pandm 0.1.0

Beautiful, local-first experiment tracking. A lightweight alternative to wandb / tensorboard.

pandm

pandm tracks ML experiments locally. The Python SDK writes metrics and images straight to a .pandm/ directory next to your code — no account, no daemon, no cloud — and pandm ui serves a dashboard to compare runs. Unlike wandb there is nothing to sign up for, and unlike tensorboard the data is plain SQLite + PNG files you can query yourself. The same scripts report to a shared server over HTTP when you set one env var.

Install

pip install pandm

Quick start

import pandm

run = pandm.init(project="mnist", config={"lr": 1e-3, "batch_size": 64})
for step in range(1000):
    loss, acc = train_step()
    run.log({"train/loss": loss, "train/acc": acc}, step=step)
    if step % 100 == 0:
        run.log_image("samples", sample_grid, step=step)  # PIL / numpy / torch / path
run.finish()

pandm ui   # opens http://127.0.0.1:7878

The dashboard overlays selected runs per metric, with smoothing, log scale, step/time axes, an image browser with a step slider, and a config/summary comparison table. It polls while runs are alive, so curves grow during training.

Usage

step is optional (an internal counter is used). Runs end as finished or crashed: uncaught exceptions are detected via sys.excepthook (and the context manager), and hard-killed processes (kill -9, OOM) are presumed crashed once their 15s heartbeat goes quiet for 60s — self-healing if the process was merely suspended.

with pandm.init(project="mnist") as run:
    run.log({"loss": 0.5})

List or delete runs from the terminal:

pandm ls
pandm delete <run_id>

Data lives in ./.pandm by default; override with --dir or PANDM_DIR.

Cloud mode

Run a server anywhere, then point training scripts at it — no code changes:

pandm server --api-key my-secret               # on the server (default port 7878)

export PANDM_REMOTE=http://my-host:7878
export PANDM_API_KEY=my-secret
python train.py                                 # same script, now reports over HTTP

The API key protects write endpoints only; put the server behind a reverse proxy if reads need auth too. If the server becomes unreachable mid-run, the SDK warns and keeps training: it retries every 30s, replays run creation on recovery, and drops whatever was logged while offline.

API

pandm.init(project, name=None, config=None, *, directory=None, remote=None, api_key=None)	start a run
run.log(metrics, step=None)	log scalar metrics
run.log_image(key, image, step=None, caption=None)	log an image
run.finish(status="finished")	end the run (also via atexit)
GET /api/docs	REST API reference on any running server

Development

uv sync && uv run pytest          # python sdk + server
cd web && pnpm install && pnpm dev   # dashboard dev server (proxies to :7878)
pnpm build                        # bundles the dashboard into src/pandm/static

License

MIT