Training on GPU

CatBoost supports training on GPUs.

Training on GPU is non-deterministic, because the order of floating point summations is non-deterministic in this implementation.

Choose the implementation for more details on the parameters that are required to start training on GPU.

Python package

The parameters that enable and customize training on GPU are set in the constructors of the following classes:

Parameters

The processing unit type to use for training.

Possible values:

• CPU
• GPU

devices

IDs of the GPU devices to use for training (indices are zero-based).

Format

• <unit ID> for one device (for example, 3)
• <unit ID1>:<unit ID2>:..:<unit IDN> for multiple devices (for example, devices='0:1:3')
• <unit ID1>-<unit IDN> for a range of devices (for example, devices='0-3')

Note

Other training parameters are also available. Some of them are CPU-specific or GPU-specific. See the Python package training parameters section for more details.

For example, use the following code to train a classification model on GPU:

from catboost import CatBoostClassifier

train_data = [[0, 3],
[4, 1],
[8, 1],
[9, 1]]
train_labels = [0, 0, 1, 1]

model = CatBoostClassifier(iterations=1000,
devices='0:1')
model.fit(train_data,
train_labels,
verbose=False)



R package

For the catboost.train method:

Parameters

The processing unit type to use for training.

Possible values:

• CPU
• GPU

devices

Parameters

IDs of the GPU devices to use for training (indices are zero-based).

Format

• <unit ID> for one device (for example, 3)
• <unit ID1>:<unit ID2>:..:<unit IDN> for multiple devices (for example, devices='0:1:3')
• <unit ID1>-<unit IDN> for a range of devices (for example, devices='0-3')

For example, use the following code to train a model on GPU:

library(catboost)

dataset = matrix(c(1900,7,
1896,1,
1896,41),
nrow=3,
ncol=2,
byrow = TRUE)
label_values = c(0,1,1)

fit_params <- list(iterations = 100,
loss_function = 'Logloss',

pool = catboost.load_pool(dataset, label = label_values)

model <- catboost.train(pool, params = fit_params)


Command-line version

For the catboost fit command:

Command keys

The processing unit type to use for training.

Possible values:

• CPU
• GPU

--devices

IDs of the GPU devices to use for training (indices are zero-based).

Format

• <unit ID> for one device (for example, 3)
• <unit ID1>:<unit ID2>:..:<unit IDN> for multiple devices (for example, devices='0:1:3')
• <unit ID1>-<unit IDN> for a range of devices (for example, devices='0-3')

Train a classification model on GPU:

catboost fit --learn-set ../pytest/data/adult/train_small --column-description ../pytest/data/adult/train.cd --task-type GPU