README.md

metadata

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

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:

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.