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. Starting from 1.5, users can define a custom iterator to load data in chunks for running XGBoost algorithms. External memory can be used for 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, whereas training requires the entire dataset to be loaded into the memory. Significant progress was made in the 3.0 release for the GPU implementation. We will introduce the difference between CPU and GPU in the following sections.

Note

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

Note

The feature is considered experimental but ready for public testing in 3.0. Vector-leaf is not yet supported.

The external memory support has undergone multiple development iterations. 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 does not concatenate the batches (unless specified by the extmem_single_page) . Instead, it caches all batches in the external memory and fetch them on-demand. Go to the end of the document to see a comparison between QuantileDMatrix and the external memory version of ExtMemQuantileDMatrix.

Contents

Data Iterator

Starting with XGBoost 1.5, users can define their own data loader using Python or C interface. Some examples are in the demo directory for a quick start. To enable external memory training, users need to define a data iterator with 2 class methods: next and reset, then pass it into the DMatrix or the ExtMemQuantileDMatrix 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]) -> None:
    self._file_paths = svm_file_paths
    self._it = 0
    # XGBoost will generate some cache files under the current directory with the prefix
    # "cache"
    super().__init__(cache_prefix=os.path.join(".", "cache"))

  def next(self, input_data: Callable) -> bool:
    """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 False to let XGBoost know this is the end of the iteration
      return False

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

  def reset(self) -> None:
    """Reset the iterator to its beginning"""
    self._it = 0

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

# Use the ``ExtMemQuantileDMatrix`` for the hist tree method.
Xy = xgboost.ExtMemQuantileDMatrix(it)
booster = xgboost.train({"tree_method": "hist"}, Xy)

# The ``approx`` tree method also works, but with lower performance and cannot be used
# with the quantile DMatrix.
Xy = xgboost.DMatrix(it)
booster = xgboost.train({"tree_method": "approx"}, 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.

The ExtMemQuantileDMatrix is an external memory version of the QuantileDMatrix. These two classes are specifically designed for the hist tree method for reduced memory usage and data loading overhead. See respective references for more info.

It is important to set the batch size based on the memory available. A good starting point for CPU 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 severely hurt performance in gradient boosting. See below sections for information about the GPU version and other best practices.

GPU Version (GPU Hist tree method)

External memory is supported by GPU algorithms (i.e., when device is set to cuda). Starting with 3.0, the default GPU implementation is similar to what the CPU version does. It also supports the use of ExtMemQuantileDMatrix when the hist tree method is employed. For a GPU device, the main memory is the device memory, whereas the external memory can be either a disk or the CPU memory. XGBoost stages the cache on CPU memory by default. Users can change the backing storage to disk by specifying the on_host parameter in the DataIter. However, using the disk is not recommended as it’s likely to make the GPU slower than the CPU. The option is here for experimental purposes only. In addition, ExtMemQuantileDMatrix parameters max_num_device_pages, min_cache_page_bytes, and max_quantile_batches can help control the data placement and memory usage.

Inputs to the ExtMemQuantileDMatrix (through the iterator) must be on the GPU. Following is a snippet from Experimental support for external memory:

import cupy as cp
import rmm
from rmm.allocators.cupy import rmm_cupy_allocator

# It's important to use RMM for GPU-based external memory to improve performance.
# If XGBoost is not built with RMM support, a warning will be raised.
# We use the pool memory resource here, you can also try the `ArenaMemoryResource` for
# improved memory fragmentation handling.
mr = rmm.mr.PoolMemoryResource(rmm.mr.CudaAsyncMemoryResource())
rmm.mr.set_current_device_resource(mr)
# Set the allocator for cupy as well.
cp.cuda.set_allocator(rmm_cupy_allocator)
# Make sure XGBoost is using RMM for all allocations.
with xgboost.config_context(use_rmm=True):
    # Construct the iterators for ExtMemQuantileDMatrix
    # ...
    # Build the ExtMemQuantileDMatrix and start training
    Xy_train = xgboost.ExtMemQuantileDMatrix(it_train, max_bin=n_bins)
    # Use the training DMatrix as a reference
    Xy_valid = xgboost.ExtMemQuantileDMatrix(it_valid, max_bin=n_bins, ref=Xy_train)
    booster = xgboost.train(
        {
            "tree_method": "hist",
            "max_depth": 6,
            "max_bin": n_bins,
            "device": device,
        },
        Xy_train,
        num_boost_round=n_rounds,
        evals=[(Xy_train, "Train"), (Xy_valid, "Valid")]
    )

It’s crucial to use RAPIDS Memory Manager (RMM) with an asynchronous memory resource for all memory allocation when training with external memory. XGBoost relies on the asynchronous memory pool to reduce the overhead of data fetching. In addition, the open source NVIDIA Linux driver is required for Heterogeneous memory management (HMM) support. Usually, users need not to change ExtMemQuantileDMatrix parameters max_num_device_pages and min_cache_page_bytes, they are automatically configured based on the device and don’t change model accuracy. However, the max_quantile_batches can be useful if ExtMemQuantileDMatrix is running out of device memory during construction, see QuantileDMatrix and the following sections for more info.

In addition to the batch-based data fetching, the GPU version supports concatenating batches into a single blob for the training data to improve performance. For GPUs connected via PCIe instead of nvlink, the performance overhead with batch-based training is significant, particularly for non-dense data. Overall, it can be at least five times slower than in-core training. Concatenating pages can be used to get the performance closer to in-core training. This option should be used in combination with subsampling to reduce the memory usage. During concatenation, subsampling removes a portion of samples, reducing the training dataset size. The GPU hist tree method supports gradient-based sampling, enabling users to set a low sampling rate without compromising accuracy. Before 3.0, concatenation with subsampling was the only option for GPU-based external memory. After 3.0, XGBoost uses the regular batch fetching as the default while the page concatenation can be enabled by:

param = {
  "device": "cuda",
  "extmem_single_page": true,
  'subsample': 0.2,
  'sampling_method': 'gradient_based',
}

For more information about the sampling algorithm and its use in external memory training, see this paper. Lastly, see following sections for best practices.

NVLink-C2C

The newer NVIDIA platforms like NVLink-C2C, which facilitates a fast interconnect between the CPU and the GPU. With the host memory serving as the data cache, XGBoost can retrieve data with significantly lower overhead. When the input data is dense, there’s minimal to no performance loss for training, except for the initial construction of the ExtMemQuantileDMatrix. The initial construction iterates through the input data twice, as a result, the most significant overhead compared to in-core training is one additional data read when the data is dense. Please note that there are multiple variants of the platform and they come with different C2C bandwidths. During initial development of the feature, we used the LPDDR5 480G version, which has about 350GB/s bandwidth for host to device transfer.

To run experiments on these platforms, the open source NVIDIA Linux driver with version >=565.47 is required, it should come with CTK 12.7 and later versions.

Distributed Training

Distributed training is similar to in-core learning, but the work for framework integration is still on-going. See Experimental support for distributed training with external memory for an example for using the communicator to build a simple pipeline. Since users can define their custom data loader, it’s unlikely that existing distributed frameworks interface in XGBoost can meet all the use cases, the example can be a starting point for users who have custom infrastructure.

Best Practices

In previous sections, we demonstrated how to train a tree-based model with data residing on an external memory and made some recommendations for batch size. Here are some other configurations we find useful. The external memory feature 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 significantly slower performance.

In addition, this hist tree method should be preferred over the approx tree method as the former doesn’t recreate the histogram bins for every iteration. Creating the histogram bins requires loading the raw input data, which is prohibitively expensive. The ExtMemQuantileDMatrix designed for the hist tree method can speed up the initial data construction and the evaluation significantly for external memory.

Since the external memory implementation focuses on training where XGBoost needs to access the entire dataset, only the X is divided into batches while everything else is concatenated. As a result, it’s recommended for users to define their own management code to iterate through the data for inference, especially for SHAP value computation. The size of SHAP results can be larger than X, making external memory in XGBoost less effective. Some frameworks like dask can help with the data chunking and iterate through the data for inference with memory spilling.

When external memory is used, the performance of CPU training is limited by disk IO (input/output) speed. This means that the disk IO speed primarily determines the training speed. Similarly, PCIe bandwidth limits the GPU performance, assuming the CPU memory is used as a cache and address translation services (ATS) is unavailable. During development, we observed that typical data transfer in XGBoost with PCIe4x16 has about 24GB/s bandwidth, which is significantly lower than the GPU processing performance. Whereas with a C2C-enabled machine, the performance of data transfer and processing in training are similar. Running inference is much less computation-intensive than training and, hence, much faster. As a result, the performance bottleneck of inference is back to data transfer. For GPU, the time it takes to read the data from host to device completely determines the time it takes to run inference, even if a C2C link is available.

Xy_train = xgboost.ExtMemQuantileDMatrix(it_train, max_bin=n_bins)
Xy_valid = xgboost.ExtMemQuantileDMatrix(it_valid, max_bin=n_bins, ref=Xy_train)

In addition, since the GPU implementation relies on asynchronous memory pool, which is subject to memory fragmentation even if the CudaAsyncMemoryResource is used. You might want to start the training with a fresh pool instead of starting training right after the ETL process. If you run into out-of-memory errors and you are convinced that the pool is not full yet (pool memory usage can be profiled with nsight-system), consider tuning the RMM memory resource like using CudaAsyncMemoryResource in conjunction with BinningMemoryResource(mr, 21, 25) instead of the PoolMemoryResource. Alternately, the ArenaMemoryResource is also an excellent option.

During CPU benchmarking, we used an NVMe connected to a PCIe-4 slot. Other types of storage can be too slow for practical usage. However, your system will likely perform some caching to reduce the overhead of the file read. See the following sections for remarks.

Remarks

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 devices can process way more data than storage devices can read in a single unit of time. The ratio is in the order of magnitudes. A GPU is capable of processing hundreds 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 determine its viability. For instance, if your NVMe drive can transfer 4GB (a reasonably practical number) of data per second, and you have a 100GB of data in a compressed XGBoost cache (corresponding to a dense float32 numpy array with 200GB, give or take). A tree with depth 8 needs at least 16 iterations through the data when the parameter is optimal. 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 a TB-level size, you might need thousands of trees to get a generalized model. These calculations can help you get an estimate of the expected training time.

However, sometimes, we can ameliorate this limitation. One should also consider that the OS (mainly 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 in 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, the performance should be decent after initialization. Our development so far focuses on following 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.
For GPU, the actual computation should overlap with memory copy as much as possible.

Starting with XGBoost 2.0, the implementation of external memory uses mmap. It has not been 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 use 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 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 the DMatrix or the ExtMemQuantileDMatrix, it instead enables the external memory feature. The QuantileDMatrix concatenates the data in memory after compression and doesn’t fetch data during training. On the other hand, the external memory DMatrix (ExtMemQuantileDMatrix) 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. For many platforms, the training speed can be an order of magnitude faster than external memory.

Brief History

For a long time, external memory support has been an experimental feature and has undergone multiple development iterations. Here’s a brief summary of major changes:

Gradient-based sampling was introduced to the GPU hist in 1.1.
The iterator interface was introduced in 1.5, along with a major rewrite for the internal framework.
2.0 introduced the use of mmap, along with optimization in XBGoost to enable zero-copy data fetching.
3.0 reworked the GPU implementation to support caching data on the host and disk, introduced the ExtMemQuantileDMatrix class, added quantile-based objectives support.
In addition, we begin support for distributed training in 3.0

Text File Inputs

Warning

This is the original form of external memory support before 1.5 and is now deprecated, users are encouraged to use a custom data iterator instead.

There is no significant difference between using the external memory version of text input and the in-memory version of text input. 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 typical 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.

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 the CLI version, simply add the cache suffix, e.g. "../data/agaricus.txt.train?format=libsvm#dtrain.cache".