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การเข้าถึงหน้านี้ต้องได้รับการอนุญาต คุณสามารถลอง ลงชื่อเข้าใช้หรือเปลี่ยนไดเรกทอรีได้
การเข้าถึงหน้านี้ต้องได้รับการอนุญาต คุณสามารถลองเปลี่ยนไดเรกทอรีได้
Important
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These examples run classic machine learning tasks on AI Runtime. They show how to use GPU acceleration for traditional ML algorithms, tabular foundation models, and time series forecasting, including XGBoost regression, zero-shot prediction with TabFM, and probabilistic forecasting with GluonTS.
| Example | Interface | Description |
|---|---|---|
| XGBoost model training | Notebook | This notebook demonstrates how to train an XGBoost regression model on a single GPU. XGBoost can significantly benefit from GPU acceleration for large datasets. |
| Tune XGBoost hyperparameters with Optuna | Notebook | This notebook demonstrates hyperparameter optimization for an XGBoost classification model on a single GPU using Optuna and MLflow experiment tracking. |
| Zero-shot tabular predictions with TabFM | Notebook | This notebook demonstrates zero-shot classification and regression with Google's TabFM tabular foundation model. TabFM uses in-context learning to make predictions in a single forward pass, with no fine-tuning or dataset-specific training. |
| Time series forecasting with GluonTS | Notebook | This notebook demonstrates an end-to-end workflow for probabilistic time-series forecasting of electricity consumption data with GluonTS's DeepAR model on a serverless GPU cluster. It covers data ingestion, resampling, model training, prediction, visualization, and evaluation. |
| XGBoost training on a single GPU | CLI | Train an XGBoost classifier on the Forest CoverType dataset on one A10 GPU, log metrics to MLflow, and register the model in Unity Catalog. |
| XGBoost hyperparameter search with Ray Tune | CLI | Use Ray Tune and ASHA to search XGBoost hyperparameters across four A10 GPUs, log the best configuration to MLflow, and register the model in Unity Catalog. |