CUDA Python

In this introduction, we show one way to use CUDA in Python, and explain some basic principles of CUDA programming. We choose to use the Open Source package Numba. Numba is a just-in-time compiler for Python that allows in particular to write CUDA kernels. Numba is freely available at https://github.com/numba/numba PyCUDA lets you access Nvidia's CUDA parallel computation API from Python. Key Features: Maps all of CUDA into Python. Enables run-time code generation (RTCG) for flexible, fast, automatically tuned codes. Added robustness: automatic management of object lifetimes, automatic error checkin Numba supports CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model. One feature that significantly simplifies writing GPU kernels is that Numba makes it appear that the kernel has direct access to NumPy arrays

An introduction to CUDA in Python (Part 1

There are a few ways to write CUDA code inside of Python and some GPU array-like objects which support subsets of NumPy's ndarray methods (but not the rest of NumPy, like linalg, fft, etc..) PyCUDA and PyOpenCL come closest Writing CUDA-Python¶ The CUDA JIT is a low-level entry point to the CUDA features in Numba. It translates Python functions into PTX code which execute on the CUDA hardware. The jit decorator is applied to Python functions written in our Python dialect for CUDA. Numba interacts with the CUDA Driver API to load the PTX onto the CUDA device and execute

PyCUDA NVIDIA Develope

• Transferring Data¶. The next step in most programs is to transfer data onto the device. In PyCuda, you will mostly transfer data from numpy arrays on the host. (But indeed, everything that satisfies the Python buffer interface will work, even a str.)Let's make a 4x4 array of random numbers
• CUDA is ontwikkeld door NVIDIA en om gebruik te maken van deze computerarchitectuur is er een NVIDIA GPU en een speciale stream processing driver vereist. CUDA werkt alleen op de nieuwere grafische kaarten GeForce 8 serie, die gebruikmaken van de G8x GPUs; NVIDIA garandeert dat programma's ontwikkeld voor de GeForce 8-serie zonder enige aanpassing zullen werken op alle toekomstige NVIDIA grafische kaarten. CUDA geeft ontwikkelaars toegang tot de native instruction-set en geheugen van de omvangr
• Turing T4 GPU block diagram Introduction In this post, you will learn how to write your own custom CUDA kernels to do accelerated, parallel computing on a GPU, in python with the help of numba and CUDA. We will use the Google Colab platform, so you don't even need to own a GPU to run this tutorial

Introduction to Numba: CUDA Programmin

Completeness. PyCUDA puts the full power of CUDA's driver API at your disposal, if you wish. It also includes code for interoperability with OpenGL. Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. Speed. PyCUDA's base layer is written in C++, so all the niceties above are virtually free Package Description. scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit, as well as interfaces to select functions in the CULA Dense Toolkit

CUDA is a parallel computing platform and an API model that was developed by Nvidia. Using CUDA, one can utilize the power of Nvidia GPUs to perform general computing tasks, such as multiplying matrices and performing other linear algebra operations, instead of just doing graphical calculations Optional - To call OpenCV CUDA routines from python, install the x64 bit version of Anaconda3, making sure to tick Register Anaconda as my default Python This guide has been tested against Anaconda with Python 3.7, installed in the default location for a single user and Python 3.8 installed in its own conda environment Beyond CUDA: GPU Accelerated Python for Machine Learning on Cross-Vendor Graphics Cards Made Simple. A practical deep dive into GPU Accelerated Python on cross-vendor graphics cards (AMD, Qualcomm, NVIDIA & friends) building machine learning algorithms using the Vulkan Kompute Python Framework Package Description. scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit, as well as interfaces to select functions in the CULA Dense Toolkit.Both low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy.

Fig 24: Using the IDLE python IDE to check that Tensorflow has been built with CUDA and that the GPU is available Conclusions. These were the steps I took to install Visual Studio, CUDA Toolkit, CuDNN and Python 3.6, all with the ultimate aim of installing Tensorflow with GPU support on Windows 10 scikit-cuda¶. scikit-cuda provides Python interfaces to many of the functions in the CUDA device/runtime, CUBLAS, CUFFT, and CUSOLVER libraries distributed as part of NVIDIA's CUDA Programming Toolkit, as well as interfaces to select functions in the CULA Dense Toolkit.Both low-level wrapper functions similar to their C counterparts and high-level functions comparable to those in NumPy and. PyCUDA has compiled the CUDA source code and uploaded it to the card. Note. This code doesn't have to be a constant-you can easily have Python generate the code you want to compile. See Metaprogramming. PyCUDA's numpy interaction code has automatically allocated space on the device,. CUDA Python¶ We will mostly foucs on the use of CUDA Python via the numbapro compiler. Low level Python code using the numbapro.cuda module is similar to CUDA C, and will compile to the same machine code, but with the benefits of integerating into Python for use of numpy arrays, convenient I/O, graphics etc. Optionally, CUDA Python can provid Configure an Install TensorFlow 2.0 GPU (CUDA), Keras, & Python 3.7 in Windows 10 Configure TensorFlow To Train an Object Detection Classifier How To Train an Object Detection Classifier Using.

cuda - Python GPU programming - Stack Overflo

1. See how to install CUDA Python followed by a tutorial on how to run a Python example on a GPU.Find code used in the video at:.
2. Add the CUDA®, CUPTI, and cuDNN installation directories to the %PATH% environmental variable. For example, if the CUDA® Toolkit is installed to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0 and cuDNN to C:\tools\cuda, update your %PATH% to match
3. g is required
4. OpenCV 4.5.0 (changelog) which is compatible with CUDA 11.1 and cuDNN 8.0.4 was released on 12/10/2020, see Accelerate OpenCV 4.5.0 on Windows - build with CUDA and python bindings, for the updated guide. Because the pre-built Windows libraries available for OpenCV 4.3.0 do not include the CUDA modules, or support for the Nvidia Video Codec [
5. # Python 2/3 compatibility: from __future__ import print_function: import numpy as np: import cv2 as cv: import os: from tests_common import NewOpenCVTests, unittest: class cuda_test (NewOpenCVTests): def setUp (self): super (cuda_test, self). setUp if not cv. cuda. getCudaEnabledDeviceCount (): self. skipTest (No CUDA-capable device is.
6. Accelerate Python Functions. Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN

Writing CUDA-Python — numba 0

• not using CUDA algorithms available with the Python API. Unfortunately, only one background subtractor (MOG2) is available as implementations for CPU and GPU/CUDA with the Python API. KNN and MOG are available for CPU or CUDA only. Let's have a look at the results (source code at the end of this page): Let's zoom into the lower FPS region
• CUDA C++ extends C++ by allowing the programmer to define C++ functions, called kernels, that, when called, are executed N times in parallel by N different CUDA threads, as opposed to only once like regular C++ functions.. A kernel is defined using the __global__ declaration specifier and the number of CUDA threads that execute that kernel for a given kernel call is specified using a new.
• s) > Learn CUDA's parallel thread hierarchy and how to extend parallel program possibilities. > Launch massively parallel custom CUDA kernels on the GPU. > Utilize CUDA atomic operations to avoid race conditions during parallel execution. Break (15
• An introduction to CUDA in Python (Part 2) @Vincent Lunot · Nov 26, 2017. In the first part of this introduction, we saw how to launch a CUDA kernel in Python using the Open Source just-in-time compiler Numba. In this part, we will learn more about CUDA kernels. Thread Indexing
• g. PyCUDA's numpy interaction code has automatically allocated space on the device,.
• How to use CUDA and the GPU Version of Tensorflow for Deep Learning. If you plan to be using the super user (sudo) with Python, then you will want to add the above export code to /etc/environment, otherwise you will fail at importing cuDNN. Whew, okay, step 2 completed

Installation of Python Deep learning on Windows 10 PC to utilise GPU may not be a straight-forward process for many people due to compatibility issues. One good and easy alternative is to use. Initialize PyTorch's CUDA state. You may need to call this explicitly if you are interacting with PyTorch via its C API, as Python bindings for CUDA functionality will not be available until this initialization takes place. Ordinary users should not need this, as all of PyTorch's CUDA methods automatically initialize CUDA state on-demand

Video: Tutorial - PyCUDA 2020

Boost python with numba + CUDA! (c) Lison Bernet 2019 Introduction In this post, you will learn how to do accelerated, parallel computing on your GPU with CUDA, all in python! This is the second part of my series on accelerated computing with python: Part I : Make python fast with numba : accelerated python on the CP Using latest version of Tensorflow provides you latest features and optimization, using latest CUDA Toolkit provides you speed improvement with latest gpu support and using latest CUDNN greatly improves deep learing training time. There must be 64-bit python installed tensorflow does not work on 32-bit python installation NVIDIA CUDA Toolkit 5.0 or later. Note that both Python and the CUDA Toolkit must be built for the same architecture, i.e., Python compiled for a 32-bit architecture will not find the libraries provided by a 64-bit CUDA installation. CUDA versions from 7.0 onwards are 64-bit. To run the unit tests, the following packages are also required CUDA Python Specification (v0.2)¶ (This documents reflects the implementation of CUDA Python in NumbaPro 0.12. In time, we may refine the specification.) As usage of Python on CUDA GPUs is becoming more mature, it has become necessary to define a formal specification for a dialect and its mapping to the PTX ISA nvidia/cuda:10.2-devel is a development image with the CUDA 10.2 toolkit already installed Now you just need to install what we need for Python development and setup our project

CUDA - Wikipedi

• Actually why python is so popular in machine learning domain is that you can easily call c / c++ code because of its dynamic typing(or something). The number crunching / heavy lifting is always done in c / c++. So you can think about making cuda calls from python but most likely you will need to write a c / c++ wrapper anyway
• al and ente
• In this CUDACast video, we'll see how to write and run your first CUDA Python program using the Numba Compiler from Continuum Analytics
• numba.cuda.cudadrv.driver.CudaAPIError: [1] Call to cuLaunchKernel results in CUDA_ERROR_INVALID_VALUE Even when I got close to the limit the CPU was still a lot faster than the GPU. $ python speed.py cpu 100000 Time: 0.0001056949986377731 $ python speed.py cuda 100000 Time: 0.11871792199963238 $ python speed.py cpu 11500000 Time: 0.013704434997634962 $ python speed.py cuda 11500000 Time: 0.

Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the CUDA C Programming Guide, located in the CUDA Toolkit documentation directory Python model.cuda() Examples The following are 14 code examples for showing how to use model.cuda(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example

Nvidia Cuda can accelerate C or Python by GPU power. And we can test Cuda with Docker. Here is the menu. ������1. Install CMake ������2. Install DLib with Python 3 ������3. +NVidia CUDA 11 Support ������1. Install CMake. CMake is a great C-tool compiler. Especially, CMake-Gui, it can let you compile source code on GUI level Accelerating python -cudamat provides a CUDA-based python matrix class Primary goal: easy dense matrix manipulation Useful to perform matrix ops on GPU Perform many matrix operations multiplication and transpose Elementwise addition, subtraction, multiplication, and division Elementwise application of exp, log, pow, sqr Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book This is going to be a tutorial on how to install tensorflow 1.8.0 GPU version. We will also be installing CUDA 9.2 and cuDNN 7.1.4 along with the GPU version of tensorflow 1.8.0. At the time of writing this blog post, the latest version of tensorflow is 1.8.0.This tutorial is for building tensorflow from source

CUDA kernels in python - The Data Fro

1. Numba.cuda.jit allows Python users to author, compile, and run CUDA code, written in Python, interactively without leaving a Python session. Here is an image of writing a stencil computation that smoothes a 2d-image all from within a Jupyter Notebook
2. For Cuda test program see cuda folder in the distribution. Pyfft tests were executed with fast_math=True (default option for performance test script). In the following tables sp stands for single precision, dp for double precision. Mac OS 10.6.6, Python 2.6, Cuda 3.2, PyCuda 2011.1, nVidia GeForce 9600M, 32 Mb buffer
3. OpenCV, CUDA, Python with Jetson Nano. Autonomous Machines. Jetson & Embedded Systems. Jetson Nano. drakorg. February 1, 2020, 8:04am #21. I'm not sure what you mean by checking cmake outputs, or how to confirm they point to the python and numpy files. Andrey1984
4. CUDA (Compute Unified Device Architecture) is a parallel computing platform and API created by Nvidia. This instructor-led, live training (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs
5. Combining Python/CUDA JIT Compilation for Flexible Acceleration in RAPIDS. In this blog, we'll introduce our design and implementation of a framework within RAPIDS cuDF that enables compiling Python user-defined functions (UDF) and inlining them into native CUDA kernels.Our framework uses the Numba Python compiler and Jitify CUDA just-in-time (JIT) compilation library to provide cuDF users.
6. ute read K-means is a popular clustering algorithm that is not only simple, but also very fast and effective, both as a quick hack to preprocess some data and as a production-ready clustering solution

Hands-On GPU Programming with Python and CUDA will help you discover ways to develop high performing Python apps combining the power of Python and CUDA. This book will help you hit the ground running-you'll start by learning how to apply Amdahl's law, use a code profiler to identify bottlenecks in your Python code, and set up a GPU programming environment I need a torch version with cuda that can be installed on python 2.7 for windows 10 and works with the cuda 11.1 toolkit. Thank you all in advance! ptrblck. January 19, 2021, 12:03am #2. As you've already mentioned, the current PyTorch binaries are not built anymore for Python2.7. I would. CUDA-GDB is an extension to the x86-64 port of GDB, the GNU Project debugger. CUDA-MEMCHECK CUDA-MEMCHECK is a suite of run time tools capable of precisely detecting out of bounds and misaligned memory access errors, checking device. Hello, I´m using the cv2.calcOpticalFlowFarneback() function on my Jetson TX2 with Python, but unfortunately I´m not getting any improvement in performance after building OpenCV 4.1.2 from source with CUDA support. As long as I know, since OpenCV4, all the functions are optimized and integrated by default, but apparently CUDA optimized functions are not implemented with python Wrappers I am new to cuda and have followed instructions here- https://developer.nvidia.com/how-to-cuda-python GPU - geforce 940mx cudatoolkit - 8.0 numba - 0.37.0 python - 3.

In contrast, Python is a high-level language that places emphasis on ease-of-use over speed. This updated second edition follows a practical approach to teaching you efficient GPU programming techniques with the latest version of Python and CUDA Toggle navigation Andreas Klöckner's web page. About; Research; Teaching; Archives; PyCUD Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA (English Edition) eBook: Tuomanen, Dr. Brian: Amazon.nl: Kindle Stor Python. C++ / Java. CUDA. 9.2. 10.1. 10.2. 11.0. None. Run this Command: conda install pytorch torchvision -c pytorch. Previous versions of PyTorch Quick Start With Cloud Partners. Get up and running with PyTorch quickly through popular cloud platforms and machine learning services Custom CUDA Kernels in Python with Numba. Learn CUDA's parallel thread hierarchy; Launch massively parallel custom CUDA kernels on the GPU; Utilize atomic operations to avoid race conditions during parallel execution. Learn how to extend parallel program possibilities,. including the ability to design and write flexible and powerful CUDA kernels

In this video from the ECSS Symposium, Abe Stern from NVIDIA presents: CUDA-Python and RAPIDS for blazing fast scientific computing.. We will introduce Numba and RAPIDS for GPU programming in Python. Numba allows us to write just-in-time compiled CUDA code in Python, giving us easy access to the power of GPUs from a powerful high-level language Verify if CUDA is available to PyTorch. To check if your GPU driver and CUDA are accessible by PyTorch, use the following Python code to decide if or not the CUDA driver is enabled: import torch torch.cuda.is_available() In the case of people who are interested, the following two parts introduce PyTorch and CUDA. What is PyTorch

pycuda · PyP

1. from C to Python with ctypes, so it can run without compiling anything. Note: that this is a direct translation with no attempt to make the code Pythonic. It's meant as a general demonstration on how to obtain CUDA device information: from Python without resorting to nvidia-smi or a compiled Python extension. Author: Jan Schlüter import.
2. PyTorch is a machine learning package for Python. This code sample will test if it access to your Graphical Processing Unit (GPU) to use CUDA <pre>from __future__ import print_function import torch x = torch.rand(5, 3) print(x) if not torch.cuda.is_available(): print (Cuda is available) device_id = torch.cuda.current_device() gpu_properties = torch.cuda.get_device_properties(device_id.
3. #include <opencv2/core/cuda.hpp> Returns the number of installed CUDA-enabled devices. Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, this function returns 0
4. Hi guys. I am a newbie in pytorch. My Ubuntu 16.04.02 LTS got python2.7 and cuda 9.0 installed (torch.cuda.is_available() returns True). However, when I tried run the following, my python just got frozen and I can only

scikit-cuda · PyPI - PyPI · The Python Package Inde

• This is going to be a tutorial on how to install tensorflow 1.12 GPU version. We will also be installing CUDA 10.0 and cuDNN 7.3.1 along with the GPU version of tensorflow 1.12. At the time of writing this blog post, the latest version of tensorflow is 1.12. This tutorial is for building tensorflow from source. If you want to use the official pre-built pip package instead, I recommend another.
• Prerequisite. This tutorial assumes you have CUDA 10.0 installed and you can run python and a package manager like pip or conda.Miniconda and Anaconda are both fine. We wrote an article on how to install Miniconda.. 5 Steps to Install PyTorch With CUDA 10.
• Numba CUDA Python 120 > Python Numba CUDA > Numba Python > 60 Numba Python CUDA 120 > CUDA > GPU CUDA > CUDA . 15 Numba CUDA Python RNG 120 > xoroshiro128 + RNG GPU > 2D > 2D GPU 15 > > > • CUDA C/C++ CUDA Nsight Systems profiler C/C++ CPU GPU • CUDA C/C++ GPU GPU GPU.
• python和cuda交互：Pycuda安装（填坑） 国家二级退堂骨演奏家 回复 北漂客: 谢谢楼主，准备换装备了。 python和cuda交互：Pycuda安装（填坑） 北漂客 回复 国家二级退堂骨演奏家: 首先确认安装好cuda，cudnn 本人电脑： cuda9.0 cudnn7.3这些版本需要对应�
• python对CUDA扩展有不错的支持，CUDA通过大量线程的并行化可以大幅提高代码计算速度，一般python常用numba、pycuda套件来支持CUDA扩展。numba通过JIT编译器只需将nu
• Geometric Deep Learning Extension Library for PyTorch (with CUDA
• Python, OpenGL and CUDA/CL. @for Developers @author Kai Ruhl @since 2011-09. Python is a nice language for prototyping things. It can wrap C++ libraries (required for performance sensitive parts) quite well, as evident e.g. by the fast numpy (array manipulation) library

CUDA Tutorial - Tutorialspoin

1. g a reduction on CUDA; Recreational; More examples; Writing CUDA in C. Review of GPU Architechture - A Simplification; Cuda C program - an Outline; Distributed computing for Big Data. Why and when does distributed computing matter
2. Install MXNet (with Anaconda Python 3, CUDA, cuDNN, Intel MKL, OpenCV, Zsh) for p2.xlarge on Ubuntu 16.04 (ami-6f587e1c). - install_mxnet_p2xlarge.s
3. Installing the Python API. A Python script is available in the ZED SDK installation folder and can automatically detect your platform, CUDA and Python version and download the corresponding pre-compiled Python API package. Running the install script. Windows. The Python install script is located in C:\Program Files (x86)\ZED SDK\
4. Numba's pipeline does this with a sequence of stages, each moving the representation further from the Python source, and closer to executable machine code: The Numba compilation pipeline starts with Python source code and takes it through the following stages to generate PTX code for CUDA GPUS. We'll walk through seven stages of the pipeline
5. The other day, I was looking to read an Arrow buffer on GPU using Python, but as far as I could tell, none of the provided pyarrow packages on conda or pip are built with CUDA support
6. 一定要注意的是，cuda_visible_devices，每!一!个!字!母! 必须完全写对，如果不小心写成了cuda_visible_divices或者cuda_visiable_devices ，代码是不会报错的，但是gpu调用不会成功， 以上就是python怎么利用gpu加速的详细内容，更多请关注php中文网其它相关文章
7. Class-based version. Here the gpu array can be persistent over multiple calls. This can be very useful when the array is large and the function is called many times

Local CUDA/NVCC version has to match the CUDA version of your PyTorch. Both can be found in python-m detectron2.utils.collect_env. When they are inconsistent, you need to either install a different build of PyTorch (or build by yourself) to match your local CUDA installation, or install a different version of CUDA to match PyTorch To write kernels in CUDA C++ and run them from Python, pyCUDA seems to be a good option. To use CUDA libraries that already offer a high level C++ driver interface and which do not have a Python binding yet, cython as suggested by u/lxkarthi would be a good option. Hope this is helpful. 1. Reply. share. Report Save python-pytorch_geometric (requires python-pytorch_spline_conv) python-pytorch_geometric-cuda (requires python-pytorch_spline_conv) python-pytorch_geometric-cuda

Accelerate OpenCV 4

• CUDA_PROFILE=1 python scriptname.py After running this code in current directory there will be file cuda_profile_0.log containing names of kernels that were run and time it took to run each kernel, both on CPU and on GPU
• Build a TensorFlow pip package from source and install it on Windows.. Note: We already provide well-tested, pre-built TensorFlow packages for Windows systems. Setup for Windows. Install the following build tools to configure your Windows development environment. Install Python and the TensorFlow package dependencie
• 【Python】Windows10でCuPyを導入してCUDAコアを利用するまで Python CUDA Python3 CuPy CUDA10.2 導入でハマったので記事にしました�

PythonからCUDAを使えるとCUDA Cで面倒なメモリ管理が楽になるだけでなく、ファイル入出力や可視化もPythonのライブラリが利用できるので非常に多くのメリットを持ちます。 本コースとは. 本コースは、PyCUDAを使ったGPU並列計算の入門講座です� Install TensorFlow. This section provides instructions for installing or downgrading TensorFlow on Databricks Runtime for Machine Learning and Databricks Runtime, so that you can try out the latest features in TensorFlow.Due to package dependencies, there might be compatibility issues with other pre-installed packages OpenCV Pre-built CUDA binaries. This project is now hosted as the nuget packages : Supports all major package types (over 27 and growing) such as Maven, npm, Python, NuGet, Gradle, Go, and Helm including Kubernetes and Docker as well as integration with leading CI servers and DevOps tools that you already... See Software. MyGet

Beyond CUDA: GPU Accelerated Python for Machine Learning

-DEIGEN3_INCLUDE_DIR =../../eigen -DPYTHON = ` which python `-DBACKEND = cuda make -j 2 # replace 2 with the number of available cores cd python python./../setup.py build --build-dir =.. --skip-build install # add `--user` for a user-local install. # this should suffice, but on some systems you may need to add the following line to your # init files in order for the compiled .so files be. This post is the needed update to a post I wrote nearly a year ago (June 2018) with essentially the same title. This time I have presented more details in an effort to prevent many of the gotchas that some people had with the old guide. This is a detailed guide for getting the latest TensorFlow working with GPU acceleration without needing to do a CUDA install OpenCV-Python Tutorials¶. OpenCV introduces a new set of tutorials which will guide you through various functions available in OpenCV-Python. This guide is mainly focused on OpenCV 3.x version (although most of the tutorials will work with OpenCV 2.x also).. A prior knowledge on Python and Numpy is required before starting because they won't be covered in this guide Python model 模块， cuda() 实例源码. 我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用model.cuda()�