| --- |
| 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. |
|
|
