AI 原生开放教材

外观

The Context Course系统课程·英文原文 ↗原件 ↓仓库 ↗

OpenCode Workflow ​

OpenCode is the canonical control plane for this repo.

The benchmark surface is unchanged:

  • train.py is the experiment file
  • prepare.py is read-only
  • Hugging Face Jobs is the only remote execution path
  • Trackio is the only remote observability path
  • research/results.tsv is the append-only local run ledger
  • train_orig.py plus research/live/ define the current promoted local master

Repo Surface ​

  • opencode.json Repo-local OpenCode config, including disabled-by-default MCP entries for Hugging Face and Trackio.
  • .opencode/agent/ The checked-in OpenCode agents: autolab, planner, experiment-worker, reviewer, memory-keeper, researcher, and reporter.
  • .agents/skills/ Shared repo-local skills that OpenCode can load on demand.
  • research/templates/ Canonical campaign, experiment, and do-not-repeat templates.
  • scripts/opencode_worker.py Creates isolated worktrees and launches experiment-worker runs.
  • scripts/print_opencode_kickoff.py Prints a standard parent-session kickoff prompt.

Setup ​

  1. Install dependencies:
bash
uv sync
  1. Create a local operator env file:
bash
mkdir -p ~/.autolab
cp .autolab.credentials.example ~/.autolab/credentials
$EDITOR ~/.autolab/credentials
. ~/.autolab/credentials
  1. Authenticate Hugging Face:
bash
hf auth login
  1. Authenticate OpenCode and select a model.

Default provider path:

bash
opencode auth login
opencode
# inside OpenCode:
/models

Choose Hugging Face and then select an open model through Hugging Face Inference Providers. Do not pin a single model in repo config.

  1. Validate the environment:
bash
bash scripts/bootstrap_public.sh
  1. Warm the shared HF cache once:
bash
uv run scripts/hf_job.py launch --mode prepare

Parent Session Workflow ​

  1. Refresh current local benchmark truth:
bash
uv run scripts/refresh_master.py --fetch-dag
  1. Review the current notebook:
  • research/notes.md
  • research/do-not-repeat.md
  • research/campaigns/
  • research/experiments/
  • research/results.tsv
  • research/live/master.json
  • research/live/dag.json
  1. Print a kickoff prompt if you want one:
bash
uv run scripts/print_opencode_kickoff.py --gpu-slots 1
  1. Start OpenCode in the pre-training project root:
bash
opencode
  1. Use the autolab primary agent. Delegate to:
  • planner for fresh experiment queues
  • reviewer for rule and comparability checks
  • researcher for paper-derived ideas
  • reporter for Trackio and HF Jobs status
  • memory-keeper after each worker finishes

Isolated Worker Workflow ​

Create one isolated worktree per experiment:

bash
uv run scripts/opencode_worker.py create exp-warmdown-20 \
  --campaign "schedule: shorter cooldowns" \
  --hypothesis "Shorten warmdown to test whether the long cooldown tail is wasting the fixed budget." \
  --worker-id worker-0

This creates: