Using XGBoost External Memory Version

XGBoost supports loading data from external memory using builtin data parser. And starting from version 1.5, users can also define a custom iterator to load data in chunks. The feature is still experimental and not yet ready for production use. In this tutorial we will introduce both methods. Please note that training on data from external memory is not supported by exact tree method.

Data Iterator

Starting from XGBoost 1.5, users can define their own data loader using Python or C interface. There are some examples in the demo directory for quick start. This is a generalized version of text input external memory, where users no longer need to prepare a text file that XGBoost recognizes. To enable the feature, user need to define a data iterator with 2 class methods next and reset then pass it into DMatrix constructor.

import os
from typing import List, Callable
import xgboost
from sklearn.datasets import load_svmlight_file

class Iterator(xgboost.DataIter):
  def __init__(self, svm_file_paths: List[str]):
    self._file_paths = svm_file_paths
    self._it = 0
    # XGBoost will generate some cache files under current directory with the prefix
    # "cache"
    super().__init__(cache_prefix=os.path.join(".", "cache"))

  def next(self, input_data: Callable):
    """Advance the iterator by 1 step and pass the data to XGBoost.  This function is
    called by XGBoost during the construction of ``DMatrix``

    """
    if self._it == len(self._file_paths):
      # return 0 to let XGBoost know this is the end of iteration
      return 0

    # input_data is a function passed in by XGBoost who has the exact same signature of
    # ``DMatrix``
    X, y = load_svmlight_file(self._file_paths[self._it])
    input_data(data=X, label=y)
    self._it += 1
    # Return 1 to let XGBoost know we haven't seen all the files yet.
    return 1

  def reset(self):
    """Reset the iterator to its beginning"""
    self._it = 0

it = Iterator(["file_0.svm", "file_1.svm", "file_2.svm"])
Xy = xgboost.DMatrix(it)

# Other tree methods including ``hist`` and ``gpu_hist`` also work, but has some caveats
# as noted in following sections.
booster = xgboost.train({"tree_method": "approx"}, Xy)

The above snippet is a simplified version of demo/guide-python/external_memory.py. For an example in C, please see demo/c-api/external-memory/.

Text File Inputs

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:

filename?format=libsvm#cacheprefix

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:

filename.csv?format=csv&label_column=0#cacheprefix

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/external_memory.py. 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?format=libsvm#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?format=libsvm#dtrain.cache".

GPU Version (GPU Hist tree method)

External memory is 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. Internally the tree method still concatenate all the chunks into 1 final histogram index due to performance reason, but in compressed format. So its scalability has an upper bound but still has lower memory cost in general.

CPU Version

For CPU histogram based tree methods (approx, hist) it’s recommended to use grow_policy=depthwise for performance reason. Iterating over data batches is slow, with depthwise policy XGBoost can build a entire layer of tree nodes with a few iterations, while with lossguide XGBoost needs to iterate over the data set for each tree node.