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按照官方文档《Atlas 300 AI加速卡Mind Studio安装》安装了mind studio以后,mind studio服务可以正常启动,但是通过浏览器ip:port的方式无法访问。Bad RequestThis combination of host and port requires TLS.在哪里可以看到失败的log?
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比如:https://repo.huaweicloud.com/ubuntu-cloud-images/releases/bionic/release/ 对应于https://cloud-images.ubuntu.com/releases/bionic/release/根本就没下。这从Ubuntu下特别慢,lxd的各种image,如能同步非常有用。大厂中腾讯云同步是好的。
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求mini_mind_studio_Ubuntu_arm_server.tar.gz
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辛苦帮忙看一下,问题如图所示:制作sd卡时,设置了usb ip为192.168.225.160:ubuntu的网卡情况:然后在ubuntu上面不能ping通板子:在mind studio上面也不能连接上板子:
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刚安装好的时候,可以通过127.0.0.1:8888进入,进入界面后没有提示“Workspace is running”,新建工程也没有红色框里面的选项然后重启Ubuntu,就进入不了Mindstudio
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ubuntu虚拟机无法连接网络。已自动配置NAT连接主机连接WIFI, ifconfig截图如下尝试把虚拟机subnet ip改成和主机同一网段后仍然无法上网。
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问题背景: 我想用自己的数据集训练一个目标检测网络。 华为提供了fasterrcnn模型( https://github.com/Ascend/models/tree/master/computer_vision/object_detect/faster_rcnn ),根据描述,这个模型是基于py-faster-rcnn构建的,这里面可能有一些官方caffe不支持的算子,所以官方版的caffe是不是不能直接使用?(我直接用官方版caffe替代py-faster-rcnn中的caffe后测试demo运行不通过。)因此我也打算使用这个版本来训练我的网络。问题: 但是在配置环境的过程中,我发现由于长久没有更新,这个py-faster-rcnn中的caffe原生支持的环境是cudnn v4,对应cuda7.0,而cuda7.0只能装在Ubuntu14,我的环境是Ubuntu16。所以我根据该教程在Ubuntu16.04上安装了cuda7.0,在这个过程中将gcc和g++的版本改为了4.8。结果在编译的过程中出现了.build_release/lib/libcaffe.so: undefined reference to `google::protobuf::Message::InitializationErrorString() const' ...等错误。 我寻找了一些解决办法,但是都行不通。(在此之前,我的环境是cuda9,我已经成功编译过官方最新的caffe。)这时,我又将之前编译好的官方caffe清除(make clean)掉,然后再次编译,此时却编译失败了,与编译py-faster-rcnn中的caffe的错误是同一种类型。然后我又将gcc和g++的版本切换回原来的5.4,然后再编译就通过了,所以我推测失败的原因应该是gcc版本问题。 接着我安装了cuda8.0和cudnn v5,gcc和g++保持为5.4,但是此时编译仍然出错:./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_0000286f_00000000-5_adadelta_solver.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/solvers/adadelta_solver.o' failed make: *** [.build_release/cuda/src/caffe/solvers/adadelta_solver.o] Error 1 make: *** Waiting for unfinished jobs.... nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). ./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_0000287b_00000000-5_adagrad_solver.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/solvers/adagrad_solver.o' failed make: *** [.build_release/cuda/src/caffe/solvers/adagrad_solver.o] Error 1 nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). ./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_00002874_00000000-5_rmsprop_solver.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/solvers/rmsprop_solver.o' failed make: *** [.build_release/cuda/src/caffe/solvers/rmsprop_solver.o] Error 1 nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). ./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_00002892_00000000-5_sgd_solver.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/solvers/sgd_solver.o' failed make: *** [.build_release/cuda/src/caffe/solvers/sgd_solver.o] Error 1 nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). ./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_0000287a_00000000-5_nesterov_solver.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/solvers/nesterov_solver.o' failed make: *** [.build_release/cuda/src/caffe/solvers/nesterov_solver.o] Error 1 nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). ./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_00002899_00000000-5_adam_solver.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/solvers/adam_solver.o' failed make: *** [.build_release/cuda/src/caffe/solvers/adam_solver.o] Error 1 nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). ./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_0000288b_00000000-5_im2col.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/util/im2col.o' failed make: *** [.build_release/cuda/src/caffe/util/im2col.o] Error 1 nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). ./include/caffe/util/cudnn.hpp(126): error: argument of type "int" is incompatible with parameter of type "cudnnNanPropagation_t" ./include/caffe/util/cudnn.hpp(126): error: too few arguments in function call 2 errors detected in the compilation of "/tmp/tmpxft_000028ca_00000000-5_math_functions.cpp4.ii". Makefile:604: recipe for target '.build_release/cuda/src/caffe/util/math_functions.o' failed make: *** [.build_release/cuda/src/caffe/util/math_functions.o] Error 1这个应该还是因为cudnn版本不符合py-faster-rcnn中的caffe所要求的版本导致的。总结:所以我想请问的是,华为官方是使用py-faster-rcnn中的caffe来训练fasterrcnn的吗?如果是的话,那开发环境是怎样的呢?有没有对代码做什么修改呢?
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Ascend310 device安装、的X86服务器上该X86服务器为Cenos7.4,同时应该在这个X86主机上也安装了Altas 300 DDK驱动。开发环境是Mind Studio,要求安装的环境智能是Ubuntu,打算安装在自己的办公电脑上;如果这样安装Mind Studio,开发demo程序后,怎么驱动X86服务器上的Ascend310 device工作?是不是直接把编译后的程序ftp到X86上,运行就可以了(程序配置文件指定device id)?有没有一种方式,把办公电脑的Ubuntu通过网络对接到X86的CentOS,通过什么中间软件驱动Ascend310工作?如果是ftp方式,在办公电脑Ubuntu上安装的Mind Studio,有没有类似仿真模块?
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软件介绍Docker是一个开源的应用容器引擎,让开发者可以打包他们的应用以及依赖包到一个可移植的镜像中,然后发布到任何流行的Linux或Windows机器上,也可以实现虚拟化。容器是完全使用沙箱机制,相互之间不会有任何接口。支持的操作系统经过华为云严格实测,以下操作系统在鲲鹏生态中可以完整运行Docker的全部功能:l CentOS 7.5centos-extra仓库必须处于“enabled”状态。这是操作系统默认配置,如果你已经设置成“disabled”,则需要重新设置。相关命令如下:l 查询仓库状态:yum repolist all例如:yum repolist all|grep "CentOS-7 - Extras"l 设置为“enabled”状态:yum-config-manager --enable例如: yum-config-manager --enable "CentOS-7 - Extras - mirrors.huaweicloud.com"l 设置为“disabled”状态:yum-config-manager --disable例如:yum-config-manager --disable "CentOS-7 - Extras - mirrors.huaweicloud.com"l EulerOS 2.8l Ubuntu 18.04安装与部署方式Ubuntu操作系统1. 准备环境准备实例从华为云官网购买鲲鹏云服务ECS实例,详细配置如下:类别子项版本云服务器配置ECS实例类型kc1.xlarge.4ECS配置4U16GBEVS(系统盘)高IO(40GB)EVS(数据盘)高IO(40GB)云OSUbuntu18.04Kernel4.15.0-29安装依赖包执行以下命令安装依赖包。sudo apt-get updatesudo apt-get install apt-transport-https ca-certificates curl gnupg-agent software-properties-common2. 执行安装方式一:通过软件仓库安装1) 老版本的Docker的命名为“docker”、“docker.io” 或 “docker-engine”,如果安装了这些版本,需要先卸载掉。保存在“/var/lib/docker”/中的内容,包括图片、磁盘和网络配置等都会保留下来。sudo apt-get remove docker docker-engine docker.io containerd runc2) 添加Docker官方GPG key。curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -3) 设置Docker CE软件仓库版本为arm64。sudo add-apt-repository \"deb [arch=arm64] https://download.docker.com/linux/ubuntu \$(lsb_release -cs) \stable"4) 安装docker-ce软件。sudo apt-get updatesudo apt-get install docker-ce docker-ce-cli containerd.io如果需要安装指定版本的docker-ce,可以执行以下命令:sudo apt-get install docker-ce=<VERSION_STRING> docker-ce-cli=<VERSION_STRING> containerd.io方式二:下载软件包安装1) 通过https://download.docker.com/linux/ubuntu/dists/bionic/pool/stable/arm64/,下载指定版本的软件包。2) 执行命令安装软件包及依赖。“package.deb”为下载的软件包。sudo dpkg -i /path/to/package.deb3. 启动软件1) 启动docker。sudo systemctl start docker2) 使用一个hello-world镜像验证docker是否正常。sudo docker run hello-world回显内容如下:Unable to find image 'hello-world:latest' locallylatest: Pulling from library/hello-world3b4173355427: Pull complete Digest: sha256:41a65640635299bab090f783209c1e3a3f11934cf7756b09cb2f1e02147c6ed8Status: Downloaded newer image for hello-world:latest Hello from Docker!This message shows that your installation appears to be working correctly. To generate this message, Docker took the following steps: 1. The Docker client contacted the Docker daemon. 2. The Docker daemon pulled the "hello-world" image from the Docker Hub. (arm64v8) 3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading. 4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal. To try something more ambitious, you can run an Ubuntu container with: $ docker run -it ubuntu bash Share images, automate workflows, and more with a free Docker ID: https://hub.docker.com/ For more examples and ideas, visit: https://docs.docker.com/get-started/CentOS操作系统1. 准备环境准备实例从华为云官网购买鲲鹏云服务ECS实例,详细配置如下:类别子项版本云服务器配置ECS实例类型rc3.xlarge.4ECS配置4U14GBEVS(系统盘)高IO(40GB)EVS(数据盘)高IO(40GB)云OSCentOS7.5Kernel4.14.0-49 安装依赖包执行以下命令安装依赖包。sudo yum install -y yum-utils device-mapper-persistent-data lvm22. 执行安装方式一:使用软件仓库安装1) 老版本的docker的命名为“docker”或 “docker-engine”,如果安装了这些版本,需要先卸载掉。保存在“/var/lib/docker/”中的内容,包括图片、磁盘和网络配置等都会保留下来。sudo yum remove docker docker-client docker-client-latest docker-common docker-latest docker-latest-logrotate2) 配置软件仓库。sudo yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo3) 安装docker-ce。sudo yum install docker-ce docker-ce-cli containerd.io这个命令总是会安装最新版本的docker-ce,如果需要安装指定版本的可以参考下面的操作:sudo yum install docker-ce-<VERSION_STRING> docker-ce-cli-<VERSION_STRING> containerd.io方式二:下载软件包安装1) 通过https://download.docker.com/linux/centos/7/aarch64/stable/Packages/,下载指定版本的软件包。2) 执行命令安装软件包及依赖。“package.rpm”为下载的软件包。sudo yum install /path/to/package.rpm3. 启动软件1) 启动Docker。sudo systemctl start docker2) 使用一个hello-world镜像验证Docker是否正常。sudo docker run hello-world回显内容如下:Unable to find image 'hello-world:latest' locallylatest: Pulling from library/hello-world3b4173355427: Pull complete Digest: sha256:41a65640635299bab090f783209c1e3a3f11934cf7756b09cb2f1e02147c6ed8Status: Downloaded newer image for hello-world:latest Hello from Docker!This message shows that your installation appears to be working correctly. To generate this message, Docker took the following steps: 1. The Docker client contacted the Docker daemon. 2. The Docker daemon pulled the "hello-world" image from the Docker Hub. (arm64v8) 3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading. 4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal. To try something more ambitious, you can run an Ubuntu container with: $ docker run -it ubuntu bash Share images, automate workflows, and more with a free Docker ID: https://hub.docker.com/ For more examples and ideas, visit: https://docs.docker.com/get-started/软件下载源码下载:立即下载
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Unescaped left brace in regex is illegal here in regex; marked by <-- HERE in m/\${ <-- HERE ([^ \t=:+{}]+)}/ at /usr/bin/automake line 3930.sub substitute_ac_subst_variables{ my ($text) = @_; $text =~ s/\${([^ \t=:+{}]+)}/substitute_ac_subst_variables_worker ($1)/ge; return $text;}需要修改为 $text =~ s/\$\{([^ \t=:+{}]+)}/substitute_ac_subst_variables_worker ($1)/ge;
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试图在树莓派的ubuntu mate16.04系统上安装时遇到如图bug,已经安装了各种依赖环境,并且版本和在x86ubuntu16.04系统上装的一样 求问是不是板子系统的原因,ubuntu mate16.04是不是并不适配mind studio
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#化鲲为鹏,我有话说#ARM(鲲鹏),给您不一样的感觉。解决http://ddebs.ubuntu.com源下各类架构包太慢的问题。这个源貌似国内没有镜像,apt-get国内用户有些一秒20k甚至1000b,^_^!那要用到上面的各种接近1g的架构包怎么办呢?现在以ubuntu x64为例,arm64作为习题请自行推理练习。比如800m的debuginfo kernel xx架构 ddeb包apt-get非常慢,那么可以选择自己编译源码来生成。 $ cd $HOME $ sudo apt-get install dpkg-dev debhelper gawk $ mkdir tmp $ cd tmp $ sudo apt-get build-dep --no-install-recommends linux-image-$(uname -r) $ apt-get source linux-image-$(uname -r)cd linux-signed-hwe-5.0.0/上面是我这次实际生成的目录,请根据实际情况进入自己的目录。sudo apt-get install pkg-config-dbgsym fakeroot debian/rules clean AUTOBUILD=1 fakeroot debian/rules binary skipdbg=false sudo dpkg -i ../linux-image-5.0.0-27-generic-dbgsym_5.0.0-27.28~18.04.1_amd64.ddeb提示依赖linux-image-unsigned-5.0.0-27-generic-dbgsym这...已经做了一个包,再做一个就不符合这篇文章炫酷装逼的风格了...所以必须用另一外一条路子来展现拉风,就是shell拼命刷新那种(先假装不知道apt-fast,哈哈哈)这时候不要去百度,去bing搜索linux-image-unsigned-5.0.0-27-generic-dbgsym就能获得下载链接http://ddebs.ubuntu.com/pool/main/l/linux/linux-image-unsigned-5.0.0-27-generic-dbgsym_5.0.0-27.28_amd64.ddebsudo dpkg -r linux-image-5.0.0-27-generic-dbgsym sudo apt-get install axel cd ~ mkdir axel axel -n 1024 -o axel/ http://ddebs.ubuntu.com/pool/main/l/linux/linux-image-unsigned-5.0.0-27-generic-dbgsym_5.0.0-27.28_amd64.ddeb已下载 834.4 兆字节,用时 1 分 34 秒。(9018.85 KB/s)cd axel/ sudo dpkg -i linux-image-unsigned-5.0.0-27-generic-dbgsym_5.0.0-27.28_amd64.ddeb cd ~ sudo dpkg -i tmp/linux-signed-hwe-5.0.0/linux-image-5.0.0-27-generic-dbgsym_5.0.0-27.28~18.04.1_amd64.ddeb解决依赖关系~
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