publish: hmode_89, te_ped_89, ti_ped_89, t_rot_ped_89, edensfit89 (migrate flat root into edensfit89/)

63d77f8 verified 5 months ago

3.58 kB

license: apache-2.0
tags:
  - fusion
  - tokamak
  - diii-d
  - pedestal
  - onnx
library_name: onnx

edensfit89

Consumer note. Most users should not load this bundle's ONNX files directly. Use the PedestalEnsemble Python wrapper shipped in the PedestalPredictor GitHub repo, which loads all five bundles with one call and exposes a unified predict_one(...) API. Direct ONNX access documented below is for advanced users who want to integrate a single bundle into an existing ONNX-only pipeline.

Summary

Task: regression
Target: edens_ped
Dataset version: v1_446 (446-dim MSE history)
FPE signal dim: 32
Exported at: 2026-04-23T18:45:12.072496+00:00
torch / onnx: 2.8.0 / 1.19.0 (opset 17)
Git SHA at export: 5aceecf48dbf8696a4801a8d73bf5708c04d3b5b

Output interpretation (regression)

The FPE graph emits a scalar prediction per time step in z-scored edens_ped units. To recover physical units:

# target_mean=2.580092217753647, target_std=1.606271753311305
y_phys = pred * target_std + target_mean

Validation tolerance note: RMS tolerance 1e-3 in normalized units ≈ 1.61e-03 (normalized units) for this target (target_std=1.6063).

Input contract- MSE history: `(batch, 50, 446)` — stats per shot.- MSE mask: `(batch, 50)` — 1.0 = valid, 0.0 = padding.- MSE aux: `(batch, 3)` — `bzn_seconds`, `disrupt_seconds`, `disrupt_coverage`- FPE sequences: `(batch, seq_len, 32)` — z-scored signals.- FPE signal mask: `(batch, 32)` — per-channel availability.- FPE padding mask: `(batch, seq_len)` — 1.0 = valid.See `normalization_params.json` for the exact z-score means andstds used at training time; the per-channel order matches the`fpe_signal_names` list below.
FPE signal names (in channel order)

idx	signal
0	`pohm`
1	`pinj`
2	`tinj`
3	`ech_total`
4	`f1a`
5	`f2a`
6	`f3a`
7	`f4a`
8	`f5a`
9	`f6a`
10	`f7a`
11	`f8a`
12	`f9a`
13	`f1b`
14	`f2b`
15	`f3b`
16	`f4b`
17	`f5b`
18	`f6b`
19	`f7b`
20	`f8b`
21	`f9b`
22	`ecoila`
23	`ecoilb`
24	`gasa_cal`
25	`gasb_cal`
26	`gasc_cal`
27	`gasd_cal`
28	`gase_cal`
29	`ip`
30	`ipspr15v`
31	`bt`

Files

File	Purpose
`mse_encoder.onnx`	Machine-state encoder graph (opset 17)
`fpe_encoder.onnx`	Fast-physics encoder graph
`model_config.json`	Architecture + task metadata (this card's authoritative source)
`provenance.json`	Export-time torch/onnx versions, git SHA, sidecar hashes
`normalization_params.json`	Per-channel z-score means + stds for FPE inputs
`target_norm.json`	`target_mean` / `target_std` (+ optional `clip_min`/`clip_max`) for de-normalizing regression outputs

Validation

Each bundle ships with both random-tensor and real-sample validation. On the PedestalPredictor GitHub repo, run:

python -m inference.validate_onnx \
    --model-dir <trial-dir> \
    --onnx-dir onnx_models/edensfit89 \
    --dataset-dir <dataset> \
    --dataset-cls regression \
    --num-samples 10

(See docs/export_and_publish.md in the GitHub repo for exact per-bundle invocations.)

License

Licensed under APACHE 2.0 (see repo root).