Itô Training

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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.

Category: AI & Intelligent Agents
Repo: affaan-m-everything-claude-code
Path: skills/ito-training/SKILL.md
Updated: 8/9/2026, 10:15:25 PM
Open on GitHubHow to Install

AI Summary

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. It is useful for LLM applications, agent orchestration, RAG pipelines, AI evaluation, and multi-agent workflows. Source: affaan-m-everything-claude-code (skills/ito-training/SKILL.md).

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.

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