Buckets:
| license: openmdw-1.1 | |
| tags: | |
| - tabular-foundation-model | |
| - structured-data-models | |
| # Kumo Tabular | |
| Kumo Tabular is NVIDIA's pretrained tabular foundation model for classification and regression. | |
| ## Getting Started | |
| Install [`structured-data-models`](https://github.com/NVIDIA/structured-data-models) for inference: | |
| ```bash | |
| pip install structured-data-models | |
| ``` | |
| Use labeled examples as context to predict class probabilities for new data: | |
| ```python | |
| import torch | |
| from sklearn.datasets import load_breast_cancer | |
| import sdm | |
| df = load_breast_cancer(as_frame=True).frame | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| table = sdm.TableTensor.from_pandas( | |
| df=df, | |
| stypes=sdm.infer_stypes(df, overrides={"target": "categorical"}), | |
| device=device, | |
| ) | |
| model = sdm.models.KumoTabular(task="classification", device=device) | |
| with torch.amp.autocast( | |
| device.type, | |
| dtype=torch.float16, | |
| enabled=device.type == "cuda", | |
| ): | |
| probs = model( | |
| x_context=table[:300].drop_columns("target"), | |
| y_context=table[:300, "target"], | |
| x_query=table[300:].drop_columns("target"), | |
| num_estimators=8, | |
| ) | |
| print(probs) | |
| ``` | |
| To learn more, visit [structured-data-models](https://github.com/NVIDIA/structured-data-models). | |
| ## License | |
| Kumo Tabular weights are released under OpenMDW 1.1. | |
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