pandm 0.2.0

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

Training scripts never change — sign in once per machine and pandm.init() dual-writes: local stays the source of truth, a background thread syncs to the server, and anything logged offline is backfilled on reconnect. Delivery is exact-once (re-pushes are deduped server-side).

pandm login https://pandm.example.com   # browser approval, like `gh auth login`
python train.py                          # local + cloud
pandm sync                               # backfill runs whose process already exited

Each user signs in with GitHub and sees only their own runs. Two interchangeable server implementations speak the same protocol — the full walkthrough (OAuth App, custom domain, backups, troubleshooting) is in docs/deploy.md:

Cloudflare Workers (serverless: D1 for metrics, R2 for media — workers/):

cd workers && pnpm install
npx wrangler d1 create pandm             # paste the database_id into wrangler.jsonc
npx wrangler secret put GITHUB_CLIENT_ID     # OAuth App callback: https://<domain>/api/auth/callback
npx wrangler secret put GITHUB_CLIENT_SECRET
npx wrangler secret put PANDM_SECRET_KEY     # e.g. `openssl rand -hex 32`
npx wrangler d1 migrations apply pandm --remote
pnpm run deploy

Note: D1 bills per row written (100k/day free). Logging ~10 metrics/sec around the clock lands in the paid tier — a few dollars a month.

Self-hosted Python server (same binary as pandm ui):

GITHUB_CLIENT_ID=… GITHUB_CLIENT_SECRET=… docker compose up -d   # multi-user mode

Without OAuth env vars the server falls back to single-tenant mode — pandm server --api-key my-secret plus PANDM_REMOTE/PANDM_API_KEY on the client (remote-only, no local copy, no accounts).

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
cd workers && pnpm install && pnpm test   # cloudflare workers server (contract tests)

License

MIT