thesis-tracker 1.0.3

Push experiments, training curves, and figures (TensorBoard / wandb / matplotlib) into your self-hosted Thesis Tracker dashboard.

thesis-tracker (Python SDK)

Push ML experiments into the same dashboard as your papers and thesis plan.

The Thesis Tracker web app tracks literature, milestones, and report progress. This package connects your training scripts to that same Supabase database — runs, step-indexed curves, and figure artifacts show up under Experiments without a separate wandb/MLflow silo.

Why use it

Problem	This SDK
Experiment logs live in TensorBoard; thesis context lives elsewhere	One DB: link runs to related_paper, compare sweeps in the PWA
Wiring a custom API for every project	Same RLS + migrations as the web app — self-host once
Heavy MLOps platforms	Lightweight decorator + optional Lightning/Keras callbacks

Install

pip install thesis-tracker                 # core (supabase + httpx)
pip install 'thesis-tracker[figures]'      # matplotlib/Pillow artifacts
pip install 'thesis-tracker[tensorboard]'  # tbparse import
pip install 'thesis-tracker[wandb]'        # wandb import
pip install 'thesis-tracker[all,dev]'      # everything + pytest

Sync sources register at import time but stay unavailable until their extra is installed (clear error if you call one without deps).

Configure

One token auth: copy a bearer token from the Thesis Tracker dashboard (Settings → API token). The SDK sends it to your Thesis Tracker web app instance, which applies RLS using your session.

export THESIS_TRACKER_TOKEN=tt_...
# Optional; defaults to http://localhost:3000
export THESIS_TRACKER_API_URL=http://localhost:3000
export THESIS_TRACKER_PROJECT="My Thesis"   # or THESIS_TRACKER_PROJECT_ID=<uuid>

Apply migrations through at least 0017 (metrics + artifacts bucket) — see root README § Database.

Quick example

from thesis_tracker import track_experiment

@track_experiment(name="beta-vae sweep", config={"latent_dim": 32},
                  sync={"tensorboard": "runs/beta4"})
def train(run, beta=4.0):
    for step in range(100):
        run.log_metric("val_loss", loss(step), step=step)
    run.log_figure(fig, name="reconstruction")
    return {"val_loss": 0.11}

train(beta=4.0)

• @track_experiment — creates row (running), pins git state, logs metrics, uploads figures, sets done/failed on exit.
• with track(...) as run: — same without a decorator.
• Callbacks — thesis_tracker.integrations.lightning.ThesisTrackerCallback, .keras.ThesisTrackerCallback.

CLI

thesis-tracker list --project "My Thesis"
thesis-tracker import-tb runs/beta4 --name "beta-vae sweep"
thesis-tracker import-wandb entity/project/run_id

Extend (Open/Closed)

Implement MetricSource (id, available(), read(ref)), register on default_registry, use track(sync={"my_source": ref}). Example: examples/custom_source.py.

Architecture

Mirrors the web app: features/experiments/{domain,application,infrastructure}, container.py composition root, repository interfaces tested with in-memory fakes. No duplicate schema — migrations in ../supabase/migrations/ are the contract.

Test

pip install -e '.[dev]'
pytest   # offline; Supabase integration test skips without env creds

More

• Root pitch + web app: ../README.md
• Design principles: ../docs/DESIGN.md