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Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow – dmlc/xgboost, * Remove GPU memory usage demo . * Add tests for demos. * Remove `silent`. * Remove shebang as it’s not portable.
See demo/gpu_acceleration/memory.py for a simple example. Memory inside xgboost training is generally allocated for two reasons – storing the dataset and working memory. The dataset itself is stored on device in a compressed ELLPACK format. The ELLPACK format is a type of sparse matrix that stores elements with a constant row stride.
1/8/2020 · See demo/gpu_acceleration/memory.py for a simple example. Does this hold true for CPU training too? In any case I find the memory usage very high, and similar experiments with LightGBM on the same data set give me memory consumptions that are 100x lower.
A workaround is to serialise the booster object after training. See demo/gpu_acceleration/memory.py for a simple example. Memory inside xgboost training is generally allocated for two reasons – storing the dataset and working memory. The dataset itself is stored on device in a compressed ELLPACK format.
