Itô Training
Run training work on rented Itô metal by delegating to the canonical Itô compute
backend (Layer 0.3). ECC does not implement a parallel training stack, trainer,
or scheduler, and does no browser automation. This skill chains off a
completed booking from ito-compute; it never books, reserves, or spends.
Prerequisite
A completed booking from the ito-compute skill (booking id, node IPs, SSH,
GPU SKU, node count, fabric) in harness memory. Without one, stop.
Delegation
ECC calls the canonical backend through the ecc ito bridge; it never
re-implements training. Authenticate once with ecc ito login, as
ito-compute documents. Never put a key or token in arguments, files, logs, or
chat.
ecc ito train \
--booking <booking-id> \
--model-size <e.g. 8B> \
--data <data-ref> \
--target <capability> \
--budget-usd <ceiling> \
[--post-training sft|dpo|rlvr]
What the backend does (Layer 0.3)
The desk backend runs a staged, eval-gated pipeline; this skill reports stage gates and never overrides one:
- Data prep — manifest, dedup, decontamination against the eval suite; 150M-ladder decision job as the cheap pre-check for custom data.
- Parallelism and precision — selected from model size, node count, fabric; wasteful combinations refused.
- Checkpointing and fault tolerance — async DCP, torchft; detect < 10 min, resume < 15 min. Loss-spike restart is a proposed, human-gated action.
- Curriculum and eval gates — staged pretrain / mid-train / long-context / post-training, each with a fixed eval battery; a failed gate stops the run.
- Post-training — SFT → DPO → RLVR (GRPO with DAPO stability fixes), trainer/rollout separation with bounded staleness.
Emits desk telemetry (goodput, interruption rate, checkpoint bandwidth) so the desk prices training blocks honestly.
Unavailable today
Not yet wired: the canonical CLI's run verb and the desk training-run
backend are scaffolds. Until they land, this skill reports the missing
capability and stops. Never substitute a local trainer or a purchase endpoint.