##################################### Using XGBoost External Memory Version ##################################### **Contents** .. contents:: :backlinks: none :local: ******** Overview ******** When working with large datasets, training XGBoost models can be challenging as the entire dataset needs to be loaded into the main memory. This can be costly and sometimes infeasible. External memory training is sometimes called out-of-core training. It refers to the capability that XGBoost can optionally cache data in a location external to the main processor, be it CPU or GPU. XGBoost doesn't support network file systems by itself. As a result, for CPU, the external memory usually refers to a harddrive. And for GPU, it refers to either the host memory or a harddrive. 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. During model training, XGBoost fetches the cache in batches to construct the decision trees, hence avoiding loading the entire dataset into the main memory and achieve better vertical scaling (scaling within the same node). 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. We recommend using the default ``hist`` tree method for performance reasons. .. 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. See below sections for a brief history. ************* Data Iterator ************* To start using the external memory, users need define a data iterator. The data iterator interface was added to the Python and C interfaces in 1.5, and to the R interface in 3.0.0. Like the :py:class:`~xgboost.QuantileDMatrix` with :py:class:`~xgboost.DataIter`, XGBoost loads data batch-by-batch using the custom iterator supplied by the user. However, unlike the :py:class:`~xgboost.QuantileDMatrix`, external memory does not concatenate the batches. 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 :py:class:`~xgboost.QuantileDMatrix` and the external memory version of :py:class:`~xgboost.ExtMemQuantileDMatrix`. Some examples are in the ``demo`` directory for a quick start. To enable external memory training, the custom data iterator needs to have two class methods: ``next`` and ``reset``. .. code-block:: python import os from typing import List, Callable import numpy as np import xgboost class Iterator(xgboost.DataIter): """A custom iterator for loading files in batches.""" def __init__( self, device: Literal["cpu", "cuda"], file_paths: List[Tuple[str, str]] ) -> None: self.device = device self._file_paths = 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 load_file(self) -> Tuple[np.ndarray, np.ndarray]: """Load a single batch of data.""" X_path, y_path = self._file_paths[self._it] # When the `ExtMemQuantileDMatrix` is used, the device must match. GPU cannot # consume CPU input data and vice-versa. if self.device == "cpu": X = np.load(X_path) y = np.load(y_path) else: import cupy as cp X = cp.load(X_path) y = cp.load(y_path) assert X.shape[0] == y.shape[0] return X, y 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 iteration return False # input_data is a keyword-only function passed in by XGBoost and has the similar # signature to the ``DMatrix`` constructor. X, y = self.load_file() input_data(data=X, label=y) self._it += 1 return True def reset(self) -> None: """Reset the iterator to its beginning""" self._it = 0 After defining the iterator, we can to pass it into the :py:class:`~xgboost.DMatrix` or the :py:class:`~xgboost.ExtMemQuantileDMatrix` constructor: .. code-block:: python it = Iterator(device="cpu", file_paths=["file_0.npy", "file_1.npy", "file_2.npy"]) # Use the ``ExtMemQuantileDMatrix`` for the hist tree method, recommended. 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 :ref:`sphx_glr_python_examples_external_memory.py`. 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 :py:class:`~xgboost.DMatrix` object for training, prediction, and evaluation. The :py:class:`~xgboost.ExtMemQuantileDMatrix` is an external memory version of the :py:class:`~xgboost.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 :py:class:`~xgboost.ExtMemQuantileDMatrix` when the ``hist`` tree method is employed (default). 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 :py:class:`~xgboost.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 experimentation purposes only. In addition, :py:class:`~xgboost.ExtMemQuantileDMatrix` parameters ``min_cache_page_bytes``, and ``max_quantile_batches`` can help control the data placement and memory usage. Inputs to the :py:class:`~xgboost.ExtMemQuantileDMatrix` (through the iterator) must be on the GPU. It's crucial to use an asynchronous memory pool for all memory allocations when training with external memory. XGBoost relies on the asynchronous memory pool to reduce the overhead of data fetching. There are two options for setting up the memory pool: - **CUDA Async Pool**: Uses the CUDA driver's built-in async memory pool. This option doesn't require any additional dependencies. It's the same as using the `CudaAsyncMemoryResource` from RMM (see below). - **RMM Pool**: Uses `RAPIDS Memory Manager (RMM) `__ with an asynchronous memory resource. This option requires RMM to be installed and XGBoost to be built with RMM support. Choose the one that best fits your use case. ===================== Using CUDA Async Pool ===================== The CUDA async pool uses the driver's default memory pool with a configured release threshold. See :ref:`global_config` for the parameter `use_cuda_async_pool`. .. versionadded:: 3.2.0 .. warning:: This is an experimental feature and is subject to change without notice. Windows is not supported yet. .. code-block:: python import cupy as cp import cuda.bindings.driver as driver import cuda.bindings.runtime as cudart from cupy.cuda import MemoryAsyncPool # Get the default memory pool and configure the release threshold status, dft_pool = cudart.cudaDeviceGetDefaultMemPool(0) # Set the release threshold to 90% of total device memory status, free, total = cudart.cudaMemGetInfo() v = driver.cuuint64_t(int(total * 0.9)) cudart.cudaMemPoolSetAttribute( dft_pool, cudart.cudaMemPoolAttr.cudaMemPoolAttrReleaseThreshold, v, ) # Set the allocator for cupy as well. cp.cuda.set_allocator(MemoryAsyncPool().malloc) # Make sure XGBoost is using the CUDA async pool for all allocations. with xgboost.config_context(use_cuda_async_pool=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_bin": n_bins, "device": device, }, Xy_train, num_boost_round=n_rounds, evals=[(Xy_train, "Train"), (Xy_valid, "Valid")] ) ============== Using RMM Pool ============== Alternatively, you can use RMM with an asynchronous memory resource. If XGBoost is not built with RMM support, a warning will be raised: .. code-block:: python import cupy as cp import rmm from rmm.allocators.cupy import rmm_cupy_allocator # We use the pool memory resource here for simplicity, 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")] ) In addition, the open source `NVIDIA Linux driver `__ is required for ``Heterogeneous memory management (HMM)`` support. Usually, users need not to change :py:class:`~xgboost.ExtMemQuantileDMatrix` parameters like ``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 :py:class:`~xgboost.ExtMemQuantileDMatrix` is running out of device memory during construction, see :py:class:`~xgboost.QuantileDMatrix` and the following sections for more info. Currently, we focus on devices with ``NVLink-C2C`` support for GPU-based external memory support. ========== NVLink-C2C ========== The newer NVIDIA platforms like `Grace-Hopper `__ use `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 :py:class:`~xgboost.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. When choosing the variant for training XGBoost models, one should pay extra attention to the C2C bandwidth. Here we provide a simple example as a starting point for training with external memory. We used this example for one of the benchmarks. To train a model with `2 ^ 29` 32-bit floating point samples, `512` features (total 1TB) on a GH200 (a H200 GPU connected to a Grace CPU by a chip-to-chip link) system. One can start with: - Evenly divide the data into 128 batches with 8GB per batch. - Define a custom iterator as previously described. - Set the `max_quantile_batches` parameter of the :py:class:`~xgboost.ExtMemQuantileDMatrix` to 32 (256GB per sub-stream for quantization). Load the data. - Start training with ``device=cuda``. 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. Lastly, there's a known issue with Linux 6.11 that can lead to CUDA host memory allocation failure with an ``invalid argument`` error. .. _extmem-adaptive-cache: ============== Adaptive Cache ============== Starting with 3.1, XGBoost introduces an adaptive cache for GPU-based external memory training. The feature helps split the data cache into a host cache and a device cache. By keeping a portion of the cache on the GPU, we can reduce the amount of data transfer during training when there's sufficient amount of GPU memory. The feature can be controlled by the ``cache_host_ratio`` parameter of the :py:class:`xgboost.ExtMemQuantileDMatrix`. Unless explicitly specified, the ratio is automatically estimated based on device memory size and the size of the dataset. However, this parameter increases memory fragmentation as XGBoost needs large memory pages with irregular sizes. As a result, you might see out of memory error after the construction of the ``DMatrix`` but before the actual training begins. For reference, we tested the adaptive cache with a 128GB (512 features) dense 32bit floating dataset using a NVIDIA A6000 GPU, which comes with 48GB device memory. The ``cache_host_ratio`` was estimated to be about 0.3, meaning about 30 percent of the quantized cache was on the host and rest of 70 percent was actually in-core. Given this ratio, the overhead is minimal. However, the estimated ratio increases as the data size grows. ================================ Non-Uniform Memory Access (NUMA) ================================ On multi-socket systems, `NUMA `__ helps optimize data access by prioritizing memory that is local to each socket. On these systems, it's essential to set the correct affinity to reduce the overhead of cross-socket data access. Since the out of core training stages the data cache on the host and trains the model using a GPU, the training performance is particularly sensitive to the data read bandwidth. To provide some context, on a GB200 machine, accessing the wrong NUMA node from a GPU can reduce the C2C bandwidth by half. Even if you are not using distributed training, you should still pay attention to NUMA control since there's no guarantee that your process will have the correct configuration. To configure the NUMA binding from command line on Linux, one can use the ``numactl`` or the ``hwloc-bind``: .. code-block:: sh numactl --membind=${NODEID} --cpunodebind=${NODEID} ./myapp To obtain the node ID, you can check the machine topology via ``nvidia-smi``: .. code-block:: sh nvidia-smi topo -m The column ``NUMA Affinity`` lists the NUMA node ID for each GPU. In the example output shown below, the `GPU0` is associated with the `0` node ID:: GPU0 GPU1 NIC0 NIC1 NIC2 NIC3 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV18 NODE NODE NODE SYS 0-71 0 2 GPU1 NV18 X SYS SYS SYS NODE 72-143 1 10 NIC0 NODE SYS X PIX NODE SYS NIC1 NODE SYS PIX X NODE SYS NIC2 NODE SYS NODE NODE X SYS NIC3 SYS NODE SYS SYS SYS X Alternatively, one can also use the ``hwloc`` command line interface, please make sure the strict flag is used: .. code-block:: sh hwloc-bind --strict --membind node:${NODEID} --cpubind node:${NODEID} ./myapp Both projects provide a programming interface for configuring NUMA bindings within applications. See :ref:`sphx_glr_python_examples_distributed_extmem_basic.py` for a complete example of using ``pyhwloc`` in a distributed training setting. ******************** Distributed Training ******************** Distributed training is similar to in-core learning, but the work for framework integration is still on-going. See :ref:`sphx_glr_python_examples_distributed_extmem_basic.py` 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. In addition, we made some recommendations for batch size and NUMA. 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 (tree size is exponential to the depth). In addition, the ``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 :py:class:`~xgboost.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 matrix can be larger than the feature matrix ``X``, making external memory in XGBoost less effective. 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 and about 42GB/s with PCIe5, 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 close to each other. 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. .. code-block:: python 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, memory fragmentation can occur regardless of whether you use the CUDA async pool or RMM. 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 using the :py:class:`~rmm.mr.ArenaMemoryResource` memory resource with RMM, or using the CUDA asynchronous pool with the latest NVIDIA kernel driver. 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. .. _ext_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 CPU 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 :py:func:`~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 :py:class:`~xgboost.QuantileDMatrix` enables direct construction of :py:class:`~xgboost.QuantileDMatrix` with data chunks. On the other hand, if it's passed to the :py:class:`~xgboost.DMatrix` or the :py:class:`~xgboost.ExtMemQuantileDMatrix`, it instead enables the external memory feature. The :py:class:`~xgboost.QuantileDMatrix` concatenates the data in memory after compression and doesn't fetch data during training. On the other hand, the external memory :py:class:`~xgboost.DMatrix` (:py:class:`~xgboost.ExtMemQuantileDMatrix`) fetches data batches from external memory on demand. Use the :py:class:`~xgboost.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 :py:class:`~xgboost.ExtMemQuantileDMatrix` class, added quantile-based objectives support. - In addition, we begin support for distributed training in 3.0 - 3.1 added support for having divided cache pages. One can have part of a cache page in the GPU and the rest of the cache in the host memory. In addition, XGBoost works with the Grace Blackwell hardware decompression engine when data is sparse. - The text file cache format has been removed in 3.1.0. - The page concatenation option has been removed in 3.2.0.