Using XGBoost External Memory Version

When working with large datasets, training XGBoost models can be challenging as the entire dataset needs to be loaded into memory. This can be costly and sometimes infeasible. Staring from 1.5, users can define a custom iterator to load data in chunks for running XGBoost algorithms. External memory can be used for both training and prediction, but training is the primary use case and it will be our focus in this tutorial. For prediction and evaluation, users can iterate through the data themselves while training requires the full dataset to be loaded into the memory.

During training, there are two different modes for external memory support available in XGBoost, one for CPU-based algorithms like hist and approx, another one for the GPU-based training algorithm. We will introduce them in the following sections.


Training on data from external memory is not supported by the exact tree method.


The feature is still experimental as of 2.0. The performance is not well optimized.

The external memory support has gone through multiple iterations and is still under heavy development. Like the QuantileDMatrix with DataIter, XGBoost loads data batch-by-batch using a custom iterator supplied by the user. However, unlike the QuantileDMatrix, external memory will not concatenate the batches unless GPU is used (it uses a hybrid approach, more details follow). Instead, it will cache all batches on the external memory and fetch them on-demand. Go to the end of the document to see a comparison between QuantileDMatrix and external memory.

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, users need to define a data iterator with 2 class methods: next and reset, then pass it into the 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)

# The ``approx`` also work, but with low performance. GPU implementation is different from CPU.
# as noted in following sections.
booster = xgboost.train({"tree_method": "hist"}, Xy)

The above snippet is a simplified version of Experimental support for external memory. For an example in C, please see demo/c-api/external-memory/. The iterator is the common interface for using external memory with XGBoost, you can pass the resulting DMatrix object for training, prediction, and evaluation.

It is important to set the batch size based on the memory available. A good starting point is to set the batch size to 10GB per batch if you have 64GB of memory. It is not recommended to set small batch sizes like 32 samples per batch, as this can seriously hurt performance in gradient boosting.

CPU Version

In the previous section, we demonstrated how to train a tree-based model using the hist tree method on a CPU. This method involves iterating through data batches stored in a cache during tree construction. For optimal performance, we recommend using the grow_policy=depthwise setting, which allows XGBoost to build an entire layer of tree nodes with only a few batch iterations. Conversely, using the lossguide policy requires XGBoost to iterate over the data set for each tree node, resulting in slower performance.

If external memory is used, the performance of CPU training is limited by IO (input/output) speed. This means that the disk IO speed primarily determines the training speed. During benchmarking, we used an NVMe connected to a PCIe-4 slot, other types of storage can be too slow for practical usage. In addition, your system may perform caching to reduce the overhead of file reading.

GPU Version (GPU Hist tree method)

External memory is supported by GPU algorithms (i.e. when device is set to cuda). However, the algorithm used for GPU is different from the one used for CPU. When training on a CPU, the tree method iterates through all batches from external memory for each step of the tree construction algorithm. On the other hand, the GPU algorithm uses a hybrid approach. It iterates through the data during the beginning of each iteration and concatenates all batches into one in GPU memory for performance reasons. To reduce overall memory usage, users can utilize subsampling. The GPU hist tree method supports gradient-based sampling, enabling users to set a low sampling rate without compromising accuracy.

param = {
  'subsample': 0.2,
  'sampling_method': 'gradient_based',

For more information about the sampling algorithm and its use in external memory training, see this paper.


When GPU is running out of memory during iteration on external memory, user might receive a segfault instead of an OOM exception.


When using external memory with XGBoost, data is divided into smaller chunks so that only a fraction of it needs to be stored in memory at any given time. It’s important to note that this method only applies to the predictor data (X), while other data, like labels and internal runtime structures are concatenated. This means that memory reduction is most effective when dealing with wide datasets where X is significantly larger in size compared to other data like y, while it has little impact on slim datasets.

As one might expect, fetching data on-demand puts significant pressure on the storage device. Today’s computing device can process way more data than a storage can read in a single unit of time. The ratio is at order of magnitudes. An GPU is capable of processing hundred of Gigabytes of floating-point data in a split second. On the other hand, a four-lane NVMe storage connected to a PCIe-4 slot usually has about 6GB/s of data transfer rate. As a result, the training is likely to be severely bounded by your storage device. Before adopting the external memory solution, some back-of-envelop calculations might help you see whether it’s viable. For instance, if your NVMe drive can transfer 4GB (a fairly practical number) of data per second and you have a 100GB of data in compressed XGBoost cache (which corresponds to a dense float32 numpy array with the size of 200GB, give or take). A tree with depth 8 needs at least 16 iterations through the data when the parameter is right. You need about 14 minutes to train a single tree without accounting for some other overheads and assume the computation overlaps with the IO. If your dataset happens to have TB-level size, then you might need thousands of trees to get a generalized model. These calculations can help you get an estimate on the expected training time.

However, sometimes we can ameliorate this limitation. One should also consider that the OS (mostly talking about the Linux kernel) can usually cache the data on host memory. It only evicts pages when new data comes in and there’s no room left. In practice, at least some portion of the data can persist on the host memory throughout the entire training session. We are aware of this cache when optimizing the external memory fetcher. The compressed cache is usually smaller than the raw input data, especially when the input is dense without any missing value. If the host memory can fit a significant portion of this compressed cache, then the performance should be decent after initialization. Our development so far focus on two fronts of optimization for external memory:

  • Avoid iterating through the data whenever appropriate.

  • If the OS can cache the data, the performance should be close to in-core training.

Starting with XGBoost 2.0, the implementation of external memory uses mmap. It is not tested against system errors like disconnected network devices (SIGBUS). In the face of a bus error, you will see a hard crash and need to clean up the cache files. If the training session might take a long time and you are using solutions like NVMe-oF, we recommend checkpointing your model periodically. Also, it’s worth noting that most tests have been conducted on Linux distributions.

Another important point to keep in mind is that creating the initial cache for XGBoost may take some time. The interface to external memory is through custom iterators, which we can not assume to be thread-safe. Therefore, initialization is performed sequentially. Using the xgboost.config_context with verbosity=2 can give you some information on what XGBoost is doing during the wait if you don’t mind the extra output.

Compared to the QuantileDMatrix

Passing an iterator to the QuantileDmatrix enables direct construction of QuantileDmatrix with data chunks. On the other hand, if it’s passed to DMatrix, it instead enables the external memory feature. The QuantileDmatrix concatenates the data on memory after compression and doesn’t fetch data during training. On the other hand, the external memory DMatrix fetches data batches from external memory on-demand. Use the QuantileDMatrix (with iterator if necessary) when you can fit most of your data in memory. The training would be an order of magnitude faster than using external memory.

Text File Inputs

This is the original form of external memory support, users are encouraged to use custom data iterator instead. There is no big difference between using external memory version of text input and the 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.

If you have a dataset stored in a file similar to demo/data/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".