.ipynb" target="blank">Causal Self-Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/09causalattention.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`causal_attention(Q, K, V)`	🔥	Autoregressive masking with `-inf`, GPT-style
10	<a href="https://github.com/duoan/TorchCode/blob/master/templates/10gqa.ipynb" target="blank">Grouped Query Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/10gqa.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`GroupQueryAttention` (nn.Module)	⭐	GQA (LLaMA 2), KV sharing across heads
11	<a href="https://github.com/duoan/TorchCode/blob/master/templates/11slidingwindow.ipynb" target="blank">Sliding Window Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/11slidingwindow.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`sliding_window_attention(Q, K, V, w)`	⭐	Mistral-style local attention, O(n·w) complexity
12	<a href="https://github.com/duoan/TorchCode/blob/master/templates/12linearattention.ipynb" target="blank">Linear Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/12linearattention.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`linear_attention(Q, K, V)`	💡	Kernel trick, `φ(Q)(φ(K)^TV)`, O(n·d²)
14	<a href="https://github.com/duoan/TorchCode/blob/master/templates/14kvcache.ipynb" target="blank">KV Cache Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/14kvcache.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`KVCacheAttention` (nn.Module)	🔥	Incremental decoding, cache K/V, prefill vs decode
24	<a href="https://github.com/duoan/TorchCode/blob/master/templates/24rope.ipynb" target="blank">RoPE</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/24rope.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`apply_rope(q, k)`	🔥	Rotary position embedding, relative position via rotation
25	<a href="https://github.com/duoan/TorchCode/blob/master/templates/25flashattention.ipynb" target="blank">Flash Attention</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/25flashattention.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`flash_attention(Q, K, V, block_size)`	💡	Tiled attention, online softmax, memory-efficient

🏗️ Architecture & Adaptation — Put it all together

#	Problem	What You'll Implement	Freq	Key Concepts
26	<a href="https://github.com/duoan/TorchCode/blob/master/templates/26lora.ipynb" target="blank">LoRA</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/26lora.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`LoRALinear` (nn.Module)	⭐	Low-rank adaptation, frozen base + `BA` update
27	<a href="https://github.com/duoan/TorchCode/blob/master/templates/27vitpatch.ipynb" target="blank">ViT Patch Embedding</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/27vitpatch.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`PatchEmbedding` (nn.Module)	💡	Image → patches → linear projection
13	<a href="https://github.com/duoan/TorchCode/blob/master/templates/13gpt2block.ipynb" target="blank">GPT-2 Block</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/13gpt2block.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`GPT2Block` (nn.Module)	⭐	Pre-norm, causal MHA + MLP (4x, GELU), residual connections
28	<a href="https://github.com/duoan/TorchCode/blob/master/templates/28moe.ipynb" target="blank">Mixture of Experts</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/28moe.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`MixtureOfExperts` (nn.Module)	⭐	Mixtral-style, top-k routing, expert MLPs

⚙️ Training & Optimization

#	Problem	What You'll Implement	Difficulty	Freq	Key Concepts
29	<a href="https://github.com/duoan/TorchCode/blob/master/templates/29adam.ipynb" target="blank">Adam Optimizer</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/29adam.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`MyAdam`		⭐	Momentum + RMSProp, bias correction
30	<a href="https://github.com/duoan/TorchCode/blob/master/templates/30cosinelr.ipynb" target="blank">Cosine LR Scheduler</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/30cosinelr.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`cosine_lr_schedule(step, ...)`		⭐	Linear warmup + cosine annealing

🎯 Inference & Decoding

#	Problem	What You'll Implement	Freq	Key Concepts
32	<a href="https://github.com/duoan/TorchCode/blob/master/templates/32topksampling.ipynb" target="blank">Top-k / Top-p Sampling</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/32topksampling.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`sample_top_k_top_p(logits, ...)`	🔥	Nucleus sampling, temperature scaling
33	<a href="https://github.com/duoan/TorchCode/blob/master/templates/33beamsearch.ipynb" target="blank">Beam Search</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/33beamsearch.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`beam_search(log_prob_fn, ...)`	🔥	Hypothesis expansion, pruning, eos handling
34	<a href="https://github.com/duoan/TorchCode/blob/master/templates/34speculativedecoding.ipynb" target="blank">Speculative Decoding</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/34speculativedecoding.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`speculative_decode(target, draft, ...)`	💡	Accept/reject, draft model acceleration

🔬 Advanced — Differentiators

#	Problem	What You'll Implement	Freq	Key Concepts
35	<a href="https://github.com/duoan/TorchCode/blob/master/templates/35bpe.ipynb" target="blank">BPE Tokenizer</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/35bpe.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`SimpleBPE`	💡	Byte-pair encoding, merge rules, subword splits
36	<a href="https://github.com/duoan/TorchCode/blob/master/templates/36int8quantization.ipynb" target="blank">INT8 Quantization</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/36int8quantization.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`Int8Linear` (nn.Module)	💡	Per-channel quantize, scale/zero-point, buffer vs param
37	<a href="https://github.com/duoan/TorchCode/blob/master/templates/37dpoloss.ipynb" target="blank">DPO Loss</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/37dpoloss.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`dpo_loss(chosen, rejected, ...)`	💡	Direct preference optimization, alignment training
38	<a href="https://github.com/duoan/TorchCode/blob/master/templates/38grpoloss.ipynb" target="blank">GRPO Loss</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/38grpoloss.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`grpo_loss(logps, rewards, group_ids, eps)`	💡	Group relative policy optimization, RLAIF, within-group normalized advantages
39	<a href="https://github.com/duoan/TorchCode/blob/master/templates/39ppoloss.ipynb" target="blank">PPO Loss</a> <a href="https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/39ppoloss.ipynb" target="blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="20"></a>	`ppo_loss(new_logps, old_logps, advantages, clip_ratio)`	💡	PPO clipped surrogate loss, policy gradient, trust region

⚙️ How It Works

Each problem has two notebooks:

File	Purpose
`01_relu.ipynb`	✏️ Blank template — write your code here
`01_relu_solution.ipynb`	📖 Reference solution — check when stuck

Workflow

text

1. Open a blank notebook           →  Read the problem description
2. Implement your solution         →  Use only basic PyTorch ops
3. Debug freely                    →  print(x.shape), check gradients, etc.
4. Run the judge cell              →  check("relu")
5. See instant colored feedback    →  ✅ pass / ❌ fail per test case
6. Stuck? Get a nudge              →  hint("relu")
7. Review the reference solution   →  01_relu_solution.ipynb
8. Click 🔄 Reset in the toolbar  →  Blank slate — practice again!

In-Notebook API

python

from torch_judge import check, hint, status

check("relu")               # Judge your implementation
hint("causal_attention")    # Get a hint without full spoiler
status()                    # Progress dashboard — solved / attempted / todo

📅 Suggested Study Plan

Total: ~12–16 hours spread across 3–4 weeks. Perfect for interview prep on a deadline.

Week	Focus	Problems	Time
1	🧱 Foundations	ReLU → Softmax → CE Loss → Dropout → Embedding → GELU → Linear → LayerNorm → BatchNorm → RMSNorm → SwiGLU MLP → Conv2d	2–3 hrs
2	🧠 Attention Deep Dive	SDPA → MHA → Cross-Attn → Causal → GQA → KV Cache → Sliding Window → RoPE → Linear Attn → Flash Attn	3–4 hrs
3	🏗️ Architecture + Training	GPT-2 Block → LoRA → MoE → ViT Patch → Adam → Cosine LR → Grad Clip → Grad Accumulation → Kaiming Init	3–4 hrs
4	🎯 Inference + Advanced	Top-k/p Sampling → Beam Search → Speculative Decoding → BPE → INT8 Quant → DPO Loss → GRPO Loss → PPO Loss + speed run	3–4 hrs

🏛️ Architecture

text

┌──────────────────────────────────────────┐
│           Docker / Podman Container      │
│                                          │
│  JupyterLab (:8888)                      │
│    ├── templates/  (reset on each run)   │
│    ├── solutions/  (reference impl)      │
│    ├── torch_judge/ (auto-grading)       │
│    ├── torchcode-labext (JLab plugin)    │
│    │     🔄 Reset — restore template     │
│    │     🔗 Colab — open in Colab        │
│    └── PyTorch (CPU), NumPy              │
│                                          │
│  Judge checks:                           │
│    ✓ Output correctness (allclose)       │
│    ✓ Gradient flow (autograd)            │
│    ✓ Shape consistency                   │
│    ✓ Edge cases & numerical stability    │
└──────────────────────────────────────────┘

Single container. Single port. No database. No frontend framework. No GPU.

🛠️ Commands

bash

make run    # Build & start (http://localhost:8888)
make stop   # Stop the container
make clean  # Stop + remove volumes + reset all progress

🧩 Adding Your Own Problems

TorchCode uses auto-discovery — just drop a new file in torch_judge/tasks/:

python

TASK = {
    "id": "my_task",
    "title": "My Custom Problem",
    "difficulty": "medium",
    "function_name": "my_function",
    "hint": "Think about broadcasting...",
    "tests": [ ... ],
}

No registration needed. The judge picks it up automatically.

📦 Publishing `torch-judge` to PyPI (maintainers)

The judge is published as a separate package so Colab/users can pip install torch-judge without cloning the repo.

Automatic (GitHub Action)

Pushing to master after changing the package version triggers `.github/workflows/pypi-publish.yml`, which builds and uploads to PyPI. No git tag is required.

1.Bump version in torch_judge/_version.py (e.g. __version__ = "0.1.1").
2.Configure PyPI Trusted Publisher (one-time):
3.PyPI → Your project torch-judge → Publishing → Add a new pending publisher
4.Owner: duoan, Repository: TorchCode, Workflow: pypi-publish.yml, Environment: (leave empty)
5.Run the workflow once (push a version bump to master or Actions → Publish torch-judge to PyPI → Run workflow); PyPI will then link the publisher.
6.Release: commit the version bump and git push origin master.

Alternatively, use an API token: add repository secret PYPI_API_TOKEN (value = pypi-... from PyPI) and set TWINE_USERNAME=__token__ and TWINE_PASSWORD from that secret in the workflow if you prefer not to use Trusted Publishing.

Manual

bash

pip install build twine
python -m build
twine upload dist/*

Version is in torch_judge/_version.py; bump it before each release.

❓ FAQ

<details> <summary>Do I need a GPU?</summary> No. Everything runs on CPU. The problems test correctness and understanding, not throughput. </details>

<details> <summary>Can I keep my solutions between runs?</summary> Blank templates reset on every <code>make run</code> so you practice from scratch. Save your work under a different filename if you want to keep it. You can also click the 🔄 Reset button in the notebook toolbar at any time to restore the blank template without restarting. </details>

<details> <summary>Can I use Google Colab instead?</summary> Yes! Every notebook has an Open in Colab badge at the top. Click it to open the problem directly in Google Colab — no Docker or local setup needed. You can also use the Colab toolbar button inside JupyterLab. </details>

<details> <summary>How are solutions graded?</summary> The judge runs your function against multiple test cases using <code>torch.allclose</code> for numerical correctness, verifies gradients flow properly via autograd, and checks edge cases specific to each operation. </details>

<details> <summary>Who is this for?</summary> Anyone preparing for ML/AI engineering interviews at top tech companies, or anyone who wants to deeply understand how PyTorch operations work under the hood. </details>

Built for engineers who want to deeply understand what they build.

If this helped your interview prep, consider giving it a ⭐

☕ Buy Me a Coffee

Scan to support

</div>

🔥 TorchCode

🎯 Why TorchCode?

🚀 Quick Start

Option 0 — Try it online (zero install)

Option 0b — Use the judge in Colab (pip)

Option 1 — Pull the pre-built image (fastest)

Option 2 — Build locally

📋 Problem Set

🧱 Fundamentals — "Implement X from scratch"

🧠 Attention Mechanisms — The heart of modern ML interviews

🏗️ Architecture & Adaptation — Put it all together

⚙️ Training & Optimization

🎯 Inference & Decoding

🔬 Advanced — Differentiators

⚙️ How It Works

Workflow

In-Notebook API

📅 Suggested Study Plan

🏛️ Architecture

🛠️ Commands

🧩 Adding Your Own Problems

📦 Publishing torch-judge to PyPI (maintainers)

Automatic (GitHub Action)

Manual

❓ FAQ

☕ Buy Me a Coffee

📦 Publishing `torch-judge` to PyPI (maintainers)