Datasets:
File size: 2,894 Bytes
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license: apache-2.0
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- machine-learning
- distributed-training
- debugging
- observability
- anomaly-detection
- checkpointing
- flashback
size_categories:
- 10K<n<100K
configs:
- config_name: steps
data_files:
- split: train
path: steps/train-*.parquet
- split: test
path: steps/test-*.parquet
- config_name: runs
data_files:
- split: train
path: runs/train-*.parquet
- split: test
path: runs/test-*.parquet
---
# Flashback Forensics
Labelled telemetry from training runs that were **deliberately broken at a known
step**. Every row is a few hundred bytes of per-step summary statistics; the
label is the step at which the fault was actually injected.
The point of the dataset: to make "how early can you tell a run went wrong?"
a measurable question instead of an anecdote.
## Contents
| config | rows | one row is |
|---|---|---|
| `steps` | 21,600 | one training step of one run: 128 sketch metrics + labels |
| `runs` | 18 | one run: fault kind, ground-truth first-bad step, model config |
Splits are **by run**: no step of a test run appears in train.
## Fields (`steps`)
- `run_id`, `step` -- identity
- `fault_kind` -- one of `bit_flip`, `data_poison_heavy`, `data_poison_subtle`, `lowprec_overflow`, `lr_spike_loud`, `lr_spike_subtle`
- **128 metric columns** -- the Flashback sketch: per-group gradient and
update norms, maxima, variances, sign-flip rates, non-finite counters,
log-magnitude histograms, and gradient-norm quantiles.
Names follow `flashback.sketch.SketchSchema`:
scalars like `grad_norm`, group metrics like `group:attn_qkv:max_abs`,
histogram bins like `hist_update:15`.
- `is_after_first_bad` -- 1 from the injected step onward
- `steps_to_first_bad` -- signed distance to ground truth (negative = before)
## Baselines measured while building this dataset
| detector | mean |predicted - true| | never detected |
|---|---:|---:|
| loss curve alone | 0.5 | 6 / 18 |
| Flashback consensus bisect | 0.0 | 0 / 18 |
## Usage
```python
from datasets import load_dataset
ds = load_dataset("NagaYu/flashback-forensics", "steps", split="test")
runs = load_dataset("NagaYu/flashback-forensics", "runs", split="test")
```
To reproduce, or to generate more with different faults:
```bash
pip install flashback
python scripts/build_forensics.py --scale medium --push-to-hub <you>/flashback-forensics
```
## What is *not* here
No weights, no gradients, no training data -- only aggregate statistics.
The full state history lives in a Flashback delta store, which stays local.
## Generation
Model: `tiny` (0.53M parameters), 1200 steps, 6 fault
scenarios x 3 seeds. Faults: learning-rate spikes (loud and subtle),
single-bit gradient corruption, fp8-range overflow, and data poisoning at two
contamination levels. Generated by Flashback v0.1.0.
|