Datasets:

ppak10
/

Agentic-SLS-Database

Inova-Mk1-Database is the canonical graph dataset of Inova Mk1 SLS printer entities — jobs, print sessions, print profiles, and STL objects. Each entity is its own HuggingFace config; relationships are encoded as ID references in FK-style fields, not via an explicit edges.parquet.

Domain-specific datasets (Inova-Mk1-ASTM, future Inova-Mk1-Telemetry) reference rows here by ID and may embed frozen snapshots of the referenced state when standalone consumption matters more than join-freshness.

Ecosystem

Upstream raw: ppak10/SLS4All-Backup — full printer backup (PrintProfiles/, PrintSessions/, Jobs/, Objects/, etc.). This dataset is a curated, structured slice of that.
Downstream domain datasets:
- ppak10/Inova-Mk1-ASTM — ASTM mechanical-test specimens (D256/D638/D790) + Instron/TestWorks tensile test results. Rows embed printer-state snapshots (e.g. full print profile inline) so ML consumers don't need to cross-join.
- ppak10/Inova-Mk1-Telemetry (planned).
Pattern reference: ppak10/RocketReviews uses the same per-entity-config layout (one JSONL per entity, FK fields, scripts/{entity}/01_*.py). Match that pattern when adding configs here.

Architectural decisions

Per-entity HF configs over nodes+edges parquet. Each entity (jobs, sessions, etc.) is its own config_name in the README YAML pointing to its own data/{entity}.jsonl. Consumers do load_dataset("ppak10/Inova-Mk1-Database", "jobs"). The graph is implicit in FK fields, documented in README "Used By" prose.
No git submodules between HF dataset repos. HF's load_dataset doesn't follow submodules, so consumers gain nothing; the dependency direction is also wrong (Database is the leaf, not the parent). The umbrella role is filled by the recorder repo (Agentic-Additive-Manufacturing-Process-Optimization), which contains this repo, ASTM, and Telemetry as submodules under datasets/ (all update = none — init explicitly); the datasets stay siblings of each other there.
Snapshot vs reference is decided per consumer. This dataset stores references (IDs). Domain datasets choose whether to embed snapshots inline. Default for ML-facing domain datasets: embed the full profile so rows are self-contained.

Directory layout

source/             # raw printer files, mirroring SLS4All app subdirs
  Jobs/             # .s4a archives (= zip of STLs + .metadata.json)
  PrintSessions/    # per-run session JSONs
  PrintProfiles/    # material/energy profile JSONs
  Objects/          # STL library (Objects/ASTM/ + supporting)
scripts/{entity}/   # numbered ETL: 01_extract.py, 02_*.py, ...
data/{entity}.jsonl # HF-loadable artifact, one row per record

Scaffold: uv (pyproject.toml, .python-version = 3.10), stdlib only (zipfile, json, re).

File format notes

.s4a is just a zip. Contents: the STLs referenced by the job + a .metadata.json (~22 KB) embedding the full job state. Inspect with unzip -p file.s4a .metadata.json | jq.
.metadata.json PrintProfile field is already an ID reference, not a snapshot — i.e. AutomaticJob.PrintProfile = {"Id": "..."}. The full profile data lives only in the standalone source/PrintProfiles/*.json files.
AutomaticJob.NestingState.PrintProfile can be a different profile from AutomaticJob.PrintProfile — the nesting was computed against one profile, but a different profile may be assigned at print time. Sessions confirm this: the 2026-05-25 .s4a job is assigned 52715389 (1 Second Heat Time), but session 1 ran with 25682cd2 (100% Recycled), matching the nesting profile. Worth surfacing both if it ever matters.

Row shape — `jobs` config

{
  "source_file": "D256 and D638 2026_05_25.s4a",
  "print_date": "2026-05-25",
  "job_name": "D256 and D638",
  "print_profile_id": "52715389-d580-4be9-9194-ed300bdf911b",
  "objects": [
    {
      "object_file_id": "19b91ed5-d98b-4dee-929b-955b3d757ffc",
      "name": "d256_impact_specimen.STL",
      "hash": "AB07BE60702ECFC8F495612DF56280DB63050E72",
      "instance_count": 8,
      "scale": 100
    }
  ],
  "metadata": { "...full .metadata.json..." }
}

Conventions: light envelope (filename-derived fields at top level), id references surfaced at top level, full raw metadata retained for lossless replay.

IMPORTANT gotcha — object_file_id is per-job, not stable

AutomaticJob.Objects[] and AutomaticJob.ObjectFiles[] are linked only by Name. The same STL file gets a different ObjectFile.Id in each job's metadata — confirmed: d256_impact_specimen.STL is 19b91ed5-… in the 05-25 job and 692f3c49-… in the 05-30 job. The content hash (SHA1) is the stable identity. When building the objects config, key the node by hash, not by object_file_id. object_file_id is useful only as a per-job handle.

Current state

jobs config — implemented, 21 rows (scripts/jobs/01_extract.py → data/jobs.jsonl). Covers every 2026-dated print from 2026-05-25 through 2026-07-12 (including non-ASTM Hex Coasters / Nameplates / DragonBurner Cowl side prints).
Two dates carry two jobs each: 2026-06-27 (D790 and D638 and other objects + Hex Coasters and Nameplates and Benchies) and 2026-06-29 (Debug Run + Nameplate). Downstream lookups need to key by job_id or filter by object hash — a naive date-key lookup will pick whichever job happened to be extracted last.

Planned configs

Config	Source	Likely PK	FK refs
`sessions`	`source/PrintSessions/*.json`	`Id` (session UUID)	`job_id`, `print_profile_id`
`profiles`	`source/PrintProfiles/*.json`	`Id` (profile UUID)	—
`objects`	`source/Objects/*/.STL`	content `hash` (SHA1)	—

Scope note

Current source/ holds all 2026-dated prints from SLS4All-Backup: 21 jobs / 42 sessions / 15 profiles. That's every print the backup has for 2026, including the non-ASTM side jobs (Hex Coasters, Nameplates, DragonBurner Cowl, Bucky). 2025-dated jobs (Bookmarks, Trophy Prints, T-Rex, etc.) are intentionally out of scope — they predate the current ASTM/Telemetry data-collection period.

Loose STL geometry lives in source/Objects/ASTM/ and is still ASTM-only (4 files); the STLs used by each print job are embedded inside the corresponding .s4a archive, so the jobs config is self-contained. If a future objects config wants to enumerate all STLs, it will need to either extract from the .s4a files or import the broader SLS4All-Backup/Objects/ tree.

Backfill trail (chronological):

2026-07-11: imported 06-09 Cards + 06-13 Recycled Powder → ASTM batches F ← 06-13, G ← 06-09.
2026-07-12: full 2026 sweep + backup refresh through 07-12 → ASTM batches E D790 ← 06-07, H ← 06-24, I ← 06-25, J/J_MB ← 06-27 (the D790 and D638 and other objects job, disambiguated from same-day Hex Coasters via STL-content match in the ASTM _lib).

Adding a new config — recipe

mkdir scripts/{entity} and write 01_extract.py (stdlib-only, mirror scripts/jobs/01_extract.py layout: ROOT/SOURCE_DIR/OUTPUT_FILE constants, build_row(), main()).
Row shape: top-level filename-derived envelope fields + FK ids + nested metadata blob. Surface IDs at top level for HF-side filtering; keep the raw blob for lossless replay.
Run script → data/{entity}.jsonl.
Update README.md configs: block: add config_name: {entity} pointing to the new JSONL.
Add the entity row to the README's Configs table.

CLAUDE.md

What this dataset is