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examples/quickstart.ipynb runs the quickstart end to end with its outputs kept: install tabctx[standalone,tabicl,client], start python -m tabctx.serve in the background (TABCTX_BACKEND=tabicl), fit credit-g with TabctxClassifier and predict, fit a regressor and show predict_quantiles, then list and delete the contexts and stop the server. An Open in Colab badge linking to the notebook is added to the README Quickstart section and to docs/getting-started/quickstart.md.
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Closes #113
What this adds
examples/quickstart.ipynb— the quickstart as a notebook, committed with its outputs kept from a full end-to-end run:pip install "tabctx[standalone,tabicl,client]"python -m tabctx.servein the background (TABCTX_BACKEND=tabicl) and waits for/v1/modelsTabctxClassifier, prints the context id, scores (0.772 in the kept run), and showspredict_probaTabctxRegressoron the diabetes dataset (bundled with scikit-learn, no extra download) and showspredict_quantilesas a small table (q5/q50/q95)client.datasets(), deletes both contexts, and shows the empty listdocs/getting-started/quickstart.md.Verification
The notebook was executed top to bottom locally on CPU (Python 3.12, fresh venv, weights downloaded on first server start) with zero cell errors; every output in the committed file is from that run — classification accuracy 0.772, quantile columns strictly increasing per row, both context ids listed and then deleted. The flow mirrors the README/docs quickstart exactly, so "Run all" reproduces it on a fresh Colab CPU runtime.