/ito-training

name: ito-training
description: Run an ML training job on a completed Itô compute booking through the canonical Itô backend. Use after ito-compute has booked GPU nodes and the user wants pre-training, fine-tuning, or RL on that metal. Chains off a booking record; ECC implements no training stack of its own.
metadata:
  origin: ECC

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:

1. Data prep — manifest, dedup, decontamination against the eval suite; 150M-ladder decision job as the cheap pre-check for custom data. 2. Parallelism and precision — selected from model size, node count, fabric; wasteful combinations refused. 3. Checkpointing and fault tolerance — async DCP, torchft; detect < 10 min, resume < 15 min. Loss-spike restart is a proposed, human-gated action. 4. Curriculum and eval gates — staged pretrain / mid-train / long-context / post-training, each with a fixed eval battery; a failed gate stops the run. 5. 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.