2nd/10_Wiki/Topics/AI_and_ML/Seed.md

---
id: wiki-2026-0508-seed
title: Seed
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Random Seed, RNG Seed, Reproducibility Seed]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [reproducibility, random, ml-training, image-gen, determinism]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: PyTorch
---

# Seed

## 매 한 줄
> **"매 seed 는 reproducibility 의 anchor — 매 same seed + same code + same hardware → same result"**. 매 origin 은 von Neumann 1949 mid-square method, 매 modern state 는 ML training (PyTorch, JAX), image gen (Stable Diffusion, FLUX 의 seed lock), 그리고 매 paper reproducibility 의 standard practice.

## 매 핵심

### 매 seed 가 영향 주는 곳
- **Data shuffling**: DataLoader sampler order.
- **Weight init**: Xavier/He 의 random.
- **Augmentation**: random crop/flip/color.
- **Dropout / BatchNorm noise**: training 시 stochastic.
- **Image gen**: latent noise (z) sampling.
- **MC simulation**: Monte Carlo sample order.

### 매 hardware non-determinism (매 seed 의 한계)
- **CUDA atomics**: scatter_add 등 floating-point atomic 의 비결정적 order.
- **cuDNN heuristic**: convolution 의 algo 선택.
- **TF32 / mixed precision**: FP rounding 차이.
- **Multi-GPU all-reduce**: NCCL ring order.
- → 매 seed 만으로 부족, `deterministic=True` 필요.

### 매 응용
1. ML training reproducibility (paper).
2. Image gen 의 seed lock (consistent character, A/B test).
3. Statistical simulation (bootstrap, MC).
4. Bug reproduction (flake → 매 seed pin).

## 💻 패턴

### 매 PyTorch full reproducibility (2026)
```python
import os, random
import numpy as np
import torch

def seed_everything(seed: int = 42):
    os.environ["PYTHONHASHSEED"] = str(seed)
    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"  # 매 cublas 결정적
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    # 매 cuDNN 결정적 (매 속도 trade-off)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    # 매 PyTorch 2.x deterministic algorithms
    torch.use_deterministic_algorithms(True, warn_only=True)

seed_everything(42)
```

### 매 DataLoader seed (매 worker 마다 다른 seed)
```python
def worker_init_fn(worker_id):
    seed = torch.initial_seed() % 2**32
    np.random.seed(seed + worker_id)
    random.seed(seed + worker_id)

g = torch.Generator()
g.manual_seed(42)

loader = torch.utils.data.DataLoader(
    dataset,
    batch_size=32,
    shuffle=True,
    num_workers=4,
    worker_init_fn=worker_init_fn,
    generator=g,
)
```

### 매 Stable Diffusion / FLUX 의 seed lock
```python
import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
).to("cuda")

prompt = "A cyberpunk samurai at neon market, 4k photo"

# 매 same seed → same image (same hardware)
gen = torch.Generator(device="cuda").manual_seed(20260510)
img = pipe(prompt, generator=gen, num_inference_steps=28).images[0]
img.save("samurai_seed20260510.png")

# 매 seed sweep — 매 character consistency 찾기
for s in range(1000, 1010):
    g = torch.Generator(device="cuda").manual_seed(s)
    pipe(prompt, generator=g).images[0].save(f"sweep_{s}.png")
```

### 매 JAX (functional seed, 매 split)
```python
import jax
import jax.numpy as jnp

key = jax.random.PRNGKey(42)
key, subkey1, subkey2 = jax.random.split(key, 3)

x = jax.random.normal(subkey1, (1000, 128))
y = jax.random.normal(subkey2, (1000,))

# 매 매 functional — 매 implicit global state X
# 매 same key chain → exact same numbers
```

### 매 numpy 의 새 generator API (post-1.17)
```python
import numpy as np

# 매 legacy (매 global, 매 thread-unsafe)
np.random.seed(42); np.random.randn(3)  # 매 권장 X (in 2026)

# 매 modern: explicit Generator
rng = np.random.default_rng(seed=42)
rng.standard_normal(3)         # array([ 0.30471708, -1.03998411, ...])
rng.choice([1,2,3], size=10)
```

### 매 JS (web 의 seedable, Math.random 은 X)
```js
// 매 seedrandom (매 V8 Math.random 은 seedable X)
import seedrandom from "seedrandom";

const rng = seedrandom("2026-05-10");
console.log(rng());        // 매 deterministic
console.log(rng.int32());  // 매 deterministic int
```

### 매 reproducibility checklist (매 paper / experiment)
```python
# 매 매 run 시작 시 dump:
import torch, sys, json, hashlib

manifest = {
    "seed": 42,
    "python": sys.version,
    "torch": torch.__version__,
    "cuda": torch.version.cuda,
    "cudnn": torch.backends.cudnn.version(),
    "gpu": torch.cuda.get_device_name(0) if torch.cuda.is_available() else None,
    "code_sha": _git_sha(),
    "data_sha": hashlib.sha256(open("data.bin","rb").read()).hexdigest(),
    "hyperparams": {"lr": 3e-4, "batch": 64, "epochs": 30},
}
with open("run_manifest.json","w") as f:
    json.dump(manifest, f, indent=2)
```

### 매 multi-seed eval (매 paper 의 robust 결과)
```python
results = []
for seed in [42, 123, 2024, 31337, 7]:
    seed_everything(seed)
    model = train()
    acc = evaluate(model)
    results.append(acc)

# 매 report mean ± std (NOT single-seed best)
print(f"Acc = {np.mean(results):.3f} ± {np.std(results):.3f} (n=5 seeds)")
# 매 매 single-seed claim 은 매 reviewer 가 reject.
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 paper experiment | seed_everything + multi-seed (≥3) + manifest dump |
| 매 image gen consistency | seed lock + sweep |
| 매 prod ML training | seed + log, 매 deterministic 의 perf cost 고려 |
| 매 hyperparam sweep | seed pin per run, vary hyperparam |
| 매 MC simulation | seed log per run, 매 reproducible |

**기본값**: `seed_everything(42)` + manifest JSON + 매 paper claim 매 multi-seed mean±std.

## 🔗 Graph
- 부모: [[Reproducibility]]
- 응용: [[Monte Carlo]]

## 🤖 LLM 활용
**언제**: 매 LLM의 `seed` param (OpenAI 의 `seed` arg, Anthropic 의 `temperature=0` 근사) — 매 partial reproducibility. 매 prompt 의 deterministic eval.
**언제 X**: 매 LLM 은 매 fully reproducible X (provider routing, kernel non-determinism). 매 expectation 조정.

## ❌ 안티패턴
- **Single-seed paper**: 매 매 result fragility. 매 N≥3 seed report.
- **Seed pin without manifest**: 매 hardware/lib 변경 시 깨짐.
- **Forget DataLoader workers**: 매 worker 의 random 따로 — 매 worker_init_fn 필요.
- **`np.random.seed` global**: 매 thread-unsafe — 매 `default_rng` 사용.
- **Determinism off-by-default**: 매 cuDNN benchmark=True 면 매 결과 다름.

## 🧪 검증 / 중복
- Verified (PyTorch reproducibility docs 2026, JAX PRNG design notes, Pineau "ML Reproducibility Checklist" NeurIPS).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — PyTorch + JAX + FLUX seed + multi-seed eval |