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TargetDevice

is an option for certain functions that specifies on which device the computation should be attempted.

Details
Details and Options Details and Options
Examples  
Basic Examples  
Scope  
Inference  
Training  
Possible Issues  
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TargetDevice

TargetDevice

is an option for certain functions that specifies on which device the computation should be attempted.

Details

  • Possible settings for TargetDevice change depending on the system and the available hardware.
  • General settings include:
  • "CPU"use the CPU
    "GPU"use available dedicated hardware
  • The specification TargetDevice->"GPU" has the following interpretation, depending on $SystemID:
  • "CoreML""MacOSX-ARM64"
    "CUDA""Windows-x86-64" or "Linux-x86-64"
    "DirectML""Windows-x86-64" if no CUDA card is detected
  • Settings specific to system and available hardware include:
  • "CoreML"Apple CoreML framework
    "CUDA"Nvidia CUDA API
    "DirectML"Microsoft Direct Machine Learning API
  • Currently, the only settings supported by NetTrain are "CPU" and "CUDA".
  • TargetDevice{"GPU",n} allows a specific GPU to be used, where n is an integer between 1 and the number of GPUs available on your computer.
  • In general, a specific value of n might not identify the same GPU when different backends such as "CUDA" and "DirectML" are used.
  • TargetDevice->{"GPU",All} specifies that all available GPUs should be used jointly.
  • TargetDevice->{"GPU",{n1,n2,…}} specifies that a specific subset of the GPUs should be used jointly.
  • On Apple silicon machines, TargetDevice->"CoreML" attempts to perform the computation using the Apple Neural Engine. It is not currently supported for NetTrain and NetMeasurements. »
  • On Windows machines, TargetDevice->"DirectML" can perform the computation using an integrated or discrete GPU with DirectX support. It is not currently supported for NetTrain and NetMeasurements.
  • TargetDevice"CUDA" requires NVIDIA GPUs with compute capability 3.7 or 5.0 and higher.
  • In a fresh Wolfram Language installation on Linux and Windows machines, TargetDevice->"GPU" and related GPU settings will automatically download additional libraries. Such a download can also trigger again in case updates are available. The downloads can also be started manually by running PacletInstall["MXNetResources"], PacletInstall["ONNXRuntimeResources"] and PacletInstall["CUDAResources"].

Examples

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Basic Examples  (1)

Train a net using the system's default GPU:

Evaluate the resulting net using the system's default GPU:

Scope  (6)

Inference  (5)

Evaluate a model on the CPU:

Evaluate a model on the system's default GPU:

Specify the use of CoreML (on macOS ARM64):

Specify the use of a DirectML-compatible card (on Windows x86-64):

Specify the use of a CUDA-compatible card (on Windows x86-64 or Linux x86-64):

Training  (1)

Train a net using a specified CUDA GPU:

Train a net using two specified GPUs, where each GPU receives a batch of 16 training examples per training iteration:

Train a net using all available GPUs:

Possible Issues  (3)

If the system GPU is not supported, the operation will fail:

Some settings are only supported for model evaluation and will fail during training:

There is currently no CUDA support on macOS:

Wolfram Research (2016), TargetDevice, Wolfram Language function, https://reference.wolfram.com/language/ref/TargetDevice.html (updated 2024).

Text

Wolfram Research (2016), TargetDevice, Wolfram Language function, https://reference.wolfram.com/language/ref/TargetDevice.html (updated 2024).

CMS

Wolfram Language. 2016. "TargetDevice." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2024. https://reference.wolfram.com/language/ref/TargetDevice.html.

APA

Wolfram Language. (2016). TargetDevice. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TargetDevice.html

BibTeX

@misc{reference.wolfram_2025_targetdevice, author="Wolfram Research", title="{TargetDevice}", year="2024", howpublished="\url{https://reference.wolfram.com/language/ref/TargetDevice.html}", note=[Accessed: 01-December-2025]}

BibLaTeX

@online{reference.wolfram_2025_targetdevice, organization={Wolfram Research}, title={TargetDevice}, year={2024}, url={https://reference.wolfram.com/language/ref/TargetDevice.html}, note=[Accessed: 01-December-2025]}