腾度淄博网站建设,做传销一般是不是有网站,网站导航条用什么做,网络舆情管控1.MLPerf
https://github.com/mlcommons/inference?tabreadme-ov-file https://docs.mlcommons.org/inference/benchmarks/text_to_image/sdxl/
MLPerf 是一个业界标准的机器学习基准测试套件#xff0c;旨在评估各种硬件、框架和模型的性能。它包含训练和推理两个部分readme-ov-file https://docs.mlcommons.org/inference/benchmarks/text_to_image/sdxl/
MLPerf 是一个业界标准的机器学习基准测试套件旨在评估各种硬件、框架和模型的性能。它包含训练和推理两个部分涵盖多种机器学习任务例如图像分类、对象检测、自然语言处理、推荐系统等。MLPerf 主要分为几个子项目包括 Training训练、Inference推理和 Tiny针对低功耗设备的测试。它支持多种平台如 CPU、GPU 和专用的 AI 加速器广泛用于学术界、企业和开源社区评估 AI 系统的性能 下文以https://docs.mlcommons.org/inference/benchmarks/text_to_image/sdxl/#__tabbed_1_1 为例
1. 安装cm
python 3.8(本例使用的是3.9) git 版本不能太低否则有些命令执行不了
pip install cmind
pip install --no-use-pep517 cm4mlops#需要绕过pep517 否则报一下错误 note: This error originates from a subprocess, and is likely not a problem with pip.ERROR: Failed building wheel for cm4mlopsRunning setup.py clean for cm4mlops
Failed to build cm4mlops
ERROR: ERROR: Failed to build installable wheels for some pyproject.toml based projects (cm4mlops)2. inference
MLCommons-Python - pytoch - cuda - native - Performance Estimation for Offline Scenario
cm run script --tagsinstall,python-venv --namemlperf
export CM_SCRIPT_EXTRA_CMD--adr.python.namemlperfcm run script --tagsrun-mlperf,inference,_find-performance,_full,_r4.1-dev \--modelsdxl \--implementationreference \--frameworkpytorch \--categoryedge \--scenarioOffline \--execution_modetest \--devicecuda \--quiet \--test_query_count50如果git clone https://github.com/mlcommons/inference.git不了手动下载之后在上述命令假如下述路径 --inference_src${USER_PATH}$/cuda/inference
部分数据下载较久
Collecting torchDownloading torch-2.4.1-cp39-cp39-manylinux1_x86_64.whl (797.1 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 797.1/797.1 MB 4.1 MB/s eta 0:00:00
Collecting nvidia-nccl-cu122.20.5Downloading nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 176.2/176.2 MB 3.8 MB/s eta 0:00:00
Collecting nvidia-cudnn-cu129.1.0.70Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 664.8/664.8 MB 2.0 MB/s eta 0:00:00
Collecting nvidia-cublas-cu1212.1.3.1Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 410.6/410.6 MB 3.6 MB/s eta 0:00:00
Requirement already satisfied: sympy in /home/u200810220/CM/repos/local/cache/13d32961b74a4500/mlperf/lib/python3.9/site-packages (from torch) (1.13.3)
Collecting filelockDownloading filelock-3.16.1-py3-none-any.whl (16 kB)
Collecting nvidia-cufft-cu1211.0.2.54Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 121.6/121.6 MB 7.2 MB/s eta 0:00:00
Collecting nvidia-cusparse-cu1212.1.0.106Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 196.0/196.0 MB 3.6 MB/s eta 0:00:00
Collecting nvidia-cuda-nvrtc-cu1212.1.105Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 23.7/23.7 MB 4.1 MB/s eta 0:00:00
Collecting nvidia-cusolver-cu1211.4.5.107Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 124.2/124.2 MB 8.9 MB/s eta 0:00:00
Collecting nvidia-nvtx-cu1212.1.105Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 99.1/99.1 kB 9.4 MB/s eta 0:00:00
Collecting triton3.0.0Downloading triton-3.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (209.4 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 209.4/209.4 MB 4.8 MB/s eta 0:00:00
Collecting fsspecDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 179.3/179.3 kB 6.7 MB/s eta 0:00:00
Collecting nvidia-curand-cu1210.3.2.106Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.5/56.5 MB 4.8 MB/s eta 0:00:00
Collecting nvidia-cuda-cupti-cu1212.1.105Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.1/14.1 MB 5.5 MB/s eta 0:00:00
Collecting networkxDownloading networkx-3.2.1-py3-none-any.whl (1.6 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 5.4 MB/s eta 0:00:00
Collecting jinja2Downloading jinja2-3.1.4-py3-none-any.whl (133 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 133.3/133.3 kB 4.9 MB/s eta 0:00:00
Collecting nvidia-cuda-runtime-cu1212.1.105Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 823.6/823.6 kB 6.3 MB/s eta 0:00:00
Requirement already satisfied: typing-extensions4.8.0 in /home/u200810220/CM/repos/local/cache/13d32961b74a4500/mlperf/lib/python3.9/site-packages (from torch) (4.12.2)
Collecting nvidia-nvjitlink-cu12Downloading nvidia_nvjitlink_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl (19.7 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 19.7/19.7 MB 5.2 MB/s eta 0:00:00
Downloading: rclone sync mlc-inference:mlcommons-inference-wg-public/stable_diffusion_fp32 /home/u200810220/CM/repos/local/cache/e00a5f70d26c4213/stable_diffusion_fp32 -P --error-on-no-transfer
Transferred: 12.824 GiB / 12.926 GiB, 99%, 28.401 KiB/s, ETA 1h2m34s
Transferred: 18 / 19, 95%
Elapsed time: 23m42.3s
Transferring:* checkpoint_pipe/unet/d…orch_model.safetensors: 98% /9.565Gi, 28.421Ki/s, 1h2m31s^Z
[5] Stopped cm run script --tagsrun-mlperf,inference,_find-performance,_full,_r4.1-dev --modelsdxl --implementationreference --frameworkpytorch --categoryedge --scenarioOffli
Transferred: 12.841 GiB / 12.926 GiB, 99%, 27.910 KiB/s, ETA 53m11s
Transferred: 18 / 19, 95%
Elapsed time: 34m2.3s
Transferring:* checkpoint_pipe/unet/d…orch_model.safetensors: 99% /9.565Gi, 28.406Ki/s, 52m16s^Z
[5] Stopped cm run script --tagsrun-mlperf,inference,_find-performance,_full,_r4.1-dev --modelsdxl --implementationreference --frameworkpytorch --categoryedge --scenarioOffli
Transferred: 12.858 GiB / 12.926 GiB, 99%, 27.617 KiB/s, ETA 42m33s
Transferred: 18 / 19, 95%
Elapsed time: 44m44.3s
Transferring:* checkpoint_pipe/unet/d…orch_model.safetensors: 99% /9.565Gi, 27.648Ki/s, 42m30s
2.CUDA_benchmark
https://github.com/hibagus/CUDA_Bench CUDA Benchmark 是一种用于评估在 NVIDIA GPU 上运行的程序性能的工具。它提供了一系列基准测试用来测量不同算法、库或工作负载在 CUDA 平台上的性能表现。常见的测试类型包括矩阵乘法、向量加法、卷积操作等通过这些基准可以有效评估硬件资源的利用率、带宽、延迟等关键指标帮助开发者优化 CUDA 程序的性能。CUDA Benchmark 通常用于比较不同 GPU 或优化代码执行效率。
CMake更新办法
需要cmake3.20.1以上版本
sudo apt remove cmake
wget https://github.com/Kitware/CMake/releases/download/v3.20.1/cmake-3.20.1-linux-x86_64.sh
sudo bash cmake-3.20.1-linux-x86_64.sh --skip-license --prefix/usr/local
export PATH/usr/local/bin:$PATH
make 不通过 手动下载https://github.com/rapidsai/rapids-cmake 适配不成功
(base) n1:~/cuda/CUDA_Bench/build$ make
[ 11%] Built target cutlass
[ 13%] Performing configure step for nvbench
make[3]: Entering directory /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild
make[4]: Entering directory /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild
make[5]: Entering directory /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild
make[5]: Leaving directory /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild
make[5]: Entering directory /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild
[ 11%] Performing update step for rapids-cmake-populate
fatal: unable to access https://github.com/rapidsai/rapids-cmake.git/: Failed to connect to github.com port 443: Connection timed out
CMake Error at /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild/rapids-cmake-populate-prefix/tmp/rapids-cmake-populate-gitupdate.cmake:97 (execute_process):execute_process failed command indexes:1: Child return code: 128CMakeFiles/rapids-cmake-populate.dir/build.make:135: recipe for target rapids-cmake-populate-prefix/src/rapids-cmake-populate-stamp/rapids-cmake-populate-update failed
make[5]: *** [rapids-cmake-populate-prefix/src/rapids-cmake-populate-stamp/rapids-cmake-populate-update] Error 1
make[5]: Leaving directory /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild
CMakeFiles/Makefile2:82: recipe for target CMakeFiles/rapids-cmake-populate.dir/all failed
make[4]: *** [CMakeFiles/rapids-cmake-populate.dir/all] Error 2
make[4]: Leaving directory /home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuild
Makefile:90: recipe for target all failed
make[3]: *** [all] Error 2
make[3]: Leaving directory /cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/_deps/rapids-cmake-subbuildCMake Error at /usr/local/share/cmake-3.20/Modules/FetchContent.cmake:1012 (message):Build step for rapids-cmake failed: 2
Call Stack (most recent call first):/usr/local/share/cmake-3.20/Modules/FetchContent.cmake:1141:EVAL:2 (__FetchContent_directPopulate)/usr/local/share/cmake-3.20/Modules/FetchContent.cmake:1141 (cmake_language)/usr/local/share/cmake-3.20/Modules/FetchContent.cmake:1184 (FetchContent_Populate)/home/u200810220/cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/NVBENCH_RAPIDS.cmake:35 (FetchContent_MakeAvailable)cmake/NVBenchRapidsCMake.cmake:9 (include)CMakeLists.txt:16 (nvbench_load_rapids_cmake)-- Configuring incomplete, errors occurred!
See also /cuda/CUDA_Bench/build/nvbench/build/src/nvbench-build/CMakeFiles/CMakeOutput.log.
CMakeFiles/nvbench.dir/build.make:91: recipe for target nvbench/build/src/nvbench-stamp/nvbench-configure failed
make[2]: *** [nvbench/build/src/nvbench-stamp/nvbench-configure] Error 1
CMakeFiles/Makefile2:252: recipe for target CMakeFiles/nvbench.dir/all failed
make[1]: *** [CMakeFiles/nvbench.dir/all] Error 2
Makefile:90: recipe for target all failed
make: *** [all] Error 2[ 11%] Built target cutlass
[ 13%] Performing configure step for nvbench3.NAS-Bench-Graph
https://github.com/THUMNLab/NAS-Bench-Graph https://github.com/THUMNLab/AutoGL/tree/agnn NAS-Bench-Graph 是一个用于神经架构搜索Neural Architecture Search, NAS的基准测试工具专注于图神经网络Graph Neural Networks, GNNs。它提供了一个预定义的搜索空间涵盖了多种图神经网络架构并包含了这些架构在多个数据集上的训练与评估结果。通过这个基准研究人员可以更轻松地进行神经架构搜索的实验并快速比较不同方法的性能。这一工具大大加速了 GNN 的架构搜索和优化研究。 版本不适配
graph_neural_network
https://github.com/mlcommons/training/tree/master/graph_neural_network
cd training/gnn_node_classification/
docker build -f Dockerfile -t training_gnn:latest .高性能平台docker 命令被拒绝
4.ogb
https://github.com/snap-stanford/ogb OGBOpen Graph Benchmark是一个专门用于图神经网络GNN研究的大规模基准测试集合涵盖各种真实世界的图数据集如社交网络、知识图谱和分子图。OGB 提供标准化的数据集和性能评估指标使研究人员能够更有效地比较不同的图神经网络模型。它支持不同任务类型包括节点分类、边预测和图分类适用于大规模的图结构数据推动图神经网络研究和应用的发展。
pip install ogb依赖版本不适配
5.nasbench301
NAS-Bench-301 是一个神经架构搜索NAS基准专门用于提升 NAS 方法的研究效率。它提供了一个更具挑战性的搜索空间并基于更广泛的架构评估结果避免了对真实硬件的高昂训练成本。NAS-Bench-301 提供了一个高效的代理模型能够快速预测神经网络架构的性能而无需重新训练每个架构。这个基准支持多种搜索策略并帮助研究人员更轻松地进行实验与评估。 https://github.com/automl/nasbench301 https://www.cnblogs.com/pprp/p/15491922.html
6.benchmarking-gnns
https://github.com/graphdeeplearning/benchmarking-gnns https://www.cvmart.net/community/detail/1578 Benchmarking-GNNs 是一个专注于图神经网络Graph Neural Networks, GNNs的基准测试框架用于系统地比较各种 GNN 模型在不同图形任务上的性能。它为 GNN 研究提供了标准化的数据集和实验环境支持的任务包括节点分类、边预测和图分类。该基准测试框架的目标是推动 GNN 研究的进展使研究人员能够更有效地开发和优化 GNN 模型提升其在真实世界应用中的性能。
# Setup CUDA 10.2 on Ubuntu 18.04
sudo apt-get --purge remove *cublas* cuda*
sudo apt --purge remove nvidia*
sudo apt autoremove
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo apt update
sudo apt install -y cuda-10-2
sudo reboot
cat /usr/local/cuda/version.txt # Check CUDA version is 10.2# Clone GitHub repo
conda install git
git clone https://github.com/graphdeeplearning/benchmarking-gnns.git
cd benchmarking-gnns# Install python environment
conda env create -f environment_gpu.yml # Activate environment
conda activate benchmark_gnncuda 版本不适配
7.gnn-benchmark
https://github.com/shchur/gnn-benchmark
(base) :~/cuda$ sudo apt-get install -y mongodb-org3.6.4 mongodb-org-server3.6.4 mongodb-org-shell3.6.4 mongodb-org-mongos3.6.4 mongodb-org-tools3.6.4
Reading package lists... Done
Building dependency tree
Reading state information... Done
E: Unable to locate package mongodb-org
E: Unable to locate package mongodb-org-server
E: Unable to locate package mongodb-org-shell
E: Unable to locate package mongodb-org-mongos
E: Version 3.6.4 for mongodb-org-tools was not found原版本要求的MongoDB在ubuntu18.04中找不到
