extract-tracker 0.2.2

Local-first experiment tracking for deep learning

Extract

Local-first experiment tracking for deep learning. Extract pairs a small Python SDK with a fast Rust TUI so you can log runs, watch training live, compare variants, inspect artifacts, and keep everything in a project-local SQLite store.

No hosted service. No daemon. No account. One .extract/ directory per project.

Why Extract

• Local-first by default — experiments live beside your code in .extract/.
• Hierarchical experiments — organize runs as benchmark > model > variant or any hierarchy you choose.
• Live terminal UI — stream curves during training, compare marked runs, browse artifacts, tags, notes, TODOs, lineage, and model registry entries.
• Separated metric surfaces — run.curve() stores dense per-step training series; run.log() stores headline metrics for summaries and rankings.
• Portable stores — sync with rsync, archive as tar.gz, or move one directory between machines.
• Agent-readable — optional read-only MCP server exposes experiments to Claude Code, Claude Desktop, and other MCP hosts.

Install

pip install extract-tracker

Then initialize a store in your project:

extract init

extract init writes .extract/config.toml and asks for your experiment hierarchy. Example hierarchy:

benchmark > model > variant

60-second quickstart

from extract import Store

store = Store()
exp = store.experiment({
    "benchmark": "imagenet",
    "model": "resnet50",
    "variant": "lr_0.01",
})

with exp.run(config={"lr": 0.01}, total_steps=1000) as run:
    for step in range(1000):
        loss, acc = train_step(...)
        run.curve(step=step, train_loss=loss, train_acc=acc)

    run.log(final_acc=acc, final_loss=loss)
    run.tag("baseline")
    run.note("Stable run; use as comparison anchor.")

Open another terminal while training runs:

extract tui

Curves update live. Mark runs with Space, press c to compare, press d to diff configs.

Core concepts

Store

A store is a project-local .extract/ directory:

.extract/
├── extract.db
├── config.toml
├── artifacts/
└── models/

SQLite uses WAL mode, so training scripts can write while the TUI reads.

Experiment

An experiment is a node in your configured hierarchy. With benchmark > model > variant, this call creates or reuses each path component:

exp = store.experiment({
    "benchmark": "mmlu",
    "model": "llama-3.2-1b",
    "variant": "lora-r16",
})

Run

A run is one execution under an experiment. Runs track config, status, hostname, git SHA, timestamps, tags, notes, metrics, artifacts, models, lineage, and TODOs.

with exp.run(config=config, name="seed-1", total_steps=len(loader)) as run:
    ...

Metrics

Use curve() for dense per-step values and log() for final or headline metrics.

run.curve(step=step, train_loss=loss, eval_acc=acc)  # live charts
run.log(best_acc=best_acc, final_loss=final_loss)    # summary/ranking columns

Artifacts and models

run.log_table("confusion_matrix", matrix)
run.log_text("notes", "Observed better calibration after warmup.")
run.register_model("resnet50", "v1", "checkpoints/best.pt", framework="pytorch")

CLI

extract init                             # create .extract/config.toml
extract tui                              # open Rust TUI
extract tui --store path/to/.extract     # browse another store
extract sync push user@hpc:/path/.extract/
extract sync pull user@hpc:/path/.extract/
extract sync export backup.tar.gz
extract sync import backup.tar.gz
python -m extract.mcp --store .extract   # read-only MCP server

TUI highlights

Key	Action
j / k	Move down / up
Enter	Expand / select
Space	Mark run for comparison
c	Compare marked runs
d	Diff marked run configs
r	Run browser
/	Search experiments and runs
t	Edit tags
n	Append note
M	Model registry
T	TODO view
L	Lineage DAG
?	Help
q	Quit

Full keymap: manual/tui.md.

Configuration

Edit .extract/config.toml:

[store]
hierarchy = "benchmark > model > variant"

[summary]
sections = ["runs", "metrics", "tables", "curves"]
curve_width = 80
curve_smooth = false

[metrics]
minimize = ["loss", "forgetting_rate"]
maximize = ["accuracy", "f1", "custom_score"]
order = "alpha"

[[tags.definitions]]
name = "baseline"
color = "blue"

[theme]
accent = "#89b4fa"
error = "#f38ba8"

Full config reference: manual/config.md.

Sync between machines

extract sync push user@hpc:/scratch/project/.extract/
extract sync pull user@hpc:/scratch/project/.extract/

Pull merges by experiment path and run ULID, so stores can move between laptop, workstation, and HPC jobs without a central server.

More: manual/sync.md.

MCP server

Expose your store to LLM agents with a read-only MCP server:

python -m extract.mcp --store .extract

Agents can list experiments, inspect runs, compare metrics, search tags/status, list TODOs, walk lineage, and read model registry metadata.

More: manual/mcp.md.

Development

Use Nix for project dependencies:

nix develop
pip install -e .
pytest python/tests

Build distributions locally:

python -m build
python -m twine check dist/*
pip install dist/*.whl
extract --help

Packaging and release notes: manual/packaging.md.

Project status

Extract is early-stage and optimized for local ML research workflows. Current package name is extract-tracker; Python import and CLI are both extract.

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