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Add curated educational datasets for TinyTorch milestones: TinyDigits (~310 KB): - 1000 train + 200 test samples of 8x8 digit images - Balanced: 100 samples per digit class (0-9) - Used by Milestones 03 (MLP) and 04 (CNN) - Created from sklearn digits, normalized to [0,1] TinyTalks (~40 KB): - 350 Q&A pairs across 5 difficulty levels - Character-level conversational dataset - Used by Milestone 05 (Transformer) - Designed for fast training (3-5 min on laptop) Both datasets follow Karpathy's ~1K samples philosophy: - Small enough to ship with repo - Large enough for meaningful learning - Fast training with instant feedback - Works offline, no downloads needed
70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
# TinyTorch Datasets
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This directory contains datasets for TinyTorch milestone examples.
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## Directory Structure
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```
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datasets/
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├── tinydigits/ ← 8×8 handwritten digits (ships with repo, ~310KB)
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├── tinytalks/ ← Q&A dataset for transformers (ships with repo, ~40KB)
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└── README.md ← This file
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```
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## Shipped Datasets (No Download Required)
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### TinyDigits
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- **Used by:** Milestones 03 & 04 (MLP and CNN examples)
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- **Contents:** 1,000 training + 200 test samples
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- **Format:** 8×8 grayscale images, pickled
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- **Size:** ~310 KB
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- **Purpose:** Fast iteration on real image classification
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### TinyTalks
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- **Used by:** Milestone 05 (Transformer/GPT examples)
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- **Contents:** 350 Q&A pairs across 5 difficulty levels
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- **Format:** Plain text (Q: ... A: ... format)
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- **Size:** ~40 KB
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- **Purpose:** Character-level conversational AI training
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## Downloaded Datasets (On-Demand)
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The milestones automatically download larger datasets when needed:
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### MNIST
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- **Used by:** `milestones/03_1986_mlp/02_rumelhart_mnist.py`
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- **Downloads to:** `milestones/datasets/mnist/`
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- **Contents:** 60K training + 10K test samples
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- **Format:** 28×28 grayscale images
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- **Size:** ~10 MB compressed
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- **Auto-downloaded by:** `milestones/data_manager.py`
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### CIFAR-10
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- **Used by:** `milestones/04_1998_cnn/02_lecun_cifar10.py`
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- **Downloads to:** `milestones/datasets/cifar-10/`
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- **Contents:** 50K training + 10K test samples
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- **Format:** 32×32 RGB images
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- **Size:** ~170 MB compressed
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- **Auto-downloaded by:** `milestones/data_manager.py`
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## Design Philosophy
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**Shipped datasets** follow Karpathy's "~1K samples" philosophy:
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- Small enough to ship with repo
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- Large enough for meaningful learning
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- Fast training (seconds to minutes)
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- Instant gratification for students
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**Downloaded datasets** are full benchmarks:
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- Standard ML benchmarks (MNIST, CIFAR-10)
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- Larger, slower, more realistic
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- Auto-downloaded only when needed
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- Used for scaling demonstrations
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## Total Repository Size
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- **Shipped data:** ~350 KB (tinydigits + tinytalks)
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- **USB-friendly:** Entire repo fits on any device
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- **Offline-capable:** Core milestones work without internet
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- **Git-friendly:** No large binary files in version control
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