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Some guidelines on device memory usage (#5038)

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* Add memory usage demo

* Update documentation
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Rory Mitchell
2019-11-17 07:48:24 +13:00
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demo/gpu_acceleration/README.md
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# GPU Acceleration Demo
This demo shows how to train a model on the [forest cover type](https://archive.ics.uci.edu/ml/datasets/covertype) dataset using GPU acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it time consuming to process. We compare the run-time and accuracy of the GPU and CPU histogram algorithms.
`cover_type.py` shows how to train a model on the [forest cover type](https://archive.ics.uci.edu/ml/datasets/covertype) dataset using GPU acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it time consuming to process. We compare the run-time and accuracy of the GPU and CPU histogram algorithms.
This demo requires the [GPU plug-in](https://xgboost.readthedocs.io/en/latest/gpu/index.html) to be built and installed.
The dataset is automatically loaded via the sklearn script.
`memory.py` shows how to repeatedly train xgboost models while freeing memory between iterations.