experiment-registry

name	experiment-registry
description	Manage ML experiment lifecycle via structured YAML registry — register, record results, compare runs, track status. Triggers: 'register experiment', 'compare experiments', 'experiment results', 'exp init', 'exp list', '注册实验', '对比实验', '查实验', '哪个实验最好', '记录结果'. Not for training monitoring, checkpoints, or launching jobs.

Experiment Registry

Structured YAML experiment registry for ML research. YAML was chosen over databases or JSON because experiment files should be human-readable, git-diffable, and hand-editable — researchers often need to inspect or tweak entries directly.

Prerequisites

The exp CLI must be installed:

pip install exp-registry

If exp is not found, install it first. If the project has no exp.config.yaml, run exp init before any other command.

How to Think About Experiment Registry

This tool manages the metadata layer of experiments — what was run, with what config, and what results came out. It does NOT manage training code, checkpoints, or logs.

The mental model: each experiment gets a YAML file that serves as its "identity card." Over time, benchmark results accumulate in that file as the experiment progresses through training steps. When it's time to report or decide next steps, you compare across experiments.

Decision Flow

When the user mentions experiments, follow this flow:

1. Check environment first. Does exp.config.yaml exist? If not, ask if they want to initialize (exp init). Don't silently init — the user should confirm the project root.

2. Understand intent. Map the user's request to the right action:

   • "New experiment" / "register" → exp register (but first exp list to check for ID conflicts)
   • "What's running" / "experiment status" → exp list (suggest filters if there are many)
   • "Record results" / "add benchmark" → exp add-benchmark (ask for dataset and step if not provided)
   • "Compare" / "which is better" → exp compare (auto-discover shared datasets from exp show)
   • "What happened with exp07" → exp show first, then summarize findings

3. Be proactive with context. After any operation, offer useful next steps:

   • After registering → "Ready to add benchmarks when results come in"
   • After adding a benchmark → suggest comparing with related experiments if they exist
   • After comparing → highlight the best performer and surface any findings

Smart Comparison

When the user asks "compare experiments" or "which is better," don't just dump the table. First run exp show on each experiment to discover which datasets and eval_modes they share, then run exp compare on those shared dimensions. If experiments have no common benchmarks, tell the user — don't produce an empty comparison silently.

Series Grouping

Experiment IDs like exp07a, exp07b, exp07c automatically group into series exp07. This lets you filter by series to see all variants of one experimental idea. When a user discusses "the exp07 experiments," use exp list --series exp07 to get the full picture.

Command Reference

Task	Command
Initialize	exp init
List all	exp list
Filter by status	exp list --status completed
Filter by type	exp list --type rl
Filter by series	exp list --series exp07
Show details	exp show <id>
JSON output	exp show <id> --json
Register new	exp register <id> --type rl --model Qwen3-VL-8B (auto-records git HEAD; --commit <sha> to override)
Add benchmark	exp add-benchmark <id> --dataset mmlu --eval-mode cot --samples 100 --step 50 --extra acc=0.72
Compare	exp compare <id1> <id2> --dataset mmlu
Update status	exp update <id> --status completed
Add finding	exp update <id> --finding "key insight"

All list/show/compare commands support --json for machine-readable output.

Typical Workflows

Experiment Lifecycle

register → (train) → add-benchmark at step N → add-benchmark at step M → update status + finding

Example:

1. exp register exp01 --type rl --model Qwen3-VL-8B --reward reward_tool_strict
2. (user trains the model)
3. exp add-benchmark exp01 --dataset zebra-cot --eval-mode agent --samples 50 --step 50 --extra text_only=0.42 with_tools=0.52
4. exp add-benchmark exp01 --dataset zebra-cot --eval-mode agent --samples 50 --step 90 --extra text_only=0.44 with_tools=0.55
5. exp update exp01 --status completed --finding "tool use improves reasoning"

Benchmarks are organized by step number within each dataset — this tracks how performance evolves during training, which is critical for deciding when to stop or which checkpoint to use.

Compare for Meeting/Report

exp compare exp07a exp07b exp07c --dataset zebra-cot

Outputs a Markdown table — paste directly into docs or slides.

Project Configuration

exp.config.yaml at project root:

registry_dir: experiments/           # where YAML files live
paths_template:
  local: outputs/{id}/               # {id} is replaced with experiment ID
defaults:
  type: rl                           # default for `exp register`
types:
  rl:
    fields: [model, config, script, reward]
  sft:
    fields: [model, config, script, base_model]

Config is discovered by walking up from CWD. No config = sensible defaults (registry_dir: experiments/).

YAML Schema

Each experiment is one file in <registry_dir>/<exp_id>.yaml:

Required fields: id, name, type, series, date, status

Auto-generated: series (inferred from ID prefix), paths (from template), date (today)

Structured benchmarks:

benchmarks:
  - dataset: string
    eval_mode: string
    samples: int
    steps:
      <step_number>: { <metric>: <value>, ... }

Error Recovery

Error	Cause	What to Do
"experiment already exists"	Duplicate ID	exp show <id> to check, use a different ID
"experiment not found"	Wrong ID	exp list to see available IDs
"No benchmark data found"	Wrong dataset in compare	exp show <id> to check available datasets
"Missing required fields"	Corrupted YAML	Inspect and fix the YAML file directly — it's designed to be human-editable