XGBoost GPU Support
This page contains information about GPU algorithms supported in XGBoost.
Note
CUDA 10.1, Compute Capability 3.5 required
The GPU algorithms in XGBoost require a graphics card with compute capability 3.5 or higher, with CUDA toolkits 10.1 or later. (See this list to look up compute capability of your GPU card.)
CUDA Accelerated Tree Construction Algorithms
Tree construction (training) and prediction can be accelerated with CUDA-capable GPUs.
Usage
Specify the tree_method
parameter as one of the following algorithms.
Algorithms
tree_method |
Description |
---|---|
gpu_hist |
Equivalent to the XGBoost fast histogram algorithm. Much faster and uses considerably less memory. NOTE: May run very slowly on GPUs older than Pascal architecture. |
Supported parameters
parameter |
|
---|---|
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
|
✔ |
GPU accelerated prediction is enabled by default for the above mentioned tree_method
parameters but can be switched to CPU prediction by setting predictor
to cpu_predictor
. This could be useful if you want to conserve GPU memory. Likewise when using CPU algorithms, GPU accelerated prediction can be enabled by setting predictor
to gpu_predictor
.
The experimental parameter single_precision_histogram
can be set to True to enable building histograms using single precision. This may improve speed, in particular on older architectures.
The device ordinal (which GPU to use if you have many of them) can be selected using the
gpu_id
parameter, which defaults to 0 (the first device reported by CUDA runtime).
The GPU algorithms currently work with CLI, Python, R, and JVM packages. See Installation Guide for details.
param['gpu_id'] = 0
param['tree_method'] = 'gpu_hist'
XGBRegressor(tree_method='gpu_hist', gpu_id=0)
GPU-Accelerated SHAP values
XGBoost makes use of GPUTreeShap as a backend for computing shap values when the GPU predictor is selected.
model.set_param({"predictor": "gpu_predictor"})
shap_values = model.predict(dtrain, pred_contribs=True)
shap_interaction_values = model.predict(dtrain, pred_interactions=True)
See examples here.
Multi-node Multi-GPU Training
XGBoost supports fully distributed GPU training using Dask. For getting started see our tutorial Distributed XGBoost with Dask and worked examples here, also Python documentation Dask API for complete reference.
Objective functions
Most of the objective functions implemented in XGBoost can be run on GPU. Following table shows current support status.
Objectives |
GPU support |
reg:squarederror |
✔ |
reg:squaredlogerror |
✔ |
reg:logistic |
✔ |
reg:pseudohubererror |
✔ |
binary:logistic |
✔ |
binary:logitraw |
✔ |
binary:hinge |
✔ |
count:poisson |
✔ |
reg:gamma |
✔ |
reg:tweedie |
✔ |
multi:softmax |
✔ |
multi:softprob |
✔ |
survival:cox |
✘ |
survival:aft |
✔ |
rank:pairwise |
✔ |
rank:ndcg |
✔ |
rank:map |
✔ |
Objective will run on GPU if GPU updater (gpu_hist
), otherwise they will run on CPU by
default. For unsupported objectives XGBoost will fall back to using CPU implementation by
default. Note that when using GPU ranking objective, the result is not deterministic due
to the non-associative aspect of floating point summation.
Metric functions
Following table shows current support status for evaluation metrics on the GPU.
Metric |
GPU Support |
---|---|
rmse |
✔ |
rmsle |
✔ |
mae |
✔ |
mape |
✔ |
mphe |
✔ |
logloss |
✔ |
error |
✔ |
merror |
✔ |
mlogloss |
✔ |
auc |
✔ |
aucpr |
✔ |
ndcg |
✔ |
map |
✔ |
poisson-nloglik |
✔ |
gamma-nloglik |
✔ |
cox-nloglik |
✘ |
aft-nloglik |
✔ |
interval-regression-accuracy |
✔ |
gamma-deviance |
✔ |
tweedie-nloglik |
✔ |
Similar to objective functions, default device for metrics is selected based on tree updater and predictor (which is selected based on tree updater).
Benchmarks
You can run benchmarks on synthetic data for binary classification:
python tests/benchmark/benchmark_tree.py --tree_method=gpu_hist
python tests/benchmark/benchmark_tree.py --tree_method=hist
Training time on 1,000,000 rows x 50 columns of random data with 500 boosting iterations and 0.25/0.75 test/train split with AMD Ryzen 7 2700 8 core @3.20GHz and NVIDIA 1080ti yields the following results:
tree_method |
Time (s) |
---|---|
gpu_hist |
12.57 |
hist |
36.01 |
Memory usage
The following are some guidelines on the device memory usage of the gpu_hist tree method.
Memory inside xgboost training is generally allocated for two reasons - storing the dataset and working memory.
The dataset itself is stored on device in a compressed ELLPACK format. The ELLPACK format is a type of sparse matrix that stores elements with a constant row stride. This format is convenient for parallel computation when compared to CSR because the row index of each element is known directly from its address in memory. The disadvantage of the ELLPACK format is that it becomes less memory efficient if the maximum row length is significantly more than the average row length. Elements are quantised and stored as integers. These integers are compressed to a minimum bit length. Depending on the number of features, we usually don’t need the full range of a 32 bit integer to store elements and so compress this down. The compressed, quantised ELLPACK format will commonly use 1/4 the space of a CSR matrix stored in floating point.
Working memory is allocated inside the algorithm proportional to the number of rows to keep track of gradients, tree positions and other per row statistics. Memory is allocated for histogram bins proportional to the number of bins, number of features and nodes in the tree. For performance reasons we keep histograms in memory from previous nodes in the tree, when a certain threshold of memory usage is passed we stop doing this to conserve memory at some performance loss.
If you are getting out-of-memory errors on a big dataset, try the or xgboost.DeviceQuantileDMatrix
or external memory version.
Developer notes
The application may be profiled with annotations by specifying USE_NTVX to cmake. Regions covered by the ‘Monitor’ class in CUDA code will automatically appear in the nsight profiler when verbosity is set to 3.
References
NVIDIA Parallel Forall: Gradient Boosting, Decision Trees and XGBoost with CUDA
Out-of-Core GPU Gradient Boosting
Contributors
Many thanks to the following contributors (alphabetical order):
Andrey Adinets
Jiaming Yuan
Jonathan C. McKinney
Matthew Jones
Philip Cho
Rong Ou
Rory Mitchell
Shankara Rao Thejaswi Nanditale
Sriram Chandramouli
Vinay Deshpande
Please report bugs to the XGBoost issues list: https://github.com/dmlc/xgboost/issues. For general questions please visit our user form: https://discuss.xgboost.ai/.