setup_CUDA

Request an NVIDIA GPU through CUDA as the compute device of an execution
config, naming which GPUs to use, e.g.
<code>setup_FutureExecution(device = setup_CUDA(ids = 1L))</code>. Without ids,
<code>device = "cuda"</code> is the same request. Algorithms that run on CUDA are the torch-backed ones
(MLP, TabNet) and the LightGBM family built with CUDA support; every other
algorithm runs on the CPU.

Machine learning and visualization package with an 'S7' backend featuring
comprehensive type checking and validation, observable runs, and auditable outputs.
The functional API provides one-call access to supervised and unsupervised learning
through train(), cluster(), and decomp(). All configuration steps are performed using
setup functions and validated. A single call to train() can handle preprocessing,
decomposition, hyperparameter tuning, model stacking, and testing with nested resampling.
Supports 'data.frame', 'data.table', and 'tibble' inputs and parallel execution. Plot
methods for trained and resampled supervised models draw interactive figures through
'rtemis.draw'. Includes native implementations of SuperLearner, RuleFit, and Linear
Additive Trees, and supports conformal prediction. The package first appeared in
E.D. Gennatas (2017)
<https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Efstathios D. Gennatas

rtemis

Schema-Driven Machine Learning and Visualization

E.D. Gennatas

setup_CUDA function

<dl><dt>ids</dt>
<dd>Optional Integer [0, Inf) vector: Zero-based indices of the GPUs to
use. <code>NULL</code> uses the first visible GPU. A run uses the first index listed.</dd></dl>

Arguments

Author

Setup an NVIDIA GPU as the compute device — setup_CUDA

<dl>

<dt>ids</dt>
<dd>Optional Integer [0, Inf) vector: Zero-based indices of the GPUs to
use. <code>NULL</code> uses the first visible GPU. A run uses the first index listed.</dd>

</dl>

setup_CUDA: Setup an NVIDIA GPU as the compute device

Description

Usage

Value

Arguments

Author

Examples