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CUDA Execution Provider

The CUDA Execution Provider enables hardware accelerated computation on Nvidia CUDA-enabled GPUs.

Contents

Install

Pre-built binaries of ONNX Runtime with CUDA EP are published for most language bindings. Please reference Install ORT.

Requirements

Please reference table below for official GPU packages dependencies for the ONNX Runtime inferencing package. Note that ONNX Runtime Training is aligned with PyTorch CUDA versions; refer to the Training tab on https://onnxruntime.ai/ for supported versions.

ONNX Runtime CUDA cuDNN Notes
1.9 11.4 8.2.4 (Linux)
8.2.2.26 (Windows)
libcudart 11.4.43
libcufft 10.5.2.100
libcurand 10.2.5.120
libcublasLt 11.6.1.51
libcublas 11.6.1.51
libcudnn 8.2.4
libcupti.so 2021.2.2
1.8 11.0.3 8.0.4 (Linux)
8.0.2.39 (Windows)
libcudart 11.0.221
libcufft 10.2.1.245
libcurand 10.2.1.245
libcublasLt 11.2.0.252
libcublas 11.2.0.252
libcudnn 8.0.4
libcupti.so 2020.1.1
1.7 11.0.3 8.0.4 (Linux)
8.0.2.39 (Windows)
libcudart 11.0.221
libcufft 10.2.1.245
libcurand 10.2.1.245
libcublasLt 11.2.0.252
libcublas 11.2.0.252
libcudnn 8.0.4
1.5-1.6 10.2 8.0.3 CUDA 11 can be built from source
1.2-1.4 10.1 7.6.5 Requires cublas10-10.2.1.243; cublas 10.1.x will not work
1.0-1.1 10.0 7.6.4 CUDA versions from 9.1 up to 10.1, and cuDNN versions from 7.1 up to 7.4 should also work with Visual Studio 2017

For older versions, please reference the readme and build pages on the release branch.

Build

For build instructions, please see the BUILD page.

Configuration Options

The CUDA Execution Provider supports the following configuration options.

device_id

The device ID.

Default value: 0

gpu_mem_limit

The size limit of the device memory arena in bytes. This size limit is only for the execution provider’s arena. The total device memory usage may be higher. s: max value of C++ size_t type (effectively unlimited)

arena_extend_strategy

The strategy for extending the device memory arena.

Value Description
kNextPowerOfTwo (0) subsequent extensions extend by larger amounts (multiplied by powers of two)
kSameAsRequested (1) extend by the requested amount

Default value: kNextPowerOfTwo

The type of search done for cuDNN convolution algorithms.

Value Description
EXHAUSTIVE (0) expensive exhaustive benchmarking using cudnnFindConvolutionForwardAlgorithmEx
HEURISTIC (1) lightweight heuristic based search using cudnnGetConvolutionForwardAlgorithm_v7
DEFAULT (2) default algorithm using CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM

Default value: EXHAUSTIVE

do_copy_in_default_stream

Whether to do copies in the default stream or use separate streams. The recommended setting is true. If false, there are race conditions and possibly better performance.

Default value: true

Samples

Python

import onnxruntime as ort

model_path = '<path to model>'

providers = [
    ('CUDAExecutionProvider', {
        'device_id': 0,
        'arena_extend_strategy': 'kNextPowerOfTwo',
        'gpu_mem_limit': 2 * 1024 * 1024 * 1024,
        'cudnn_conv_algo_search': 'EXHAUSTIVE',
        'do_copy_in_default_stream': True,
    }),
    'CPUExecutionProvider',
]

session = ort.InferenceSession(model_path, providers=providers)

C/C++

OrtSessionOptions* session_options = /* ... */;

OrtCUDAProviderOptions options;
options.device_id = 0;
options.arena_extend_strategy = 0;
options.gpu_mem_limit = 2 * 1024 * 1024 * 1024;
options.cudnn_conv_algo_search = OrtCudnnConvAlgoSearch::EXHAUSTIVE;
options.do_copy_in_default_stream = 1;

SessionOptionsAppendExecutionProvider_CUDA(session_options, &options);