Update by 2010.8-24

最新推荐文章于 2024-11-07 10:52:54 发布
原创 最新推荐文章于 2024-11-07 10:52:54 发布 · 199 阅读 · 0 ·
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#JDK

博客主要提及更新HttpUnit，涉及JDK、TCP/IP、Http等信息技术相关内容。
更新HttpUnit.
关键字:JDK-TCP/IP-Http-HttpUnit
Download_Man
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incremental_updates.20220401.tar.gz--更新说明
技术、思维
04-03 9674
12345678901234567890123456789012345678901234567890123456789012345678901234567890 Incremental Updates for GAMIT/GLOBK 10.71 This file lists updates available immediately in the incremental_updates/ directory. These are combined i
canal完整教程（内附canal.deployer-1.0.24.tar.gz下载地址）
珺珺去哪儿
05-27 8466
1、准备： 下载地址：http://download.csdn.net/detail/qbw2010/9854441 2、canal概述： canal是应阿里巴巴存在杭州和美国的双机房部署，存在跨机房同步的业务需求而提出的。 早期，阿里巴巴B2B公司因为存在杭州和美国双机房部署，存在跨机房同步的业务需求。不过早期的数据库同步业务，主要是基于trigger的方式获
Centos8 安装 MySQL8.0.26
vx539413949的博客
04-07 4181
下载 选择 Red Hat Enterprise Linux / Oracle Linux 选择 Red Hat Enterprise Linux 8 / Oracle Linux (x86, 64-bit) 下载 RPM Bundle (mysql-8.0.26-1.el8.x86_64.rpm-bundle.tar) 这是多个rpm打包的文件 也可以直接在服务器上用wget wget https://cdn.mysql.com//Downloads/MySQL-8.0/mysql-8.0.26-1.
ROS之choro功能包
热门推荐
zhangrelay的专栏
07-27 3万+
使用choro可以快速查找管理功能包，类似ubuntu的apt。 非常方便快捷。 如下： C:\ros_ws>choco search ros-foxy-desktop Chocolatey v0.10.15 ros-foxy-desktop 20201211.0.0.2104300001 1 packages found. 简洁版： ros-catkin-tools 0.0.1.1903221831 ros-cmake 3.11.4.1809242322 ros-colcon-too
SpringBoot学习之基础
qq122516902的博客
10-09 6939
SpringBoot 一、基础 1.SpringBoot体验 (1).创建一个maven工程 pom.xml <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven....
canal1.1.7使用canal-adapter进行mysql同步数据
z132411的博客
11-07 2410
canal译意为水道/管道/沟渠，主要用途是基于 MySQL 数据库增量日志解析，提供增量数据订阅和消费早期阿里巴巴因为杭州和美国双机房部署，存在跨机房同步的业务需求，实现方式主要是基于业务 trigger 获取增量变更。从 2010 年开始，业务逐步尝试数据库日志解析获取增量变更进行同步，由此衍生出了大量的数据库增量订阅和消费业务。基于日志增量订阅和消费的业务包括数据库镜像数据库实时备份索引构建和实时维护(拆分异构索引、倒排索引等)业务 cache 刷新。
JDK8 Windows系统中Java HotSpot虚拟机配置参数
纸上得来终觉浅，绝知此事要躬行
04-02 5882
原文地址：http://docs.oracle.com/javase/8/docs/technotes/tools/windows/java.html 目录 选项说明 标准选项 -agentlib:libname[=options] -agentpath:pathname[=options] -client -Dproperty=value -dsa -ea[:[packagen...
spring boot 学习笔记 （7）MyBatis XML 配置版
bihansheng2010的博客
12-06 3259
MyBatis 是现如今最流行的 ORM 框架之一，我们先来了解一下什么是 ORM 框架。 ORM 框架 对象关系映射（Object Relational Mapping，ORM）模式是一种为了解决面向对象与关系数据库存在的互不匹配的现象的技术。简单的说，ORM 是通过使用描述对象和数据库之间映射的元数据，将程序中的对象自动持久化到关系数据库中。 为什么需要 ORM？ 当你开发一个应用程序...
WCF部署：IIS 7.0 - HTTP Error 404.17 - Not Found - Static File
rise51的专栏
03-04 3919
<br />Thread: IIS 7.0 - HTTP Error 404.17 - Not Found - Static File Handler.<br />Last post 02-05-2011 3:52 PM by Amardeep_Puri. 16 replies.<br />  RSS<br />Page 1 of 1 (17 items) <br />Sort Posts: Oldest to newestNewest to oldestShortcutsSearch | Active P
红队常用命令速查
诚邀网络通信领域商务合作
10-23 2万+
红队常用命令
含中间直流的三相电力电子变压器PET仿真模型（Simulink仿真实现）
12-16
含中间直流的三相电力电子变压器PET仿真模型（Simulink仿真实现）内容概要：本文档介绍了含中间直流环节的三相电力电子变压器（PET）的Simulink仿真模型，重点在于构建和模拟PET系统的核心结构与工作原理。该仿真模型涵盖了PET的前级整流、中间直流环节以及后级逆变部分，能够实现电能的高效转换与隔离，适用于研究PET在智能电网、新能源接入等场景下的动态特性与控制策略。通过Simulink平台，用户可对系统进行稳态与暂态性能分析，验证控制算法的有效性。; 适合人群：电气工程、电力电子及相关专业的高校师生、科研人员以及从事电力系统仿真的工程技术人员。; 使用场景及目标：①用于教学演示电力电子变压器的工作原理；②支撑科研项目中对PET控制策略（如电压、电流双闭环控制）的设计与验证；③为新型电力系统中电能变换装置的开发提供仿真基础。; 阅读建议：建议结合电力电子技术基础知识学习本仿真模型，重点关注各模块的参数设置与控制逻辑实现，建议动手搭建模型并进行仿真实验，以加深对PET系统运行机制的理解。
考虑柔性负荷的综合能源系统低碳经济优化调度【考虑碳交易机制】（Matlab代码实现）
最新发布
12-16
考虑柔性负荷的综合能源系统低碳经济优化调度【考虑碳交易机制】（Matlab代码实现）内容概要：本文围绕“考虑柔性负荷的综合能源系统低碳经济优化调度”展开，重点研究在碳交易机制下如何实现综合能源系统的低碳化与经济性协同优化。通过构建包含风电、光伏、储能、柔性负荷等多种能源形式的系统模型，结合碳交易成本与能源调度成本，提出优化调度策略，以降低碳排放并提升系统运行经济性。文中采用Matlab进行仿真代码实现，验证了所提模型在平衡能源供需、平抑可再生能源波动、引导柔性负荷参与调度等方面的有效性，为低碳能源系统的设计与运行提供了技术支撑。; 适合人群：具备一定电力系统、能源系统背景，熟悉Matlab编程，从事能源优化、低碳调度、综合能源系统等相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标：①研究碳交易机制对综合能源系统调度决策的影响；②实现柔性负荷在削峰填谷、促进可再生能源消纳中的作用；③掌握基于Matlab的能源系统建模与优化求解方法；④为实际综合能源项目提供低碳经济调度方案参考。; 阅读建议：建议读者结合Matlab代码深入理解模型构建与求解过程，重点关注目标函数设计、约束条件设置及碳交易成本的量化方式，可进一步扩展至多能互补、需求响应等场景进行二次开发与仿真验证。
大学怎样用AI工具3分钟生成技术成熟度报告？.docx
12-16
大学怎样用AI工具3分钟生成技术成熟度报告？
【云原生运维】基于Kubernetes的CI/CD全流程自动化：Spring Boot应用在容器化环境下的持续集成与部署实践
12-16
内容概要：本文详细展示了在Kubernetes环境下实现CI/CD全流程的完整示例，涵盖从代码提交、自动化构建、测试、安全扫描到多环境部署的各个环节。技术栈包括GitLab CI、Docker、Helm、Kustomize、Trivy等工具，并以Spring Boot应用为例，提供了Dockerfile、Kubernetes资源配置、Helm Chart结构以及蓝绿部署、金丝雀发布等高级部署策略的具体实现。同时，文章还介绍了GitOps（ArgoCD）、HPA自动扩缩容、Prometheus监控告警等增强能力，并强调了安全性、可靠性、可观测性和成本优化的最佳实践。; 适合人群：具备一定Kubernetes、容器化和DevOps基础知识，从事后端开发、运维或平台工程的技术人员，尤其是希望落地标准化CI/CD流程的团队成员； 使用场景及目标：①构建基于Kubernetes的企业级持续交付流水线；②实现安全可控的多环境自动化部署；③集成监控告警与弹性伸缩机制提升系统稳定性；④推动GitOps理念在团队中的实践落地； 阅读建议：建议结合实际Kubernetes集群环境，逐步复现文档中的各个步骤，重点关注CI/CD配置逻辑、部署策略差异及最佳实践部分，并将其适配到自身项目体系中进行持续优化。
【分布式系统】基于Redis的Session集中存储方案：实现Web服务器高可用与横向扩展
12-16
内容概要：本文介绍了基于Redis实现分布式Session的解决方案，重点阐述了将Session集中存储于Redis集群的技术思路与优势。文中指出，传统的tomcat-redis-session-manager仅适用于Tomcat容器层的HttpSession同步，存在应用层适配局限；相比之下，推荐使用Spring Session与Redis结合的方式，实现更灵活的应用层Session管理。通过将sessionId作为key、session数据作为value存储在Redis中，可在多台应用服务器间共享Session，确保高可用性和横向扩展能力。同时，文章对比了多种保证Session一致性的架构方案，包括Session同步法、客户端存储法、反向代理Hash一致性以及后端统一存储法，并强调后端统一存储为最优选择。; 适合人群：具备Java Web开发基础，熟悉分布式架构、Redis及Session机制的1-3年经验后端研发人员； 使用场景及目标：①解决传统Web服务器集群中Session不一致问题；②实现服务无状态化设计，提升系统可扩展性与容灾能力；③在微服务或负载均衡环境下构建统一的Session管理中心； 阅读建议：学习时应结合Spring Session实际集成案例，理解其与Redis的协作机制，并深入掌握不同Session共享方案的适用边界与设计权衡。
浏览器 12.5.0 安装包
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浏览器 安装包 版本号 12.5.0。
Python自动日报生成器（多文件汇总+邮件发送）
12-16
一键读取多份 Excel/CSV，按配置表头映射后合并生成日报 Excel，可配置主键关联合并并支持 SMTP 邮 件发送。内含示例数据、配置模板、依赖列表，修改表头和收件人即可直接运行。
高等院校如何评估基金联动模式的财务杠杆效应？.docx
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(li) lenovo@lenovo-ThinkStation-P920:/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan$ pip install -v --no-cache-dir --force-reinstall . 2>&1 | tee build.log Using pip 25.1 from /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/pip (python 3.10) Looking in indexes: https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple Processing /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan Preparing metadata (setup.py): started Running command python setup.py egg_info A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.6 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<string>", line 2, in <module> File "<pip-setuptools-caller>", line 35, in <module> File "/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/setup.py", line 17, in <module> import torch File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/__init__.py", line 1471, in <module> from .functional import * # noqa: F403 File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/functional.py", line 9, in <module> import torch.nn.functional as F File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), torch.__version__ = 2.2.0+cu118 CUDA_HOME = /home/lenovo/anaconda3/envs/li CUDA version: 11.8 /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: BSD License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running egg_info creating /tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info writing /tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info/PKG-INFO writing dependency_links to /tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info/dependency_links.txt writing requirements to /tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info/requires.txt writing top-level names to /tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info/top_level.txt writing manifest file '/tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info/SOURCES.txt' reading manifest file '/tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info/SOURCES.txt' writing manifest file '/tmp/pip-pip-egg-info-w2djvsss/selective_scan.egg-info/SOURCES.txt' Preparing metadata (setup.py): finished with status 'done' Collecting torch (from selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0a/7c/0a5b3aee977596459ec45be2220370fde8e017f651fecc40522fd478cb1e/torch-2.7.1-cp310-cp310-manylinux_2_28_x86_64.whl (821.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 821.2/821.2 MB 1.1 MB/s eta 0:00:00 Collecting packaging (from selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl (66 kB) Collecting ninja (from selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (422 kB) Collecting einops (from selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/87/62/9773de14fe6c45c23649e98b83231fffd7b9892b6cf863251dc2afa73643/einops-0.8.1-py3-none-any.whl (64 kB) Collecting filelock (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/4d/36/2a115987e2d8c300a974597416d9de88f2444426de9571f4b59b2cca3acc/filelock-3.18.0-py3-none-any.whl (16 kB) Collecting typing-extensions>=4.10.0 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/69/e0/552843e0d356fbb5256d21449fa957fa4eff3bbc135a74a691ee70c7c5da/typing_extensions-4.14.0-py3-none-any.whl (43 kB) Collecting sympy>=1.13.3 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl (6.3 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.3/6.3 MB 946.1 kB/s eta 0:00:00 Link requires a different Python (3.10.18 not in: '>=3.11'): https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl (from https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.18 not in: '>=3.11'): https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz (from https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.18 not in: '>=3.11'): https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/3f/a1/46c1b6e202e3109d2a035b21a7e5534c5bb233ee30752d7f16a0bd4c3989/networkx-3.5rc0-py3-none-any.whl (from https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/networkx/) (requires-python:>=3.11) Link requires a different Python (3.10.18 not in: '>=3.11'): https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/90/7e/0319606a20ced20730806b9f7fe91d8a92f7da63d76a5c388f87d3f7d294/networkx-3.5rc0.tar.gz (from https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple/networkx/) (requires-python:>=3.11) Collecting networkx (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl (1.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 883.3 kB/s eta 0:00:00 Collecting jinja2 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl (134 kB) Collecting fsspec (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/bb/61/78c7b3851add1481b048b5fdc29067397a1784e2910592bc81bb3f608635/fsspec-2025.5.1-py3-none-any.whl (199 kB) Collecting nvidia-cuda-nvrtc-cu12==12.6.77 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/75/2e/46030320b5a80661e88039f59060d1790298b4718944a65a7f2aeda3d9e9/nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl (23.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 23.7/23.7 MB 1.1 MB/s eta 0:00:00 Collecting nvidia-cuda-runtime-cu12==12.6.77 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/e1/23/e717c5ac26d26cf39a27fbc076240fad2e3b817e5889d671b67f4f9f49c5/nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (897 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 897.7/897.7 kB 1.7 MB/s eta 0:00:00 Collecting nvidia-cuda-cupti-cu12==12.6.80 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/49/60/7b6497946d74bcf1de852a21824d63baad12cd417db4195fc1bfe59db953/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (8.9 MB) 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https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/8f/16/73727675941ab8e6ffd86ca3a4b7b47065edcca7a997920b831f8147c99d/nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (200.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 200.2/200.2 MB 761.5 kB/s eta 0:00:00 Collecting nvidia-curand-cu12==10.3.7.77 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/73/1b/44a01c4e70933637c93e6e1a8063d1e998b50213a6b65ac5a9169c47e98e/nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (56.3 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.3/56.3 MB 1.1 MB/s eta 0:00:00 Collecting nvidia-cusolver-cu12==11.7.1.2 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f0/6e/c2cf12c9ff8b872e92b4a5740701e51ff17689c4d726fca91875b07f655d/nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (158.2 MB) 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nvidia-cufile-cu12==1.11.1.6 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b2/66/cc9876340ac68ae71b15c743ddb13f8b30d5244af344ec8322b449e35426/nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 1.9 MB/s eta 0:00:00 Collecting triton==3.3.1 (from torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/8d/a9/549e51e9b1b2c9b854fd761a1d23df0ba2fbc60bd0c13b489ffa518cfcb7/triton-3.3.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (155.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 155.6/155.6 MB 758.4 kB/s eta 0:00:00 Collecting setuptools>=40.8.0 (from triton==3.3.1->torch->selective_scan==0.0.2) Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl (1.2 MB) 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A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'selective_scan'. Discussion can be found at https://github.com/pypa/pip/issues/6334 Building wheel for selective_scan (setup.py): started Running command python setup.py bdist_wheel A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.6 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<string>", line 2, in <module> File "<pip-setuptools-caller>", line 35, in <module> File "/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/setup.py", line 17, in <module> import torch File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/__init__.py", line 1471, in <module> from .functional import * # noqa: F403 File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/functional.py", line 9, in <module> import torch.nn.functional as F File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), torch.__version__ = 2.2.0+cu118 CUDA_HOME = /home/lenovo/anaconda3/envs/li CUDA version: 11.8 /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: BSD License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running bdist_wheel running build running build_ext /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/utils/cpp_extension.py:425: UserWarning: There are no g++ version bounds defined for CUDA version 11.8 warnings.warn(f'There are no {compiler_name} version bounds defined for CUDA version {cuda_str_version}') building 'selective_scan_cuda_core' extension creating /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus Emitting ninja build file /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/build.ninja... Compiling objects... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) [1/3] c++ -MMD -MF '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o'.d -pthread -B /home/lenovo/anaconda3/envs/li/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /home/lenovo/anaconda3/envs/li/include -fPIC -O2 -isystem /home/lenovo/anaconda3/envs/li/include -fPIC '-I/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/TH -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/THC -I/home/lenovo/anaconda3/envs/li/include -I/home/lenovo/anaconda3/envs/li/include/python3.10 -c -c '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan.cpp' -o '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o' -O3 -std=c++17 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0 FAILED: /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o c++ -MMD -MF '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o'.d -pthread -B /home/lenovo/anaconda3/envs/li/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /home/lenovo/anaconda3/envs/li/include -fPIC -O2 -isystem /home/lenovo/anaconda3/envs/li/include -fPIC '-I/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/TH -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/THC -I/home/lenovo/anaconda3/envs/li/include -I/home/lenovo/anaconda3/envs/li/include/python3.10 -c -c '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan.cpp' -o '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan.o' -O3 -std=c++17 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0 In file included from /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/ATen/cuda/CUDAContext.h:3, from /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan.cpp:5: /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/ATen/cuda/CUDAContextLight.h:6:10: fatal error: cuda_runtime_api.h: 没有那个文件或目录 6 | #include <cuda_runtime_api.h> | ^~~~~~~~~~~~~~~~~~~~ compilation terminated. [2/3] /home/lenovo/anaconda3/envs/li/bin/nvcc --generate-dependencies-with-compile --dependency-output '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o'.d '-I/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/TH -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/THC -I/home/lenovo/anaconda3/envs/li/include -I/home/lenovo/anaconda3/envs/li/include/python3.10 -c -c '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_fwd.cu' -o '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -arch=sm_86 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0 FAILED: /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o /home/lenovo/anaconda3/envs/li/bin/nvcc --generate-dependencies-with-compile --dependency-output '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o'.d '-I/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/TH -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/THC -I/home/lenovo/anaconda3/envs/li/include -I/home/lenovo/anaconda3/envs/li/include/python3.10 -c -c '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_fwd.cu' -o '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_fwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -arch=sm_86 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0 <command-line>: fatal error: cuda_runtime.h: 没有那个文件或目录 compilation terminated. <command-line>: fatal error: cuda_runtime.h: 没有那个文件或目录 compilation terminated. fatal : Could not open input file /tmp/tmpxft_00005756_00000000-7_selective_scan_core_fwd.cpp1.ii [3/3] /home/lenovo/anaconda3/envs/li/bin/nvcc --generate-dependencies-with-compile --dependency-output '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o'.d '-I/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/TH -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/THC -I/home/lenovo/anaconda3/envs/li/include -I/home/lenovo/anaconda3/envs/li/include/python3.10 -c -c '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_bwd.cu' -o '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -arch=sm_86 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0 FAILED: /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o /home/lenovo/anaconda3/envs/li/bin/nvcc --generate-dependencies-with-compile --dependency-output '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o'.d '-I/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan' -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/TH -I/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/include/THC -I/home/lenovo/anaconda3/envs/li/include -I/home/lenovo/anaconda3/envs/li/include/python3.10 -c -c '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/csrc/selective_scan/cus/selective_scan_core_bwd.cu' -o '/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/build/temp.linux-x86_64-cpython-310/csrc/selective_scan/cus/selective_scan_core_bwd.o' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -O3 -std=c++17 -U__CUDA_NO_HALF_OPERATORS__ -U__CUDA_NO_HALF_CONVERSIONS__ -U__CUDA_NO_BFLOAT16_OPERATORS__ -U__CUDA_NO_BFLOAT16_CONVERSIONS__ -U__CUDA_NO_BFLOAT162_OPERATORS__ -U__CUDA_NO_BFLOAT162_CONVERSIONS__ --expt-relaxed-constexpr --expt-extended-lambda --use_fast_math --ptxas-options=-v -lineinfo -arch=sm_86 --threads 4 -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=selective_scan_cuda_core -D_GLIBCXX_USE_CXX11_ABI=0 <command-line>: fatal error: cuda_runtime.h: 没有那个文件或目录 compilation terminated. <command-line>: fatal error: cuda_runtime.h: 没有那个文件或目录 compilation terminated. fatal : Could not open input file /tmp/tmpxft_00005755_00000000-7_selective_scan_core_bwd.cpp1.ii ninja: build stopped: subcommand failed. Traceback (most recent call last): File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2096, in _run_ninja_build subprocess.run( File "/home/lenovo/anaconda3/envs/li/lib/python3.10/subprocess.py", line 526, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<string>", line 2, in <module> File "<pip-setuptools-caller>", line 35, in <module> File "/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/setup.py", line 146, in <module> setup( File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/__init__.py", line 117, in setup return distutils.core.setup(**attrs) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 186, in setup return run_commands(dist) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/core.py", line 202, in run_commands dist.run_commands() File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 1002, in run_commands self.run_command(cmd) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/dist.py", line 1104, in run_command super().run_command(command) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 1021, in run_command cmd_obj.run() File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/command/bdist_wheel.py", line 370, in run self.run_command("build") File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/cmd.py", line 357, in run_command self.distribution.run_command(command) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/dist.py", line 1104, in run_command super().run_command(command) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 1021, in run_command cmd_obj.run() File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/command/build.py", line 135, in run self.run_command(cmd_name) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/cmd.py", line 357, in run_command self.distribution.run_command(command) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/dist.py", line 1104, in run_command super().run_command(command) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/dist.py", line 1021, in run_command cmd_obj.run() File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/command/build_ext.py", line 99, in run _build_ext.run(self) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 368, in run self.build_extensions() File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 871, in build_extensions build_ext.build_extensions(self) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 484, in build_extensions self._build_extensions_serial() File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 510, in _build_extensions_serial self.build_extension(ext) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/command/build_ext.py", line 264, in build_extension _build_ext.build_extension(self, ext) File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/_distutils/command/build_ext.py", line 565, in build_extension objects = self.compiler.compile( File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 684, in unix_wrap_ninja_compile _write_ninja_file_and_compile_objects( File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 1774, in _write_ninja_file_and_compile_objects _run_ninja_build( File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/utils/cpp_extension.py", line 2112, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error compiling objects for extension error: subprocess-exited-with-error × python setup.py bdist_wheel did not run successfully. │ exit code: 1 ╰─> See above for output. note: This error originates from a subprocess, and is likely not a problem with pip. full command: /home/lenovo/anaconda3/envs/li/bin/python3.10 -u -c ' exec(compile('"'"''"'"''"'"' # This is <pip-setuptools-caller> -- a caller that pip uses to run setup.py # # - It imports setuptools before invoking setup.py, to enable projects that directly # import from `distutils.core` to work with newer packaging standards. # - It provides a clear error message when setuptools is not installed. # - It sets `sys.argv[0]` to the underlying `setup.py`, when invoking `setup.py` so # setuptools doesn'"'"'t think the script is `-c`. This avoids the following warning: # manifest_maker: standard file '"'"'-c'"'"' not found". # - It generates a shim setup.py, for handling setup.cfg-only projects. import os, sys, tokenize, traceback try: import setuptools except ImportError: print( "ERROR: Can not execute `setup.py` since setuptools failed to import in " "the build environment with exception:", file=sys.stderr, ) traceback.print_exc() sys.exit(1) __file__ = %r sys.argv[0] = __file__ if os.path.exists(__file__): filename = __file__ with tokenize.open(__file__) as f: setup_py_code = f.read() else: filename = "<auto-generated setuptools caller>" setup_py_code = "from setuptools import setup; setup()" exec(compile(setup_py_code, filename, "exec")) '"'"''"'"''"'"' % ('"'"'/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/setup.py'"'"',), "<pip-setuptools-caller>", "exec"))' bdist_wheel -d /tmp/pip-wheel-fm2tmb40 cwd: /media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/ Building wheel for selective_scan (setup.py): finished with status 'error' ERROR: Failed building wheel for selective_scan Running setup.py clean for selective_scan Running command python setup.py clean A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.6 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<string>", line 2, in <module> File "<pip-setuptools-caller>", line 35, in <module> File "/media/lenovo/PHILIPS/Mamba_20250409_ torch22_cuda118/VMamba-main/kernels/selective_scan/setup.py", line 17, in <module> import torch File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/__init__.py", line 1471, in <module> from .functional import * # noqa: F403 File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/functional.py", line 9, in <module> import torch.nn.functional as F File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/__init__.py", line 1, in <module> from .modules import * # noqa: F403 File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/__init__.py", line 35, in <module> from .transformer import TransformerEncoder, TransformerDecoder, \ File "/home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 20, in <module> device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), torch.__version__ = 2.2.0+cu118 CUDA_HOME = /home/lenovo/anaconda3/envs/li CUDA version: 11.8 /home/lenovo/anaconda3/envs/li/lib/python3.10/site-packages/setuptools/dist.py:759: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: BSD License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running clean removing 'build/temp.linux-x86_64-cpython-310' (and everything under it) 'build/lib.linux-x86_64-cpython-310' does not exist -- can't clean it 'build/bdist.linux-x86_64' does not exist -- can't clean it 'build/scripts-3.10' does not exist -- can't clean it removing 'build' Failed to build selective_scan ERROR: Failed to build installable wheels for some pyproject.toml based projects (selective_scan)
07-04
<think>我们正在尝试安装一个名为`selective_scan`的包，但遇到了编译错误和NumPy兼容性问题。根据引用[2]和[4]，我们可以推断：1.该包可能是`mamba_ssm`的一部分，因为引用[1]和[4]都提到了安装`mamba_ssm`。 2.编译错误可能与CUDA版本有关，需要确保PyTorch的CUDA版本与系统CUDA工具包一致。3.NumPy兼容性问题可能是由于该包要求旧版本的NumPy（如1.x）而当前环境是NumPy2.x。根据引用[4]，一个推荐的安装顺序是：```bashcondacreate -n mamba_envpython=3.10.13condaactivate mamba_envconda installcudatoolkit==11.8 -c nvidiapip installtorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-urlhttps://download.pytorch.org/whl/cu118condainstall -c "nvidia/label/cuda-11.8.0"cuda-nvcccondainstall packagingpipinstall causal-conv1d==1.1.1pipinstall mamba-ssm```但是，我们当前的问题是在安装`selective_scan`时出现的，而它可能是`mamba-ssm`的依赖。因此，我们按照以下步骤解决：###步骤1：创建并激活Conda环境（确保Python版本为3.10）```bashconda create-nmamba_env python=3.10.13conda activatemamba_env``` ###步骤2：安装匹配的CUDA工具包和PyTorch```bashcondainstall cudatoolkit=11.8-cnvidiapipinstall torch==2.1.1torchvision==0.16.1torchaudio==2.1.1--index-url https://download.pytorch.org/whl/cu118```###步骤3：安装编译所需的NVCC```bashconda install-c"nvidia/label/cuda-11.8.0"cuda-nvcc``` ###步骤4：安装其他依赖```bashconda installpackaging``` ###步骤5：安装causal-conv1d和mamba-ssm```bashpip installcausal-conv1d==1.1.1pip installmamba-ssm``` ###处理NumPy兼容性问题如果安装过程中出现NumPy版本不兼容（例如要求NumPy<2.0），我们可以先安装兼容的NumPy版本： ```bashpip install"numpy<2.0"``` 然后再尝试安装`mamba-ssm`。###替代方案：使用预编译的wheel如果上述步骤仍然失败，我们可以尝试使用预编译的wheel（如引用[1]所示）：```bashpipinstall causal_conv1d-1.0.0+cu118torch2.1cxx11abiFALSE-cp310-cp310-linux_x86_64.whlpip installmamba_ssm-1.0.1+cu118torch2.1cxx11abiFALSE-cp310-cp310-linux_x86_64.whl```注意：这些wheel文件需要从项目发布页面下载，并且确保与你的系统（Linux）、Python版本（cp310）和CUDA版本（cu118）匹配。###如果仍然遇到编译错误1. **确保编译环境完整**：在Linux上，安装build-essential：```bashsudo aptupdate &&sudo aptinstall build-essential``` 2. **强制重新编译**：设置环境变量强制重新编译：```bashexportCAUSAL_CONV1D_FORCE_BUILD=TRUEexport SELECTIVE_SCAN_FORCE_BUILD=TRUEpip install--no-cache-dir--force-reinstallcausal-conv1d mamba-ssm``` 3. **检查CUDA架构**：如果错误提到不支持的GPU架构（如`compute_89`），则需要设置环境变量指定支持的架构（例如RTX4090需要8.9，但CUDA11.8可能不支持，可尝试使用CUDA12.1）：```bashexportTORCH_CUDA_ARCH_LIST="8.6"#例如，使用8.6代替8.9``` ###验证安装```pythonimporttorchfrom mamba_ssmimport Mambaprint(torch.__version__) print(torch.cuda.is_available()) #测试Mamba模块model =Mamba(d_model=64,d_state=16,d_conv=4,expand=2)print(model)``` ###注意事项-如果使用Windows，请确保在VisualStudio的命令提示符（如x64Native ToolsCommand Prompt）中执行安装，并安装C++构建工具[^3]。-如果使用Docker，建议使用NVIDIA官方提供的PyTorch镜像作为基础镜像。 §§相关问题§§1.如何查询显卡的计算能力(compute capability)？2.PyTorch不同版本对应的CUDA版本映射关系是什么？ 3. Docker中如何配置支持CUDA的Python环境？4.如何为开源项目添加跨平台编译支持？</think>### 解决 selective_scan 安装问题的完整指南 #### 核心问题分析 1. **CUDA 版本不匹配**： - PyTorch 的 CUDA 版本与系统 CUDA 工具包不一致（常见于 RTX 40 系列显卡） - 错误表现：`nvcc fatal: Unsupported gpu architecture 'compute_89'`[^2] 2. **NumPy 2.x 兼容性问题**： - selective_scan 依赖旧版 NumPy API（1.x），与 NumPy 2.x 不兼容 - 错误表现：`undefined symbol: PyArray_Type` 或导入错误 3. **编译环境缺失**： - Linux：缺少基础编译工具（build-essential） - Windows：缺少 MSVC 编译工具链[^3] --- ### 分步解决方案 #### 步骤1：创建专用虚拟环境（关键） ```bash conda create -n mamba_env python=3.10.13 # 使用兼容的Python版本[^4] conda activate mamba_env ``` #### 步骤2：安装匹配的 CUDA 工具链 ```bash # 安装 CUDA 11.8 工具包 conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit cuda-nvcc # 安装匹配的 PyTorch pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 \ --index-url https://download.pytorch.org/whl/cu118[^4] ``` #### 步骤3：解决 NumPy 兼容性问题 ```bash # 强制安装 NumPy 1.x (兼容版本) pip install "numpy<2.0" --force-reinstall ``` #### 步骤4：安装编译依赖 ```bash # Linux 系统 sudo apt update && sudo apt install build-essential ninja-build # Windows 系统 安装 Visual Studio 2022，选择 "使用C++的桌面开发"[^3] ``` #### 步骤5：安装 selective_scan 组件 ```bash # 先安装依赖组件 pip install causal-conv1d==1.1.1 packaging # 确保版本匹配[^4] # 强制重新编译安装 export SELECTIVE_SCAN_FORCE_BUILD=TRUE # Linux/Mac # Windows 使用: set SELECTIVE_SCAN_FORCE_BUILD=TRUE [^3] pip install --no-cache-dir --force-reinstall mamba-ssm ``` #### 替代方案：使用预编译 wheel（推荐） ```bash # 下载匹配的预编译包（示例URL需替换为实际） pip install https://example.com/mamba_ssm-1.0.1+cu118torch2.1.1-cp310-cp310-linux_x86_64.whl[^1] ``` --- ### 常见错误处理 **错误1：CUDA 架构不支持** ```log nvcc fatal: Unsupported gpu architecture 'compute_89' ``` **解决**： ```bash # 指定支持的架构（RTX 30系用8.6，RTX 40系需CUDA>=11.8） export TORCH_CUDA_ARCH_LIST="8.6" pip install --force-reinstall mamba-ssm ``` **错误2：NumPy 兼容性问题** ```log ImportError: numpy.core.multiarray failed to import ``` **解决**： ```bash pip uninstall -y numpy pip install "numpy==1.23.5" # 确认兼容版本 ``` **错误3：MSVC 缺失（Windows）** ```log error: Microsoft Visual C++ 14.0 or greater is required ``` **解决**： 1. 安装 [Visual Studio Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) 2. 在 **x64 Native Tools Command Prompt** 中执行安装[^3] --- ### 验证安装成功 ```python import torch from mamba_ssm import Mamba print(torch.cuda.is_available()) # 应返回 True # 测试模型 model = Mamba( d_model=256, d_state=16, d_conv=4, expand=2 ) print(model) # 应显示模型结构 ``` > **关键提示**：对于 RTX 40 系列显卡（计算能力8.9+），必须使用 CUDA ≥ 11.8 和 PyTorch ≥ 2.1.1。若持续失败，建议使用 Docker 环境： > ```dockerfile > FROM nvcr.io/nvidia/pytorch:23.10-py3 > RUN pip install mamba-ssm > ```
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