SystemCenterOperationsManager2007安装指南

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“MOM_2007_step-by-step_installation.pdf”是一份关于Microsoft Operations Manager (MOM) 2007的详尽安装指南,由微软中国有限公司于2007年3月发布。该文档详细阐述了在安装MOM 2007时所需的组件和前提条件。 MOM 2007 是微软的系统中心操作管理器,它是一个企业级的IT基础设施监控解决方案,用于管理网络中的服务器、应用程序和服务。以下是对MOM 2007主要组件及其安装要求的详细解释: 1. 运行数据库 (Operational Database): - 基于Microsoft SQL Server 2005的企业版或标准版,需要已安装Service Pack 1。 - Microsoft Core XML Services (MSXML) 6.0是必需的,通常会随MOM 2007安装包一起自动下载。 - 需要安装特定的SQL 2005补丁,例如KB918222。 2. 管理服务器 (Management Server): - 要求安装Microsoft .NET Framework 2.0和3.0。 3. 管理员控制台 (Operations Console): - 需要Windows PowerShell,它是MOM 2007命令行Shell的基础。 - 对于创建Management Pack,需要Microsoft Office Word 2003(带有.NET Programmability feature)以及Microsoft Visual Studio 2005 Tools for the Microsoft Office System。 4. 客户端代理 (Agent): - Windows Installer 3.1是安装MOM 2007客户端代理的前提。 5. 报表数据库仓库 (Data Warehouse): - 数据仓库组件可能需要额外的SQL Server配置和安装步骤,以存储报告和分析数据。 在安装过程中,必须确保所有先决条件都已满足,包括操作系统兼容性、硬件需求、权限设置以及网络连接。每个组件的安装都需要按照特定顺序进行,并且可能涉及安装、配置、更新和验证等步骤。安装MOM 2007的目标是建立一个稳定、高效的企业级监控环境,能够实时监测系统健康状况,及时发现并解决问题,确保业务连续性和效率。 此外,安装完成后,还涉及到对MOM 2007的配置,包括定义监控策略、创建自定义Management Packs、设置警报和通知规则,以及集成其他IT管理系统。MOM 2007的强大之处在于其灵活性和可扩展性,可以适应不同规模的企业环境,并与现有的IT基础设施无缝集成。因此,全面理解并正确安装MOM 2007对于优化IT运维至关重要。

global min_z hhh ddd max_z td global sametoolZmin sametoolZmax mom_tool_number toolnumber sametooltcut set toolnumber $mom_tool_number if {[info exists sametoolZmin($toolnumber)]} { if { $sametoolZmin($toolnumber) > $min_z } { set sametoolZmin($toolnumber) $min_z } } else { set sametoolZmin($toolnumber) $min_z } if {[info exists sametoolZmax($toolnumber)]} { if { $sametoolZmax($toolnumber) > $max_z } { set sametoolZmax($toolnumber) $max_z } } else { set sametoolZmax($toolnumber) $max_z } global mom_machine_time tcut tcut1 ztc tlist_zt global mom_next_oper_has_tool_change td global mom_current_oper_is_last_oper_in_program if {([info exists mom_next_oper_has_tool_change] && $mom_next_oper_has_tool_change == "YES") || ([info exists mom_current_oper_is_last_oper_in_program] && $mom_current_oper_is_last_oper_in_program == "YES")} { set tcut1 [format "%.2f" [expr $mom_machine_time-$tcut]] if {[info exists sametooltcut($toolnumber)]} { set sametooltcut($toolnumber) [expr $sametooltcut($toolnumber)+$tcut1] } else { set sametooltcut($toolnumber) $tcut1 #MOM_output_literal "(Machine time: [format "%.2f" [expr $mom_machine_time-$tcut]] MIN)" } set ztc [expr $ztc+1] if { $td != 0 } { set tlist_zt($ztc) "(Z+:[string trimright [format "%.2f" $max_z] "0"] Z-:[string trimright [format "%.2f" $min_z] "0"] Time:$tcut1\M D=[format "%02.0f" $td])" } else { set tlist_zt($ztc) "(Z+:[string trimright [format "%.2f" $max_z] "0"] Z-:[string trimright [format "%.2f" $min_z] "0"] Time:$tcut1\M)" } #MOM_output_literal "$tlist_zt($ztc)" }

2023-07-08 上传

class ASPP(nn.Module) def init(self, dim_in, dim_out, rate=1, bn_mom=0.1) super(ASPP, self).init() self.branch1 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 1, 1, padding=0, dilation=rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch2 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=4 rate, dilation=4 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch3 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=8 rate, dilation=8 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch4 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=12 rate, dilation=12 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch5 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=16 rate, dilation=16 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) self.branch6 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=20 rate, dilation=20 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True) ) self.branch7 = nn.Sequential( nn.Conv2d(dim_in, dim_out, 3, 1, padding=24 rate, dilation=24 rate, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True) ) self.branch8_conv = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=True) self.branch8_bn = nn.BatchNorm2d(dim_out, momentum=bn_mom) self.branch8_relu = nn.ReLU(inplace=True) self.conv_cat = nn.Sequential( nn.Conv2d(dim_out 8, dim_out, 1, 1, padding=0, bias=True), nn.BatchNorm2d(dim_out, momentum=bn_mom), nn.ReLU(inplace=True), ) def forward(self, x) [b, c, row, col] = x.size() conv1x1 = self.branch1(x) conv3x3_1 = self.branch2(x) conv3x3_2 = self.branch3(x) conv3x3_3 = self.branch4(x) conv3x3_4 = self.branch5(x) conv3x3_5 = self.branch6(x) conv3x3_6 = self.branch7(x) global_feature = torch.mean(x, 2, True) global_feature = torch.mean(global_feature, 3, True) global_feature = self.branch8_conv(global_feature) global_feature = self.branch8_bn(global_feature) global_feature = self.branch8_relu(global_feature) global_feature = F.interpolate(global_feature, (row, col), None, 'bilinear', True) feature_cat = torch.cat([conv1x1, conv3x3_1, conv3x3_2, conv3x3_3, conv3x3_4, conv3x3_5, conv3x3_6, global_feature], dim=1) result = self.conv_cat(feature_cat) return result用深度可分离卷积代替这段代码的3×3卷积

2023-06-12 上传