Usually the nvcc application is found in the C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10. Note that you can just as well keep your data on the card between kernel invocations–no need to copy data all the time. "sudo yum remove python-pip python-dev" works fine for CentOS 7 - Drasius 15 hours ago. First introduced in 2008, Visual Profiler supports all 350 million+ CUDA capable NVIDIA GPUs shipped since 2006 on Linux, Mac OS X, and Windows. I am trying to install pgadmin4 using Docker in Ubuntu 18. Global Memory and Special-Purpose Memory. SUID is defined as giving temporary permissions to a user to run a program/file with the permissions of the file owner rather that the user who. NVIDIA CUDAに関する質問です。Visual Profilerというものを使ってみようと思いCUDA toolkitの中をいろいろ探してはみたものの、それらしきものがみつからないのですが、どなたか、Visual Profilerのインストールディレクトリをご存知の方いらっしゃらないでしょうか。ちなみに環境はUbuntu12. 5 를 컴파일 할 수 있는 방법이 있습니까?. * Fix typos, hyphenation, and sections in the manpages. If you installed torch with the ezinstall method it comes with luarocks and installing e. NVIDIA designed NVIDIA-Docker in 2016 to enable portability in Docker images that leverage NVIDIA GPUs. It can also be used to simply launch the target application (see General for details) and later attach with NVIDIA Nsight Compute or another nv-nsight-cu-cli instance. CUDA TOOLKIT MAJOR COMPONENTS This section provides an overview of the major components of the CUDA Toolkit and points to their locations after installation. Although the EVGA card is boosting, on average, just as high if not higher than the Gigabyte blower 2080 Ti, I'm getting way low. 265 video encode/decode performance on AWS p3 instances. Usually, this issue can be solved by simply restarting the router. This manual is intended for scientists and engineers using the PGI compilers. MXNet’s Profiler is definitely the recommended starting point for profiling MXNet code, but NVIDIA also provides a couple of tools for low level profiling of CUDA code: Visual Profiler and Nsight Compute. $ nvprof-o my_profile. You can initiate the profiling directly from inside Visual Profiler or from the command line with nvprof which wraps the execution of your Python script. NVProf with Spectrum MPI. Most voted files : total uninstall pro 5. … Read more. Written in C++; Direct access to performance analysis data in Python and C++; Create your own components: any one-time measurement or start/stop paradigm can be wrapped with timemory. /vector_add ==6326== Profiling result: Time(%) Time Calls Avg Min Max Name 97. Sftp these to a machine where you can run the Nvidia Visual Profiler GUI, then open the GUI and import the profiles via. Tools to help working with nvprof SQLite files, specifically for profiling scripts to train deep learning models. But before we begin, here is the generic form that you can use to uninstall a package in Python: Now, let’s suppose that you already installed the pandas package using the PIP install method. ; click the nvprof. It will show you that your code is running on the GPU and also give you performance information about the code. In a computing application, this roughly implies that a thread is likely to read from an. crt cuda-install-samples-10. For fresh installation, we can religiously follow the installation instruction displayed on the download page: – Install CUDA repository metada. On Turing, kernels using Tensor Cores may. This tool is aimed in extracting the small bits of important information and make profiling in NVVP faster. nvprofを使って、GPUの計算時間を計測する。 >> nvprof. net * epel: mirrors. to fully remove those you can use sudo apt purge python-pip python-dev <-- this will delete all the files/directories/binaries created by that package. Delivered every other week to your inbox, "Latest Developer News from NVIDIA" is a curated email that compiles the latest GPU-accelerated news, product announcements, and resources published on the NVIDIA Developer News Center and Developer Blog. sh: Install the Gnome tweaks tool (sudo apt-get install gnome-tweak-tool) and the Chrome Gnome plugin (sudo apt-get install chrome-gnome-shell). Tools to help working with nvprof SQLite files, specifically for profiling scripts to train deep learning models. Figure 3: To get started with the NVIDIA Jetson Nano AI device, just flash the. nvprof with the nvvp tool, which might take several minutes to open the data. 1 Verifying the Installation. Note that Visual Profiler and nvprof will be deprecated in a future CUDA release. inp -o test. Work with project team to install, configure, utilize, and test various aspects of the Bro Network Security Monitor and related libraries, such as the “Broker” Client Communications library, as well as database back ends and messaging libraries, to capture, merge, analyze, and store network traffic and other types of data. Installing docker. Page 1 of 2 - Random power loss under load - posted in Internal Hardware: The issue: When I say random, I mean random. Preface: We want to emphasis that this document is a note on our OpenMP 4. ‣ Visual Profiler and nvprof now support a new application replay mode for. The files can be big and thus slow to scp and work with in NVVP. NVIDIA, NVPROF and NVVP. If it is not correct, enter the correct path to CUDA Enter CUDA install path (default /usr/local/cuda): CUDAがインストールされているパスを聞いてくる。 デフォルトの通りなので単に「ENTER」を押す。. 1/bin/ nvprof This comment has been minimized. Deprecated: Function create_function() is deprecated in /www/wwwroot/dm. There's something very strange going on. 选择install 安装完juno后,他会自己给你安装一些他需要的扩展: 右上角安装完成后重启Atom就有Juno可以用了,就这样: 然后我们就成功了, 当网速不那么流畅的时候稍微等一会儿,juno安装完成会提示你。不要着急。. Reinstall the driver using the custom option and then select the clean install option. CUDA programming is all about performance. 1 Verifying the Installation. Installation on Windows 10-bit to 32 CUDA SDK 6. NVIDIA Nsight Systems Following the deprecation of above tools, NVIDIA published the Nsight Systems and Nsight Compute tools for respectively timeline profiling and more detailed kernel analysis. Project description Release history Download files Project links. Global Memory and Special-Purpose Memory. /vector_add ==6326== Profiling result: Time(%) Time Calls Avg Min Max Name 97. Caliper: A Performance Analysis Toolbox in a Library¶. HIP provides lightweight header files that map the HIP runtime calls back to CUDA, where they use the standard CUDA runtime. /program 来查看各部分时间, (1)但是现在的程序已经改为多节点多卡MPI版本(准确说是4节点8卡)如何继续查看各部分使用时间?. How to install CUDA for gtx 970 on Windows Hello, I would like to know if I can download the latest cuda driver or if I have to download an older version. Installing MPI in Linux Abu Saad Papa This document describes the steps used to install MPICH2, the MPI-2 implementation from Argonne National Laboratory in UNIX (Fedora Core 4) based system. At first glance, nvprof seems to be just a GUI-less version of the graphical profiling features available in the NVIDIA Visual Profiler and NSight Eclipse edition. inp -o test. However, there may be compatibility issues when executing the generated code from MATLAB as the C/C++ run-time libraries that are included with the MATLAB installation are compiled for GCC 6. 00 KB (202752 bytes). Update apt package index and install the newest version of all currently installed packages $ sudo apt-get update $ sudo apt-get upgrade. * Add compat symlinks for libcuinj{32,64}. We use cookies for various purposes including analytics. e)cuda-memcheck:- CUDA-MEMCHECK is a functional correctness checking suite included in the CUDA toolkit. Tools to help working with nvprof SQLite files, specifically for profiling scripts to train deep learning models. NVTX functions with such postfix exist in multiple variants, performing the same core functionality with different parameter encodings. CUDA programming is all about performance. 12 where CUDA profiling tools (e. In this post I'll go through the basic install and setup for Docker and NVIDIA-Docker. Fixed an issue where the Tesla driver would result in installation errors on some Windows Server 2012 systems. It enables data scientists to build environments once - and ship their training/deployment quickly. For example, to install only the compiler and the occupancy calculator, use the following command −. Otherwise, there's no IB in VM. But after I compile the executable files and run, it tells me driver not compatible with this version of CUDA. Performance Optimization Strategies for GPU-accelerated Apps Author: David Goodwin Subject: Strategies to identify optimization opportunities in your app; discuss the steps you can take to turn those opportunities into actual performance improvement. 5 RN-06722-001 _v6. The NVIDIA Visual Profiler is available as part of theCUDA Toolkit. php on line 143 Deprecated: Function create_function() is deprecated in. 30 NVPROF -MPI Profiling NVPROF & Visual Profiler do not natively understand MPI It is possible to load data from multiple MPI ranks (same or different GPUS) into. Soll der Grafiktreiber jedoch installiert werden muss man sich zuvor ausloggen, ohne X Server (im Terminal) wieder einloggen und die Installation erneut starten (nicht empfohlen). Use the base installer to install CUDA toolkit and driver packages. so - The NVIDIA cuRAND Library libnppc. The Analyze Execution Profiles of the Generated Code workflow depends on the nvprof tool from NVIDIA. SQL Server memory performance metrics – Part 4 – Buffer Cache Hit Ratio and Page Life Expectancy March 5, 2014 by Milena Petrovic In SQL Server performance metrics – part 3 , we presented some of the SQL Server Buffer Manager metrics. At the installation of the Alea NuGet package, an MSBuild task is added into your project. Note that we don't immediately start the profiler, but instead call into the CUDA APIs using CUDAdrv. 6 kB 00:00:00 cuda | 2. Multiple presentations about OpenMP 4. 6689ms 32 302. The Analyze Execution Profiles of the Generated Code workflow depends on the nvprof tool from NVIDIA. However, there may be compatibility issues when executing the generated code from MATLAB as the C/C++ run-time libraries that are included with the MATLAB installation are compiled for GCC 6. nvprof profiles only one task at a time; if one profiles a GPU code which has multiple tasks (e. MXNet’s Profiler is definitely the recommended starting point for profiling MXNet code, but NVIDIA also provides a couple of tools for low level profiling of CUDA code: Visual Profiler and Nsight Compute. But nvprof is much more than that; to me, nvprof is the light-weight profiler that reaches where other tools can’t. exe process file then click the right mouse button then from the list select "Add to the block list". A metric is a characteristic of an application that is calculated from one or more event values. Some of the NVTX functions are defined to have return values. Soll der Grafiktreiber jedoch installiert werden muss man sich zuvor ausloggen, ohne X Server (im Terminal) wieder einloggen und die Installation erneut starten (nicht empfohlen). nvprof also enables you to collect events/metrics for CUDA kernels. GPU: Within the field of parallel computing we refer to our GPUs as devices. In this video from the GPU Technology Conference, Guido Juckeland from ZIH presents: Showing the Missing Middle: Enabling OpenACC Performance Analysis. 50K, threads running on the device. In contrast to the Nsight IDE, we can freely use any Python code that we have written—we won't be compelled here to write full-on, pure CUDA-C test function code. Usually the nvcc application is found in the C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10. Fixed an issue in 390. It can also be used to simply launch the target application (see General for details) and later attach with NVIDIA Nsight Compute or another nv-nsight-cu-cli instance. 0-1xenial-20191219-102913+0000 1. To do it on Catalina you need to add a line. Infrastructure. For fresh installation, we can religiously follow the installation instruction displayed on the download page: - Install CUDA. using Nsight IDE and nvprof. The columns show the percentage of execution time, the actual time, the number of calls, the average-min-max of a single call for every kernel. - This release contains the following: NVIDIA CUDA Toolkit documentation NVIDIA CUDA compiler (NVCC) and supporting tools NVIDIA CUDA runtime libraries NVIDIA CUDA-GDB debugger. 6689ms 32 302. Start timing scope for this object. Visitors are welcome to browse and search, but you must login to contribute to the forums. Libraries’ basics Lab: nvprof, nvvp, measure performance, locate bottlenecks – Thursday Jan 12 Numerical libraries: dense, batch Lab: profiling libraries – Friday Jan 13 Numerical libraries: batch (cont. 0 | ii CHANGES FROM VERSION 7. In this short tutorial, I’ll show you how to use PIP to uninstall a package in Python. Find tips for using distributed deep learning (DDL). nvprof is quite flexible, so make sure you check out the documentation. Learn more about mdcs, matlab distributed computing server, libcuda, mjs, matlab job scheduler MATLAB, MATLAB Parallel Server, Parallel Computing Toolbox. Testing Practices Overview. so - The NVIDIA cuSPARSE Library libcusolver. Tensor コア使っているか見れる $ nvcc ~~~ (未使用) nvprof のコマンドを GUI でリッチに見れるらしい。 元のコードに対し数行足すだけで Mixed Precision Training できるとのこと ただし install 時は CUDA や PyTorch のバージョンに気をつけないといけない 9. SUID is defined as giving temporary permissions to a user to run a program/file with the permissions of the file owner rather that the user who. sh nvcc nvprof cudafe++ cuda-memcheck nvcc. nvprof, 3215. h, whereas domain-specific extensions to the NVTX interface are exposed in separate header files. 12 where CUDA profiling tools (e. However, there may be compatibility issues when executing the generated code from MATLAB as the C/C++ run-time libraries that are included with the MATLAB installation are compiled for GCC 6. The output can be visualized with kcachegrind or the Eclipse Linux Tools. /vector_add Following is an example profiling result on Tesla M2050 ==6326== Profiling application:. The profiling tools contain below changes as part of the CUDA Toolkit 10. This manual is intended for scientists and engineers using the PGI compilers. However, each file must have a unique filename, or else all nvprof tasks will attempt to write to the same file, typically resulting in an unusuable profiling. はじめに UbuntuにCUDAをインストールする際にちょくちょく詰まったのでメモ. 研究室向けWikiに書いた内容とほぼ同一です. 実行時のシステム構成 ThinkPad T450 20BV001LJP CPU:. nvprof -o log. "Invalid Cross-Reference Format" lists the options in alphabetical order, with a brief description of each. … Read more. Details I want to use CUDA for neural network inference. sh gpu-library-advisor nvdisasm nvvp. 1573ms cuDeviceTotalMem 1. NVIDIA® Visual Profiler Standalone (nvvp) Integrated into NVIDIA® Nsight™ Eclipse Edition (nsight) NVIDIA® Nsight™ Visual Studio Edition nvprof Command-line Driver-based profiler still available Command-line, controlled by environment variables. 001708 sec Arrays match == 1520 = = Profiling application:. It requires minimal changes to the existing code - you only need to declare Tensor s for which gradients should be computed with the requires_grad=True keyword. sopt -i test. out # you can also add --log-file prof The default output includes 2 sections: one related to kernel and API calls; another related to memory; Execution configuration. Instructor Note: At this point, walk through the writeSegment. "sudo yum remove python-pip python-dev" works fine for CentOS 7 - Drasius 15 hours ago. 12 where CUDA profiling tools (e. 265 video encode/decode performance on AWS p3 instances. 2 to Table 14. 04 GPU: GeForce GTX 1080 手順 nvidia-dr. Sftp these to a machine where you can run the Nvidia Visual Profiler GUI, then open the GUI and import the profiles via. Overall goal, follow the open source ecosystem for infrastructure choices. Installation also involves instantiating "listener" modules, as specified. 0 support on NVIDIA GPUs date back to 2012. They occupy 73. Find tips for using distributed deep learning (DDL). ; click the nvprof. 0 compiles marker support by default, and you can enable it by setting the HIP_PROFILE_API environment variable and then running the rocm-profiler:. exe from the shell, passing these arguments: msiexec. Programs written using CUDA harness the power of GPU. The Purchasing Division is committed to the values and guiding principles of the public procurement process: Accountability * Ethics * Impartiality * Professionalism * Service * Transparency. Example: Profile an MPI application using nvprof, and bind each rank to a separate physical CPU: mpirun --bind-to none -n 2 omp_run. Note that Visual Profiler and nvprof will be deprecated in a future CUDA release. Code: Select all 0 errors found PGI: "acc_shutdown" not detected, performance results might be incomplete. A installation wizard that allows users to install the SDK packages and their dependencies on Power Systems using the x86_64 SDK. And just like NVIDIA driver, you need to know what Linux distribution you are using to choose the proper installation file. Delivered every other week to your inbox, "Latest Developer News from NVIDIA" is a curated email that compiles the latest GPU-accelerated news, product announcements, and resources published on the NVIDIA Developer News Center and Developer Blog. GPUProgramming with CUDA @ JSC, 24. Optimizing code is challenging; it requires time, thought, and investigation from developers. Follow these steps to verify the installation − Step 1 − Check the CUDA toolkit version by typing nvcc -V in the command prompt. Fixed an issue in 390. 04 GCC 6, CUDA. midi2audio. Improved Analysis Visualization. The core NVTX API is defined in file nvToolsExt. 0 production-ready tools availability for NVIDIA devices: Intel's compilers are Xeon Phi only, PGI and Cray offer only OpenACC, GCC support is only in plans. 176 RN-06722-001 _v9. Fixed an issue in 390. Mixed precision training using float16¶ In this tutorial you will walk through how one can train deep learning neural networks with mixed precision on supported hardware. However, we can get the elapsed transfer time without instrumenting the source code with CUDA events by using nvprof, a command-line CUDA profiler included with the CUDA Toolkit (starting with CUDA 5). For example, to install only the compiler and the occupancy calculator, use the following command −. The NVIDIA Visual Profiler isn't usually linked within your system (so typing nvvp in the terminal, or. $ nvprof-o my_profile. 0 compiles marker support by default, and you can enable it by setting the HIP_PROFILE_API environment variable and then running the rocm-profiler:. SQL Server memory performance metrics – Part 4 – Buffer Cache Hit Ratio and Page Life Expectancy March 5, 2014 by Milena Petrovic In SQL Server performance metrics – part 3 , we presented some of the SQL Server Buffer Manager metrics. If it is not correct, enter the correct path to CUDA Enter CUDA install path (default /usr/local/cuda): CUDAがインストールされているパスを聞いてくる。 デフォルトの通りなので単に「ENTER」を押す。. SUID ( S et owner U ser ID up on execution) is a special type of file permissions given to a file. CUDA − Compute Unified Device Architecture. At the installation of the Alea NuGet package, an MSBuild task is added into your project. , the development tools, including shared libraries, the compiler, the nvidia visual profiler, the handy tools/CUDA_Occupancy_Calculator. (Closes: #761363) * nvidia-cuda-doc: Remove more tracking scripts. For gmatrix, matrix (A, B, C for C = A*B) are copied to GPU and C matrix stored in the GPU side after the calculation, involving three times host-to-device data transfer and without device-to-host transfer. I’ll use a simple example to uninstall the pandas package. CUDA TOOLKIT MAJOR COMPONENTS This section provides an overview of the major components of the CUDA Toolkit and points to their locations after installation. In this video from the GPU Technology Conference, Guido Juckeland from ZIH presents: Showing the Missing Middle: Enabling OpenACC Performance Analysis. RogueWave, Total View. CuArrays or CUDAnative. txt; gpustarttimestamp gridsize3d threadblocksize active_warps active_cycles Compile your code and run; make clean; make debug=1. Even after fixing the training or deployment environment and parallelization scheme, a number of configuration settings and data-handling choices can impact the MXNet performance. 写并行程序时一些简单的评价指标命令:1、查看活跃线程束的情况活跃线程束比例定义为每个周期活跃的线程束的平均值与一个sm支持的线程束最大值的比。nvprof --metrics achieved_occupancy. 1 Verifying the Installation. Install the client on your local machine and then you can access the GUI on Bridges to debug your code. The NVIDIA Visual Profiler isn't usually linked within your system (so typing nvvp in the terminal, or looking in your menus isn't going to help) meaning you will need to look for the install location here in the Quick Start Guide, or you can use the cudatoolkit module on Piz Daint. backward (tensors, grad_tensors=None. xls, and so on) on a Mac without an NVIDIA GPU. nvprof) would result in a failure when enumerating the topology of the system; Fixed an issue where the Tesla driver would result in installation errors on some Windows Server 2012 systems; Fixed a performance issue related to slower H. 04 GCC 6 carml/base:amd64-cpu-latest amd64 ubuntu:18. CUDA missing library libcuda. ‣ Visual Profiler and nvprof now support a new application replay mode for. It corresponds to a single hardware counter value which is collected during kernel execution. © 2019, The University of Sheffield Hosted on Read the Docs. To measure the time spent in each data transfer, we could record a CUDA event before and after each transfer and use cudaEventElapsedTime(), as we described in aprevious post. Most voted files : total uninstall pro 5. nvvp vectorAdd. so, libnppi. nvprof runs the program and gives a summary of results that is similar to the default output in Visual Profiler. @profile to delimit interesting code and start nvprof with the option --profile-from-start off: $ nvprof --profile-from-start off julia julia> using CuArrays, CUDAdrv julia> a = CuArrays. Please add the call "acc_shutdown(acc_device_nvidia)" to the end of your application to ensure that the performance results are complete. Here is the link to the instructions: CUDA Installation Guide. LBANN uses CMake for its build system and a version newer than or equal to 3. 12 where CUDA profiling tools (e. NVIDIA CONFIDENTIAL. MXNet's Profiler is definitely the recommended starting point for profiling MXNet code, but NVIDIA also provides a couple of tools for low level profiling of CUDA code: Visual Profiler and Nsight Compute. NVIDIA CUDA Toolkit v6. CUDA Environment Setup Machine Learning pipeline is composed of many stages: Data ingestion, exploration, feature generation, data cleansing, model training, validation, and. Data bigger than grid Maximum grid sizes!! Compute capability 1. nvprof into NVVP, making sure to select the "multiple processes" option along the way. nvprof is a command-line profiler available for Linux, Windows, and OS X. nvprof) would result in a failure when enumerating the topology of the system. 1 occupancy_calculator_9. 5 version but both did not seem to work (it also may be that I just do not know how to install it properly for Video rendering). Also we will extensively discuss profiling techniques and some of the tools including nvprof, nvvp, CUDA Memcheck, CUDA-GDB tools in the CUDA toolkit. Fixed an issue in 390. Usually the nvcc application is found in the C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10. Return a printable string of aggregate profile stats. From here we'll be installing TensorFlow and Keras in a virtual environment. nvprof is quite flexible, so make sure you check out the documentation. 1 Profiling with NVIDIA Tools The CUDA Toolkit comes with two solutions for profiling an application: nvprof, which is a command line program, and the GUI application NVIDIA Visual Profiler (NVVP). To use nvprof issue: mpirun nvprof. Während der Installation wird man gefragt, ob der enthaltenen Grafiktreiber installiert werden soll. For fresh installation, we can religiously follow the installation instruction displayed on the download page: - Install CUDA. 2, including: ‣ Updated Table 13 to mention support of 64-bit floating point atomicAdd on devices of compute capabilities 6. In this video from the GPU Technology Conference, Guido Juckeland from ZIH presents: Showing the Missing Middle: Enabling OpenACC Performance Analysis. このところ、コンピューティング・コアのスピードは上がっていません。上がっているのは、プロセッサの並列度です。この傾向はここ10年ほど続いていますし、今後もまだしばらくは続くものと思われます。 研究者であれば、OpenACCで並列処理を活用し、科学計算用アプリケーションの実行. Add the path for vsinstr. The files can be big and thus slow to scp and work with in NVVP. For gmatrix, matrix (A, B, C for C = A*B) are copied to GPU and C matrix stored in the GPU side after the calculation, involving three times host-to-device data transfer and without device-to-host transfer. Instructor Note: At this point, walk through the writeSegment. 130-1 @cuda. exe -s nvcc_9. Download the Performance Tools for Visual Studio. SUID is defined as giving temporary permissions to a user to run a program/file with the permissions of the file owner rather that the user who. ty_ger07 The constant power perfcap reason is hopefully just a problem with a corrupt driver install. Building LBANN with CMake ¶. msi /qn ‣ To uninstall, use /x instead of /i. jl and manually start the profiler with CUDAdrv. It enables data scientists to build environments once - and ship their training/deployment quickly. nvvp python my_profiler_script. The files can be big and thus slow to scp and work with in NVVP. However, there may be compatibility issues when executing the generated code from MATLAB as the C/C++ run-time libraries that are included with the MATLAB installation are compiled for GCC 6. nvprof, etc. 选择install 安装完juno后,他会自己给你安装一些他需要的扩展: 右上角安装完成后重启Atom就有Juno可以用了,就这样: 然后我们就成功了, 当网速不那么流畅的时候稍微等一会儿,juno安装完成会提示你。不要着急。. 5 를 컴파일 할 수 있는 방법이 있습니까?. Using Environment Variables With the mpirun Command. 0 | ii CHANGES FROM VERSION 7. 265 video encode/decode performance on AWS p3 instances. GPU: Within the field of parallel computing we refer to our GPUs as devices. 1/bin only include the nvprof: #ls /usr/local/cuda-9. nvprof) would result in a failure when enumerating the topology of the system; Fixed an issue where the Tesla driver would result in installation errors on some Windows Server 2012 systems; Fixed a performance issue related to slower H. 0 is required. cu example on pgs 170-171 in the book to demonstrate the actual performance implications of misaligned writes using nvprof. Currently CUDA 10. Summary In this post, I will introduce how to install the newest CUDA and corresponding Nvidia driver in Ubuntu 16. So through out this course you will learn multiple optimization techniques and how to use those to implement algorithms. Viewing profiles: The above command will created a bunch of files named 3214. OK, I'll try reinstalling the driver. Created using Sphinx 1. 04 activity add multiple jars amarok apache attack axis2 bam bar chart batch blog bpel bpel4people bps build build lifecycle buildr business buzz c c++ casestudy char character choreography cir classpath clock cluster crash cricket data node datepicker dependency deploy DSA Efficient High Performance Framework. 5: •OS: 1 No LSB modules are available. How to install CUDA for gtx 970 on Windows Hello, I would like to know if I can download the latest cuda driver or if I have to download an older version. The following executables are incorporated in NVRTC Runtime. Goal: install OpenCL on your Ubuntu VirtualBox installation, test with an OpenCL implementation of reduce. 1, so the cuda-9. INSTALLATION NOTES 6. The profiling workflow of this example depends on the nvprof tool from NVIDIA. Provided by: nvidia-cuda-dev_7. * Fix typos, hyphenation, and sections in the manpages. 选择install 安装完juno后,他会自己给你安装一些他需要的扩展: 右上角安装完成后重启Atom就有Juno可以用了,就这样: 然后我们就成功了, 当网速不那么流畅的时候稍微等一会儿,juno安装完成会提示你。不要着急。. I am not sure what's next, can someone help me out?. An example profile for a linear scaling benchmark (TiO2) is shown here To run on CRAY architectures in parallel the following additional tricks are needed. The code and instructions on this site may cause hardware damage and/or instability in your system. nvprof, etc. In contrast to the Nsight IDE, we can freely use any Python code that we have written—we won't be compelled here to write full-on, pure CUDA-C test function code. 04 GCC 6, CUDA. CUDAのプロファイラとして長らくnvprofとnvvpをが使われてきたと思いますが、最近これらのツールのドキュメントの最初のほうに以下のように書かれています。. AMD μProf AMD uProf is a performance analysis tool for applications running on Windows and Linux operating systems. Search this site. so - The NVIDIA cuRAND Library libnppc. Change the Current Drive Enter the drive letter followed by a colon C:> E: E:> To change drive and directory at the same time, use CD with the /D switch C:> cd /D E:\utils E:\utils\> CHDIR is a synonym for CD. launch CUDA kernel file=C:\Users\ptheywood\SATGPU\vecaddmod\f1. 1 SETUP SYSTEM We use the following system to install and run OpenMP 4. Hours (in the TimeBank) 1000000:00:0:00:00 in time…. Average act scores by year 2. To deploy an application, the extracted resources for all the platforms on which the application is intended to run need to be deployed as well. OpenCL (Open Computing Language) is a multi-vendor open standard for general-purpose parallel programming of heterogeneous systems that include CPUs, GPUs and other processors. There is however still very limited OpenMP 4. installation and libraries (MKL-DNN, CuDNNetc. Dump profile and stop profiler. The files can be big and thus slow to scp and work with in NVVP. Nvprof is nvida's built-in profiler. GPU profiling for computer vision applications 1. 1, NVIDIA restricts access to performance counters to only admin users. c)nvprof (nvidia profiler) d)nvcc(nvidia cuda compiler):- It is a programming language for Cuda architecture licensed to Nvidia. 1573ms cuDeviceTotalMem 1. Without the proper tools, programmers have to fall back on slower, less efficient ways of trying to optimize their applications. We need the following prerequisites. nvprof profiles only one task at a time; if one profiles a GPU code which has multiple tasks (e. Last released on Nov 20, 2016 Easy to use MIDI to audio or playback via FluidSynth. Install CUDA 10. NVProf with Spectrum MPI. 1/bin/ nvprof This comment has been minimized. RogueWave, Total View. It seems it cannot find my CUDA installation I added the cuda installation with --cuda-path and left with. The NVIDIA Visual Profiler is a cross-platform performance profiling tool that delivers developers vital feedback for optimizing CUDA C/C++ applications. Usually, this issue can be solved by simply restarting the router. nvcc is a Windows program. Hello everyone, I have a paired end fastq file and I know that BLAST+ in command line, accepts fasta format. You might be in a poor network coverage area: Shift your device to the area where the network signal is good. It enables data scientists to build environments once - and ship their training/deployment quickly. Basically, I'd like to know if there's any way to stop a running TensorRT server exit normally without using ctrl-C, or if there is a workaround with this issue using nvprof and TensorRT together. 1 SETUP SYSTEM We use the following system to install and run OpenMP 4. If you are interested in profiling CP2K with nvprof have a look at these remarks. Installing pyopencl Make sure you have python installed Install the numpy library sudo apt-get install python-numpy Download the latest version from the pyopencl website Extract the package with tar -zxf Run to install as a local package python setup. 12 where CUDA profiling tools (e. Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. I have a kernel which shows poor performance, nvprof says that it has low warp execution efficiency (page 3 in the attached PDF) and suggests to reduce an "intra-warp divergence and predication". There are also problems with the installation of the Cuda toolkit on Catalina. h, but move all ViennaCL-related includes to blas3. out # you can also add --log-file prof The default output includes 2 sections: one related to kernel and API calls; another related to memory; Execution configuration. 動作が重くなりがちなので、 nvprof でprofilingだけリモートマシンで行なって、 scp でローカルマシンに結果を飛ばして、. # nvidia-profilerは別パッケージらしい sudo apt install nvidia-profiler # 測定したいコマンドの前にnvprof -o <出力>. We will use tools like nvprof to. GUIのツールだけれど、nvprofというコマンドラインがあり、基本はこれでプロファイルデータだけ作成してローカルに転送、NVIDIA Visual Profilerで閲覧したりして使える。プロファイルデータの拡張子は基本的に. 130-1 [5, 590 kB. INSTALLATION NOTES 6. CUDA programming is all about performance. == 1520 = = NVPROF is profiling process 1520, command:. Average act scores by year 2. Most voted files : total uninstall pro 5. NVIDIA CUDA Toolkit 9. The resulting externals may have other externals. This tool is aimed in extracting the small bits of important information and make profiling in NVVP faster. o If the code ran on the GPU you will get a result like this:. Note that Visual Profiler and nvprof will be deprecated in a future CUDA release. CUDAのプロファイラとして長らくnvprofとnvvpをが使われてきたと思いますが、最近これらのツールのドキュメントの最初のほうに以下のように書かれています。. The NVIDIA Visual Profiler is a cross-platform performance profiling tool that delivers developers vital feedback for optimizing CUDA C/C++ applications. Could you try nvprof --profile-child-processes python ass2. There is however still very limited OpenMP 4. Here is the link to the instructions: CUDA Installation Guide. 1/bin only include the nvprof: #ls /usr/local/cuda-9. jl and manually start the profiler with CUDAdrv. Any help or push in the right direction would be greatly appreciated. HIP provides lightweight header files that map the HIP runtime calls back to CUDA, where they use the standard CUDA runtime. This tool is aimed in extracting the small bits of important information and make profiling in NVVP faster. Page Life Expectancy "Duration, in seconds, that a page resides in the buffer pool" [2] SQL Server has more chances to find the pages in the buffer. I double checked the CUDA libraries and that specific library is in fact included in the LD_LIBRARY_PATH. (Closes: #763177) [ Andreas Beckmann ] * Add wrapper script for nvprof due to its insane library search behavior. Note that you can just as well keep your data on the card between kernel invocations–no need to copy data all the time. Some more common causes that creates the issue failing to obtain IP address are. However for the GPU version of the code we need different software to profile the MegaKernel ™ and improve its performance. Programs written using CUDA harness the power of GPU. But I guess that's another option I could try even though one would think this should not make a difference. How to install CUDA for gtx 970 on Windows Hello, I would like to know if I can download the latest cuda driver or if I have to download an older version. 12 where CUDA profiling tools (e. so - The NVIDIA cuSOLVER Library libcufft. Install nvprof and nvvp from the CUDA toolkit ; Return to Installation Instructions. to fully remove those you can use sudo apt purge python-pip python-dev <-- this will delete all the files/directories/binaries created by that package. 1, NVIDIA restricts access to performance counters to only admin users. Step 2 – Install Nvidia-Docker. After checking correct boot of both windows and linux, I proceeded in setting up the nvidia drivers on the linux system. Bases: object Profiling Frame class. USE_NVPROF: activates nvprof API calls to track GPU-related timings (default: 0) USE_OPENSSL_EVP: determines whether to use EVP API for OpenSSL that enables AES-NI support (default: 1) NBA_NO_HUGE: determines whether to use huge-pages (default: 1) NBA_PMD: determines what poll-mode driver to use (default: ixgbe). Nvprof is nvida's built-in profiler. nvprof profiles only one task at a time; if one profiles a GPU code which has multiple tasks (e. 5 RN-06722-001 _v6. nvprof) would result in a failure when enumerating the topology of the system. "Learn how OpenACC runtimes now also exposes. net base | 3. As such, the build is tested regularly on Linux-based machines, occasionally on OSX, and never on Windows machines. Fixed a performance issue related to slower H. The profiling tools contain below changes as part of the CUDA Toolkit 10. It is not necessary for the host system to have an NVIDIA GPU. 1 Windows For silent installation: ‣ To install, use msiexec. You can use these tools to profile all kinds of executables, so they can be used for profiling. using Nsight IDE and nvprof. centos与主机复制粘贴 linux 查看安装驱动 linux 查找文件路劲 linux 看文件时间戳 linux 设置屏幕大小 linux挂载有数据磁盘 linux io五种模型 linux l开头的命令 linux nvprof linux 部署定时任务 linux 查看pcie linux 高精度算法库 linux 共享资源保护 linux 进程调度试题 linux 进程下. How can I use a profiler that work ?. ‣ Added compute capabilities 6. Stop timing scope for this object. nvprof is a command-line profiler available for Linux, Windows, and OS X. It corresponds to a single hardware counter value which is collected during kernel execution. I am not able to find the newer version, so I can't run the uninstaller. 0 production-ready tools availability for NVIDIA devices: Intel's compilers are Xeon Phi only, PGI and Cray offer only OpenACC, GCC support is only in plans. An event is a countable activity, action, or occurrence on a device. It requires minimal changes to the existing code - you only need to declare Tensor s for which gradients should be computed with the requires_grad=True keyword. It is not necessary for the host system to have an NVIDIA GPU. Due to inert behavior of Buffer Cache Hit Ratio, the values it shows can be misleading and it's recommended to check values of other SQL Server Buffer Manager counters, such as Page Life Expectancy, Free list stalls/sec, Page reads/sec, etc. 0-1xenial-20191219-102913+0000 1. the output of NVIDIA Cuda and DKMS video driver installation [[email protected] ~]# yum -y install nvidia-driver-latest-dkms cuda Loaded plugins: fastestmirror Determining fastest mirrors epel/x86_64/metalink | 31 kB 00:00:00 * base: mirror. (Closes: #761363) * nvidia-cuda-doc: Remove more tracking scripts. SRUN-managed script call reports the device and node names and then calls the real nvprof tool from the CUDA toolkit module on compute node over SSH. Before we dive into writing our first lightning fast application, we should cover some fundamental terminology. 0 directory, depending on the user's option during install. It automatically detects the operating system of the target system and automates all the necessary steps to install the SDK. 148_silent install_by hamdy abu zeid. Being able to run NVIDA GPU accelerated application in containers was a big part of that motivation. While nvprof would allow you to collect either a list or all metrics, in NVIDIA Nsight Compute CLI you can use regular expressions to select a more fine-granular subset of all available metrics. But before we begin, here is the generic form that you can use to uninstall a package in Python: Now, let's suppose that you already installed the pandas package using the PIP install method. out Using Device 0: GeForce GTX 760 Vector size 16777216 sumArraysOnGPU <<< 16384, 1024 >>> Time elapsed 0. This manual is intended for scientists and engineers using the PGI compilers. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing, an approach known as General Purpose GPU (GPGPU) computing. NVTX functions with such postfix exist in multiple variants, performing the same core functionality with different parameter encodings. nvvp pyrit benchmark_long. Hopefully the last post on "Docker and NVIDIA-Docker on your Workstation" provided clarity on what is motivating my experiments with Docker. When attempting to launch nvprof through SMPI, the environment LD_PRELOAD values gets set incorrectly, which causes the cuda hooks to fail on launch. rocblas build wiki; if you call rocBLAS from your code, or if you need to install rocBLAS for other users. After checking correct boot of both windows and linux, I proceeded in setting up the nvidia drivers on the linux system. 0+ nvprof suffers from a problem that may affect running with Spectrum MPI. The presentation will be delivered remotely, but there will be an in-person viewing of the webinar for participants with current ORNL badges. Summary In this post, I will introduce how to install the newest CUDA and corresponding Nvidia driver in Ubuntu 16. 50K, threads running on the device. nvprof is a command-line profiler available for Linux, Windows, and OS X. Note that you can just as well keep your data on the card between kernel invocations–no need to copy data all the time. GPUProgramming with CUDA @ JSC, 24. Finally, we need to enable the use of auto mixed precision by: export TF_ENABLE_AUTO_MIXED_PRECISION=1. 04 activity add multiple jars amarok apache attack axis2 bam bar chart batch blog bpel bpel4people bps build build lifecycle buildr business buzz c c++ casestudy char character choreography cir classpath clock cluster crash cricket data node datepicker dependency deploy DSA Efficient High Performance Framework. nvcc A way to uninstall nvcc from your computer nvcc is a Windows program. Guided Performance Analysis NEW in 5. /standalone. There're builtin InfiniBand kernel modules in vmlinux image, so DO NOT need to install Mellanox OFED. We'll be using nvprof for this tutorial (documentation). NVML C library - a C-based API to directly access GPU monitoring and management functions. I’ll use a simple example to uninstall the pandas package. From here we’ll be installing TensorFlow and Keras in a virtual environment. Installation PyTorch is a popular deep learning library for training artificial neural networks. Install the client on your local machine and then you can access the GUI on Bridges to debug your code. 0, but it is telling me a newer version is already installed. Manager can also set up your Linux host computer. py?The profile-child-processes option is needed because your target application - python - probably executes GPU stuff in a new spawned process. To check if your GPU is using tensor cores you can use nvprof in front of your command, something like: nvprof python DeepSpeech. Fixed an issue in 390. The following description refers to the JCudaVectorAdd example. In this short tutorial, I’ll show you how to use PIP to uninstall a package in Python. Am I missing something in the below command $ docker pull dpage/pgadmin4 $ docker run -p 80:80 -e '[email protected]' -e 'PGADMIN_DEFAULT_PASSWORD=admin' -d dpage/pgadmin4 Source: StackOverflow. nvprof with the nvvp tool, which might take several minutes to open the data. so - The NVIDIA CUDA Runtime Library libcublas. Torch is using luajit, so every lua profiler based on the lua-debug api would work. cu Main steps (based off of this askUbuntu answer ): - Download the OpenCL SDK for Intel CPUs (if you have another type of CPU, you may need a different SDK). "Invalid Cross-Reference Format" lists the options in alphabetical order, with a brief description of each. Any application that replies on LD_PRELOAD could potentially see. nvprof is new in CUDA 5. Search this site. Even after fixing the training or deployment environment and parallelization scheme, a number of configuration settings and data-handling choices can impact the MXNet performance. Profiling with NVPROF + NVVP + NVTX NVPROF: Powerful profiler provided in every CUDA toolkit installation Can be used to gather detailed kernel properties and timing information NVIDIA Visual Profiler (NVVP): Graphical interface to visualize and analyze NVPROF generated profiles Does not show CPU activity out of the box. Improved Analysis Visualization. h, whereas domain-specific extensions to the NVTX interface are exposed in separate header files. The files can be big and thus slow to scp and work with in NVVP. py install --user C/C++ linking (gcc/g++) In order to compile your OpenCL program you must tell. I'm using CUDA 10. 5 ‣ Updates to add compute capabilities 6. If it's not on your path already, you can find nvprof inside your CUDA directory. You can initiate the profiling directly from inside Visual Profiler or from the command line with nvprof which wraps the execution of your Python script. Multiple presentations about OpenMP 4. However, I noticed that there is a limit of trace to print out to the stdout, around 4096 records, thought you may have N, e. Thus, increasing the computing performance. But after I compile the executable files and run, it tells me driver not compatible with this version of CUDA. Otherwise, there's no IB in VM. nvprof) would result in a failure when enumerating the topology of the system; Fixed an issue where the Tesla driver would result in installation errors on some Windows Server 2012 systems; Fixed a performance issue related to slower H. 2 Linux ‣ In order to run CUDA applications, the CUDA module must be loaded and the entries in /dev created. The client contains the test code and examples. Whilst this is well trodden ground, the same example can be seen here and in the Udacity course itself, the book presents a good narrative, introducing the CUDA profiler nvprof early to justify and examine the changes proposed, making it altogether a gentle introduction to something quite alien, if you are used to serial programming on a CPU. /exe %> nvprof --analysis-metrics -o profile. nvidia-smi CLI - a utility to monitor overall GPU compute and memory utilization. msi /qn ‣ To uninstall, use /x instead of /i. /vector_add ==6326== Profiling result: Time(%) Time Calls Avg Min Max Name 97. Note that Visual Profiler and nvprof will be deprecated in a future CUDA release. py I prefer to use --print-gpu-trace. Supports all Jetson products. Caution: nvprof metric option may negatively affect performance characteristics of function running on GPU as it may cause all kernel executions to be serialized on GPU. Most voted files : total uninstall pro 5. All I need is a ballpark estimate of the FLOPS, wit. ) 7Argonne Leadership Computing Facility Challenges nvprof--metrics all --log-file all-metrics. GPU Accelerated Computing with C and C++, which also has some videos. The Assess, Parallelize, Optimize, Deploy ("APOD") methodology is the same. Work with project team to install, configure, utilize, and test various aspects of the Bro Network Security Monitor and related libraries, such as the “Broker” Client Communications library, as well as database back ends and messaging libraries, to capture, merge, analyze, and store network traffic and other types of data. C:> CD pro* will move to C:\Program Files. Fixed a performance issue related to slower H. Output of nvprof. /vector_add Following is an example profiling result on Tesla M2050 ==6326== Profiling application:. Browse the Gentoo Git repositories. nvidia-smi CLI - a utility to monitor overall GPU compute and memory utilization. to swap space on hard disk Transfers to and from the GPU memory need to go over PCI-E PCI-E transfers are handled by DMA engines on the GPU and. 001708 sec Arrays match == 1520 = = Profiling application:. The Nsight suite of profiling tools now supersedes the NVIDIA Visual Profiler (NVVP) and nvprof. nvvp とするみたい。 $ nvprof -o profile. Additionally, you can find the CUDA installation guide and prerequisites here. nvprof) would result in a failure when enumerating the topology of the system. 1 SETUP SYSTEM We use the following system to install and run OpenMP 4. 176 RN-06722-001 _v9. launch CUDA kernel file=C:\Users\ptheywood\SATGPU\vecaddmod\f1. @profile (thus excluding the time to compile our kernel):. GPU performance inhibitors • Copying data to/from device • Device under-utilisation/ GPU memory latency • GPU memory bandwidth • Code branching This lecture will address each of these. Posts about nvidia visual profiler written by Ashwin. Preface: We want to emphasis that this document is a note on our OpenMP 4. so - The NVIDIA cuSOLVER Library libcufft. The output can be visualized with kcachegrind or the Eclipse Linux Tools. For example, see nvToolsExtCuda. The Analyze Execution Profiles of the Generated Code workflow depends on the nvprof tool from NVIDIA. In our website you will find complete information for files if they are malicious or safe Files. , the development tools, including shared libraries, the compiler, the nvidia visual profiler, the handy tools/CUDA_Occupancy_Calculator. The NVIDIA Visual Profiler is a cross-platform performance profiling tool that delivers developers vital feedback for optimizing CUDA C/C++ applications. 0をインストールする手順のメモ. - The NVIDIA Visual Profiler and the command-line profiler, nvprof, now support power, thermal, and clock profiling. wait for few seconds, then after the process list appears scroll down to find nvprof. to fully remove those you can use sudo apt purge python-pip python-dev <-- this will delete all the files/directories/binaries created by that package. 下载test_profile到可视化路径。. For this tutorial I am using NVIDIA DGX1 which has Ubuntu 18. In this video from the GPU Technology Conference, Guido Juckeland from ZIH presents: Showing the Missing Middle: Enabling OpenACC Performance Analysis. NVIDIA CUDA TOOLKIT V6. 4つのprofiling mode summary mode (default) trace mode event/metric summary mode event/metric trace mode 7 $ nvprof --print-gpu-trace --print-api-trace $ nvprof --events --metrics $ nvprof --aggregate-mode off [event|metric] $ nvprof GPUで発生する全てのアクティビティ CUDA Runtime API. Hours (in the TimeBank) 1000000:00:0:00:00 in time…. The files can be big and thus slow to scp and work with in NVVP. The Jetson Nano will then walk you through the install process, including setting your username/password, timezone, keyboard layout, etc. 2 pip install nvprof Copy PIP instructions. 1, so the cuda-9. The columns show the percentage of execution time, the actual time, the number of calls, the average-min-max of a single call for every kernel. You might be in a poor network coverage area: Shift your device to the area where the network signal is good. If this fails, we will have to ask the professors to allow us to borrow a 1080. I'll use a simple example to uninstall the pandas package. to fully remove those you can use sudo apt purge python-pip python-dev <-- this will delete all the files/directories/binaries created by that package. I am on CentOS 7. 04 LTS, but each time I create a container it crashes. It can be solved. Visual and command line interfaces to collect counters, statistics, and derived values evlipse specified CUDA kernel launches Customizable reports provide results, source pxrallel disassembly views, memory throughput diagrams, and execution flow charts Unlimited experiments on live kernels Set and compare reports to one or more baseline profiles. nvprof: Generate separate output files for each process. Last released on Dec 21, 2016 Managed machine-learning model training tool based on sacred. so - The NVIDIA CUDA Driver Library libcudart. I have GTX 1060 and…. 4つのprofiling mode summary mode (default) trace mode event/metric summary mode event/metric trace mode 7 $ nvprof --print-gpu-trace --print-api-trace $ nvprof --events --metrics $ nvprof --aggregate-mode off [event|metric] $ nvprof GPUで発生する全てのアクティビティ CUDA Runtime API. CUDA programming is all about performance. C:> CD pro* will move to C:\Program Files. In CUDA toolkit v10. However for the GPU version of the code we need different software to profile the MegaKernel ™ and improve its performance. 经过几天血泪的摸索,和几个好心的大神的帮忙,终于搞定了这些问题,特此记录。 对于一个新装的Ubuntu系统,(1)首先安装同版本的gcc和g++ $ sudo apt-get. Soll der Grafiktreiber jedoch installiert werden muss man sich zuvor ausloggen, ohne X Server (im Terminal) wieder einloggen und die Installation erneut starten (nicht empfohlen). NVPROF Command line profiler nvprof. 1 Serena 4 Release : 18. crt cuda-install-samples-10. We will end with a brief overview of the command-line Nvidia nvprof profiler. Network Balancing Act Documentation, Release 0. It can be solved by adding a link /Developer to any place. CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. Optimizing code is challenging; it requires time, thought, and investigation from developers. Hat man das schon vorher gemacht muss diese Option entfernt werden. inp -o test. Example: Profile an MPI application using nvprof, and bind each rank to a separate physical CPU: mpirun --bind-to none -n 2 omp_run. It is recommended that the script install. Next, we will install docker. Preface: We want to emphasis that this document is a note on our OpenMP 4. using Nsight IDE and nvprof. The toolkit path should point to the home directory, where the nvprof script above is located. (a); julia> CUDAdrv. 小小将 为人民日益增长的美好生活需要而读书. Project Leads. It seems it cannot find my CUDA installation I added the cuda installation with --cuda-path and left with. Bases: object Profiling Frame class. CSDN提供最新最全的jqw11信息,主要包含:jqw11博客、jqw11论坛,jqw11问答、jqw11资源了解最新最全的jqw11就上CSDN个人信息中心. I downloaded the latest and the 6. nvprof enables the collection of a timeline of CUDA-related activities on both CPU and GPU, including kernel execution, memory transfers, memory set and CUDA API calls. For simple profiling, prefix your Julia command-line invocation with the nvprof utility. pub` `sudo apt-get update` `sudo apt-get install cuda` 可能出现 Driver/library version mismatch 的问题,重启,或者按照 此方法 。.


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