Using XGBoost External Memory Version

There is no big difference between using external memory version and in-memory version. The only difference is the filename format.

The external memory version takes in the following URI format:


The filename is the normal path to libsvm format file you want to load in, and cacheprefix is a path to a cache file that XGBoost will use for caching preprocessed data in binary form.

To load from csv files, use the following syntax:


where label_column should point to the csv column acting as the label.

To provide a simple example for illustration, extracting the code from demo/guide-python/ If you have a dataset stored in a file similar to agaricus.txt.train with libSVM format, the external memory support can be enabled by:

dtrain = DMatrix('../data/agaricus.txt.train#dtrain.cache')

XGBoost will first load agaricus.txt.train in, preprocess it, then write to a new file named dtrain.cache as an on disk cache for storing preprocessed data in an internal binary format. For more notes about text input formats, see Text Input Format of DMatrix.

For CLI version, simply add the cache suffix, e.g. "../data/agaricus.txt.train#dtrain.cache".

GPU Version

External memory is fully supported in GPU algorithms (i.e. when tree_method is set to gpu_hist).

If you are still getting out-of-memory errors after enabling external memory, try subsampling the data to further reduce GPU memory usage:

param = {
  'subsample': 0.1,
  'sampling_method': 'gradient_based',

For more information, see this paper.

Distributed Version

The external memory mode naturally works on distributed version, you can simply set path like

data = "hdfs://path-to-data/#dtrain.cache"

XGBoost will cache the data to the local position. When you run on YARN, the current folder is temporary so that you can directly use dtrain.cache to cache to current folder.


  • The hist tree method hasn’t been tested thoroughly with external memory support (see this issue).

  • OSX is not tested.