Cómo realizar predicciones tabulares sin entrenamiento con los modelos de datos estructurados de NVIDIA

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In this tutorial, we implement Structured Data Models, NVIDIA’s new library that puts several pretrained foundation models for tables behind one PyTorch interface, and use it to make predictions without training. The library wraps TabICLv2 and KumoTabular for single tables and KumoRelational for whole relational databases, and each of them answers by in-context learning: we hand the model a set of labeled rows and the rows we want predicted, and it returns probabilities or a full predictive distribution in one forward pass. We start with the library’s typed table tensors, compare the two tabular models against trained scikit-learn baselines, reuse an encoded context across many queries, read calibrated prediction intervals off a regression, and scale to twenty classes. We then look at the places where the defaults quietly do the wrong thing: a preprocessing recipe that drops timestamp and text columns and an explainer with two traps in it, before building a small three-table shop database and predicting customer activity from it directly, including the one argument that separates an honest relational model from a leaky one. Check out the Full CODES here. Copy CodeCopiedUse a different Browserimport os import sys import time import textwrap import traceback import subprocess RESULTS = {} def banner(title): print("\n" + "=" * 78) print(title) print("=" * 78) def section(name): def wrap(fn): def run(*a, **kw): banner(name) try: out = fn(*a, **kw) RESULTS[name] = out if isinstance(out, str) else "ok" return out except Exception as e: RESULTS[name] = f"SKIPPED / FAILED -> {type(e).__name__}: {e}" print(f"\n[!] {name} did not complete: {type(e).__name__}: {e}") traceback.print_exc(limit=3) return None return run return wrap banner("1. Install SDM, and the one wheel URL its own