{
  "public": {
    "netWorkError": "网络或后端服务错误，请确保服务正常并重新加载页面。",
    "browserWarning": "您当前的浏览器可能导致部分功能失效或不可使用，建议使用Chrome 65版本以上的浏览器。",
    "timeout": "超时，请重新请求。",
    "noData": "暂无数据",
    "noSupport": "暂不支持",
    "emptyData": "无数据",
    "reset": "重置",
    "tagFilterPlaceHolder": "请输入需要的标签(支持正则表达式)",
    "dataError": "获取到的数据异常",
    "regIllegal": "请输入正确的检索条件",
    "stayTuned": "敬请期待",
    "select": "请选择",
    "search": "请搜索",
    "enter": "请输入",
    "remark": "备注",
    "zhLanguage": "中",
    "enLanguage": "EN",
    "sure": "确定",
    "clear": "清空",
    "cancel": "取消",
    "selectAll": "全选",
    "deselectAll": "取消全选",
    "dataLoading": "数据加载中",
    "notice": "提示",
    "caseMode": "不区分大小写",
    "all": "全部",
    "details": "详情",
    "delete": "删除",
    "dark": "深色主题",
    "light": "浅色主题",
    "noOperatorData": "非异构网络暂无数据",
    "noAICPUOperatorData": "该网络无AICPU算子，暂无数据。",
    "noStepStraceData": "迭代轨迹目前仅支持Graph模式单图场景，暂不支持PyNative、异构、多子图等场景。"
  },
  "unit": {
    "KiB": "KiB",
    "MiB": "MiB",
    "GiB": "GiB"
  },
  "symbols": {
    "leftbracket": "（",
    "rightbracket": "）",
    "point": "·",
    "slashes": "/",
    "colon": "：",
    "fullStop": "。",
    "comma": "，"
  },
  "header": {
    "refreshData": "刷新数据",
    "refreshingData": "正在刷新数据",
    "timeReload": "自动刷新",
    "timeSecond": "秒",
    "timeReloadScope": "自动刷新时长范围为[3, 300]秒"
  },
  "summaryManage": {
    "summaryList": "训练列表",
    "currentFolder": "训练日志根路径：",
    "sorting": "序号",
    "summaryPath": "训练日志路径",
    "createTime": "创建时间",
    "updateTime": "更新时间",
    "operation": "操作",
    "viewDashboard": "训练看板",
    "viewProfiler": "性能分析",
    "viewOfflineDebugger": "离线调试器",
    "lossFunctionAnalysis": "损失函数多维分析",
    "modelTraceback": "模型溯源",
    "dataTraceback": "数据溯源",
    "comparePlate": "对比看板",
    "scalarComparison": "标量对比",
    "lossComparison": "损失函数图形对比",
    "disableLossFunctionTip": "无损失函数分析日志，无法查看损失函数多维分析",
    "disableProfilerTip": "无profiler日志，无法查看性能分析",
    "disableDashboardTip": "无summary日志或pb文件，无法查看训练看板",
    "disableParameterTip": "无lineage日志，无法查看参数详情",
    "disableOfflineDebugger": "无Debugger日志，无法查看离线调试器",
    "openNewTab": "打开新页签",
    "paramDetails": "参数详情",
    "trainingParamDetails": "训练参数详情",
    "tracebackAnalysis": "溯源分析",
    "compareAnalysis": "对比分析",
    "guidTipFirst": "请在训练脚本中",
    "guidTipSecond": "收集summary",
    "guidTipThird": "，保存",
    "guidTipForth": "数据。",
    "scalarTipFirst": "标量",
    "histogramGuidTipFirst": "参数分布",
    "tensorTipFirst": "张量",
    "graphTipFirst": "计算图",
    "graphTipSecond": "结构数据。",
    "dataProcessTipFirst": "数据处理过程",
    "imageTipFirst": "指定图片",
    "guidUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html",
    "scalarUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id3",
    "histogramUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id4",
    "tensorUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id8",
    "graphUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id5",
    "dataProcessUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id6",
    "imageUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/dashboard.html#id7",
    "sessionLimit": "离线调试器的session个数超过上限",
    "sessionLimitNum": "最多同时存在2个",
    "sessionLists": "目前存在的session列表",
    "deleteSessionConfirm": "此操作将删除当前session, 是否继续?",
    "deleteSessionSuccess": "删除session成功!",
    "dumpPath": "Dump路径"
  },
  "modelTraceback": {
    "summaryPath": "训练日志路径",
    "trainSetPath": "训练集路径",
    "testSetPath": "测试集路径",
    "trainingSampleNum": "训练样本数量",
    "testSampleNum": "测试样本数量",
    "showAllData": "显示全部",
    "showSelected": "确认筛选",
    "hideSelected": "隐藏所选",
    "network": "网络",
    "optimizer": "优化器",
    "lossFunc": "损失函数",
    "learningRate": "学习率",
    "modelSize": "模型大小",
    "dataProcess": "数据处理",
    "noDataFound": "暂无满足筛选条件的数据",
    "allHide": "数据已全部隐藏",
    "userDefined": "自定义数据",
    "metric": "度量指标",
    "deviceNum": "device数目",
    "mixedItemMessage": "该参数含有多种类型数据，无法筛选展示",
    "notSupportSelected": "该参数类型暂不支持筛选",
    "displayColumn": "请选择展示列",
    "mustExist": "必选项",
    "remarkValidation": "备注为英文字母、数字、中文、下划线、中划线、点的组合，长度范围为[1,128]字符",
    "changeSuccess": "修改成功",
    "metricLabel": "Metric",
    "userDefinedLabel": "User Defined",
    "hyperLabel": "Hyper",
    "otherLabel": "其他",
    "remarkTips": "提示：终止服务后备注及tag将被清除",
    "optimizationObject": "优化目标",
    "targetDistribution": "优化目标分布",
    "parameterImportance": "参数重要性",
    "optimizationTitle": "参数和优化目标",
    "viewBigImage": "查看大图",
    "mustOptions": "必选",
    "customOptions": "自定义",
    "targetTips": "可以通过上述参数的柱状图来切换参数和优化目标的散点图",
    "reasonCode": {
      "1": "该参数含有非数字值",
      "2": "该参数和优化目标的有效条目≤2,无法计算重要性",
      "3": "重要性为NaN"
    },
    "explan": "说明"
  },
  "dataTraceback": {
    "details": "详情",
    "key": "KEY",
    "value": "VALUE",
    "dataTraceTips": "该数据涉及合并操作",
    "noDataFound": "暂无满足筛选条件的数据"
  },
  "trainingDashboard": {
    "trainingDashboardTitle": "训练看板",
    "calculationChart": "计算图",
    "dataMap": "数据图",
    "trainingScalar": "训练标量信息",
    "samplingData": "数据抽样",
    "imagesampleSwitch": "切换标签",
    "invalidId": "无效的作业",
    "summaryDirPath": "训练日志路径：",
    "loadingTip": "加载中",
    "waitLoading": "待加载",
    "switchTitle": "计算图和数据图模块不支持自动刷新",
    "integrityInfo": "summary数据可能存在损坏,请确认你要访问的数据可以正常显示。"
  },
  "scalar": {
    "titleText": "标量",
    "tagSelectTitle": "标签选择",
    "xAxisTitle": "水平轴",
    "smoothness": "平滑度",
    "step": "步骤",
    "relativeTime": "相对时间",
    "absoluteTime": "绝对时间",
    "fullScreen": "切换全屏",
    "stepBack": "分步回退",
    "openOrCloseSelection": "开启/关闭框选",
    "toggleYaxisScale": "切换Y轴比例",
    "charTipHeadName": "名称",
    "charTipTagName": "标签",
    "charTipHeadValue": "值",
    "charSmoothedValue": "平滑值",
    "comparison": "标量合成",
    "compareCancel": "取消合成",
    "open": "展开",
    "close": "折叠",
    "invalidData": "存在无效数据",
    "restore": "还原",
    "currentThreshold": "当前阈值",
    "deleteThreshold": "删除阈值",
    "setThreshold": "设置阈值",
    "currentTag": "当前标签",
    "filterCriteria": "筛选条件",
    "placeHolderThreshold": "请输入阈值",
    "or": "或",
    "and": "且",
    "greaterThan": "大于",
    "lessThan": "小于",
    "applyAllSelectTag": "应用到当前所选标签",
    "placeHolderNumber": "请输入数值",
    "noSpace": "请勿输入空格",
    "sameCompare": "不能有相同的比较运算符",
    "unreasonable": "逻辑不合理",
    "info": "提示",
    "isDelete": "是否删除当前阈值？",
    "noLog": "不存在0和负数的对数"
  },
  "images": {
    "titleText": "图像",
    "titleTip": "图像可视仅随机展示10张图片。",
    "imageErrorTip": "当前图片被更新，请等待数据加载完后再查看。",
    "tagSelectTitle": "标签选择",
    "open": "展开",
    "close": "折叠",
    "step": "步骤：",
    "setBright": "亮度调整",
    "setContrast": "对比度调整"
  },
  "histogram": {
    "titleText": "参数分布图",
    "xAxisTitle": "纵轴",
    "viewType": "视角",
    "centerValue": "中心值",
    "step": "步骤",
    "relativeTime": "相对时间",
    "absoluteTime": "绝对时间",
    "overlay": "正视",
    "offset": "俯视",
    "fullScreen": "切换全屏"
  },
  "dataMap": {
    "titleText": "数据图"
  },
  "tensors": {
    "mode": "模式",
    "tableMode": "表格模式",
    "chartMode": "图模式",
    "titleText": "张量",
    "dimension": "形状：",
    "tensorType": "数据类型：",
    "viewTypeTitle": "视图",
    "chartViewType": "表格",
    "histogramViewType": "直方图",
    "tensorDashboardLimitErrorMsg": "请求的数据超过上限10万，请点击进入张量页，尝试切片或查询其他维度。"
  },
  "graph": {
    "titleText": "计算图",
    "optimizeText": "优化计算图的可读性，降低计算图的复杂度，图中大部分的梯度计算逻辑和优化器计算逻辑将会被移除。",
    "downloadPic": "下载",
    "fitScreen": "适应屏幕",
    "nodeInfo": "节点信息",
    "legend": "图例",
    "nameSpace": "命名空间",
    "operatorNode": "算子节点",
    "virtualNode": "虚拟节点",
    "constantNode": "常量节点",
    "dynamicShapeNode": "动态Shape算子",
    "polymetric": "聚合节点",
    "dataFlowEdge": "数据流向边",
    "controllDepEdge": "控制依赖边",
    "name": "名称",
    "count": "子节点数",
    "type": "类型",
    "attr": "属性",
    "inputs": "输入",
    "outputs": "输出",
    "outputs_i": "Outputs_i",
    "controlDependencies": "控制边",
    "searchLoading": "正在定位节点，请稍候。定位速度与节点数量有关，节点数量多时，速度会变慢。",
    "queryLoading": "正在加载图，请稍候。",
    "fullScreen": "全屏",
    "partScreen": "退出全屏",
    "tooManyNodes": "节点太多，打开失败",
    "openFail": "节点展开失败，可能原因如下：1.您的机器性能不足以绘制该节点的子节点；2.该节点中直接子节点数量超过1500；3.直接子节点连线关系过于复杂。",
    "inputNodeName": "请输入节点名称",
    "guide": "用户指南",
    "guideTitle1": "1/3 主要功能介绍",
    "guideTitle2": "2/3 节点类型介绍",
    "guideTitle3": "3/3 边介绍",
    "guideContent11": "1. 在计算图展示区域可以查看计算图，可以通过滚轮对计算图放大和缩小，可以鼠标点击后拖拽。",
    "guideContent12": "2. 支持全屏展示计算图，或者将当前页面保存成SVG图片文件。",
    "guideContent13": "3. 在右边的功能区中，可以切换查看不同文件的计算图，或者在计算图中进行节点搜索。",
    "guideContent14": "4. 在节点信息的输入和输出中，选择并点击一个节点后，可以跳转到选择的节点。",
    "guideContent2": "计算图中有命名空间节点，算子节点，虚拟节点，聚合节点，常量节点等几种类型节点。其中 \"Default\" 表示正向传播，\"Gradients\" 表示反向传播。",
    "guideContent3": "计算图中存在数据边和控制边。其中数据边表示数据的输入，控制边表示节点之间执行依赖关系。",
    "next": "下一步",
    "finish": "完成",
    "dataTooLarge": "节点和边的数量过多，导致打开失败",
    "tooManyChain": "直接子节点的深度超过70，不支持展开",
    "sidebarTip": "选择要查看的计算图文件，当前最多支持展示10个文件的计算图。当超出10个文件时，部分文件将不展示。如果要查看其他未展示的文件，请将未展示的文件移动到其他训练日志目录中。"
  },
  "operator": {
    "currentCard": "当前卡片",
    "pie": "饼图",
    "bar": "柱状图",
    "allOperator": "全部",
    "classificationOperator": "分类",
    "card": "卡",
    "searchByType": "请输入算子类型搜索",
    "searchByName": "请输入算子名称搜索",
    "searchByCoreName": "请输入内核名称搜索",
    "searchByCoreFullName": "请输入算子全名搜索",
    "operatorInfo": "算子信息",
    "kernelInfo": "内核信息",
    "flops": "浮点运算次数",
    "flopsS": "每秒浮点运算次数",
    "flopsUtilization": "FLOPS利用率：",
    "flopsUtilizationTitle": "FLOPS/峰值FLOPS",
    "scopeLevelFlops": "Scope层级FLOPs(M)"
  },
  "profiling": {
    "rankID": "逻辑卡号",
    "singleHost": "单卡",
    "cluster": "集群",
    "titleText": "性能分析 - 单机",
    "profilingDashboard": "性能看板",
    "showAverage": "展示平均值",
    "iterationGapTime": "迭代间隙时间",
    "fpBpTime": "前向+反向时间",
    "fpTime": "前向时间",
    "tailTime": "迭代拖尾时间",
    "time": "时间",
    "operatorTimeConAnalysis": "算子耗时分析",
    "avgCost": "平均总耗时：",
    "getCost": "平均取数据耗时：",
    "pushCost": "平均推送数据耗时：",
    "iterationGap": "迭代间隙",
    "iterationTail": "迭代拖尾",
    "minddataTitle": "数据准备详情",
    "title": "迭代间隙流程分析",
    "dataQueue": "数据队列",
    "smartHelper": "小助手",
    "suggestions": "优化建议",
    "common-profiler_tutorial": {
      "desc": "如何使用Profiler进行性能分析",
      "anchor": ["desc"],
      "url": [
        "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_ascend.html"
      ],
      "gpuUrl": [
        "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_gpu.html"
      ]
    },
    "step_trace-proposer_type_label": {
      "desc": "迭代轨迹性能优化参考建议"
    },
    "step_trace-iter_interval": {
      "desc": "在开启图模式和数据集下沉模式后，如果平均迭代间隙时间大于{n1}ms，则数据处理至计算图执行之间的流程可能存在优化空间，可根据数据准备性能分析优化脚本。"
    },
    "common-proposer_type_label": {
      "desc": "性能分析与优化指导"
    },
    "minddata_pipeline-proposer_type_label": {
      "desc": "数据处理性能优化参考建议"
    },
    "minddata_pipeline-general": {
      "desc": "Pipeline中的算子{n1}可能存在性能瓶颈，请用户重点关注。"
    },
    "minddata_cpu_utilization": {
      "desc": "整机CPU利用率平均值为{n1}，可能影响数据处理性能，请用户关注。"
    },
    "minddata_pipeline-dataset_op": {
      "desc": "对于算子{n1}，用户可以尝试增大或减小num_parallel_workers参数。"
    },
    "minddata_pipeline-generator_op": {
      "desc": "对于算子{n1}，用户可以尝试增大或减小num_parallel_workers参数或优化训练脚本，如果性能没有得到优化，可以尝试替换为MindRecordDataset算子。"
    },
    "minddata_pipeline-map_op": {
      "desc": "对于算子{n1}，用户可以尝试增大或减小num_parallel_workers参数,如果使用的是Python的算子，可以尝试优化训练脚本。"
    },
    "minddata_pipeline-batch_op": {
      "desc": "对于算子{n1}，用户可以尝试增加prefetch_size大小。"
    },
    "minddata_device_queue_rate": {
      "empty_rate": "当前主机侧队列空队列占比为{empty_rate}%，阈值为{empty_warning_threshold}%，数据处理阶段可能存在优化空间，可根据数据处理性能优化参考建议调整脚本。",
      "empty_warning_threshold": "当前主机侧队列空队列占比为{empty_rate}%，阈值为{empty_warning_threshold}%，且芯片侧队列存在空队列，数据发送阶段可能存在优化空间，建议到MindSpore社区提交issue进行反馈。"
    },
    "minddata-proposer_type_label": {
      "desc": "数据准备性能分析"
    },
    "minddata_device_queue": {
      "desc": "主机侧队列为空比例{n1}/{n2}，非空比例{n3}/{n4}。"
    },
    "minddata_get_next_queue": {
      "desc": "芯片侧队列为空比例{n1}/{n2}，非空比例{n3}/{n4}。"
    },
    "device_queue_warning": {
      "desc": "可根据数据处理性能优化参考建议优化脚本。"
    },
    "millisecond": "ms",
    "curCard": "当前卡片",
    "stepTrace": "迭代轨迹",
    "mindData": "数据准备",
    "timeLine": "时间线",
    "rankOfOperator": "算子耗时统计排名",
    "stepTraceDetail": "迭代轨迹详情",
    "viewDetail": "查看详情",
    "stepNum": "耗时step数",
    "iterGapTimeLabel": "迭代间隙时长",
    "iterGapRateLabel": "迭代间隙占比",
    "fpBpTimeLabel": "前向+反向时长",
    "fpTimeLabel": "前向时长",
    "fpBpRateLabel": "前向+反向占比",
    "fpRateLabel": "前向占比",
    "tailTimeLabel": "迭代拖尾时长",
    "tailRateLabel": "迭代拖尾占比",
    "operatorDetail": "算子详情",
    "times": "次",
    "queueStep": "队列step分布图",
    "queueInfo": "迭代间隙",
    "pipeline": "数据处理",
    "pipelineTopTitle": "算子间队列平均使用率",
    "pipelineMiddleTitle": "算子间队列关系",
    "deviceQueueOp": "数据发送",
    "deviceQueueOpTip": "前向+反向",
    "deviceQueueOpFpTip": "前向",
    "getNext": "取数据算子",
    "connectorQuene": "主机队列",
    "getData": "数据获取",
    "opTotalTime": "算子执行总时间：",
    "streamNum": "执行流数量：",
    "opNum": "算子数目：",
    "opTimes": "算子执行总次数：",
    "features": "功能介绍：",
    "iterationInfo": "迭代轨迹展示的是每个step从上个迭代开始至该step结束的耗时信息，主体时间分为3部分：迭代间隙、前向反向、迭代拖尾。(该特性暂不支持异构训练场景)",
    "iterationGapInfo": "主要负责从数据队列中读取数据，如果该部分耗时较长，建议前往数据处理部分进一步分析；",
    "fpbpTitle": "前向反向",
    "fpbpInfo": "执行网络中的前向算子以及反向算子，承载了一个step主要的计算工作，如果该部分耗时较长，建议前往算子统计或时间线中进一步分析；",
    "iterativeTailingTitle": "迭代拖尾",
    "iterativeTailingInfo": "主要在多卡场景下执行参数聚合、参数更新操作，如果该部分耗时较长，建议查看AllReduce算子耗时以及并行情况。",
    "statistics": "统计信息：",
    "totalTime": "总耗时：",
    "totalSteps": "总step数：",
    "fpbpTimeRatio": "前向反向耗时占比：",
    "fpTimeRatio": "前向耗时占比：",
    "iterationGapTimeRatio": "迭代间隙耗时占比：",
    "iterativeTailingTimeRatio": "迭代拖尾耗时占比：",
    "dataProcess": "该图展示了数据处理阶段的流程，数据通过数据处理阶段存入主机队列，再通过数据传输阶段存入芯片侧的数据队列，最终由数据传输算子get_next发送给前向训练使用。",
    "dataProcessInfo": "综合分析该阶段的流程，通过判断主机队列和数据队列为空的情况就可以初步判断可能出现性能异常的阶段。",
    "analysisOne": "1、如果迭代间隙较长，并且芯片侧的数据队列部分batch为空，那么可能由于数据处理和数据传输阶段导致的性能异常，参考2，反之则定位数据传输算子get_next内部问题；",
    "analysisTwo": "2、如果通过1定位为数据处理、数据传输阶段异常，则查看主机队列情况，如果大概率为空，则可能为数据处理阶段导致异常，如果大概率不为空，则可能数据传输阶段异常；",
    "higherAnalysis": "注：可结合下方算子耗时进行高阶分析",
    "chipInfo": "芯片侧数据队列为空比例：",
    "hostIsEmpty": "主机侧队列为空比例：",
    "hostIsFull": "主机侧队列非空比例：",
    "operatorInfo": "{msg1}、{msg2}算子信息",
    "workersNum": "使用线程数",
    "queueDeepChartTitle": "{msg}队列深度折线图",
    "sampleInterval": "采样间隔",
    "queueTip1": "队列非空比例：",
    "queueTip2": "队列为空比例：",
    "totalCapacity": "总容量",
    "averageCapacity": "平均使用容量",
    "FPMessage": "前向起始算子：",
    "BPMessage": "反向终止算子：",
    "approximateTime": "总时长 ≈ ",
    "stepInputTip": "请输入step值（1~{max}的正整数，值为空时展示平均值）",
    "stepInput": "1~{max}",
    "stageTip": "请选择stage",
    "inputError": "输入参数异常，请输入一个1~{max}的正整数",
    "defaultTip": "默认展示平均值",
    "downloadTimeline": "下载",
    "timelineTips": {
      "title1": "时间线功能可以帮您对训练过程进行分析，它可以展示：",
      "content11": "- 算子分配到哪个设备 (AICPU/AICORE) 执行；",
      "content12": "- MindSpore对该网络的流切分策略；",
      "content13": "- 算子在Device上的执行序列和执行时长；",
      "content14": "- 算子ScopeName信息，例如算子Conv2D-op11的全名为：Default/network/lenet5/Conv2D-op11，该算子的第一层ScopeName为Default、第二层为network。",
      "title2": "如何查看时间线：",
      "content21": {
        "part1": "要查看时间线的详细信息，您可以点击 \"",
        "part2": "下载",
        "part3": "\" 按钮将带有时间线信息的文件保存到本地，再通过工具查看。"
      },
      "content22": "我们推荐您使用谷歌插件：chrome://tracing，或 Perfetto工具：https://ui.perfetto.dev/#!/viewer",
      "content23": {
        "part1": "选择一个工具，在浏览器地址栏输入地址并",
        "part2": "回车",
        "part3": "，进入页面点击按钮加载文件查看时间线（tracing工具点击左上角 \"",
        "part4": "Load",
        "part5": "\"，Perfetto工具点击左侧栏 \"",
        "part6": "Open trace file",
        "part7": "\"）。"
      },
      "title3": "如何使用时间线：",
      "content31": "您可以通过时间线信息分析流切分方法是否合理、迭代间隙和拖尾时间是否过长等；",
      "content32": "也可以具体定位到某个算子，查看分析它的执行时间。",
      "title4": "各种类型时间线的解释：",
      "content41": "Process AICORE Op：在AICORE上执行的算子的时间线。",
      "content42": "Step：训练迭代数。",
      "content43": "Scope Name：展示计算图中算子的层级结构。",
      "content44": "Stream #ID：在该stream上执行的算子。",
      "content45": "Process AICPU Op：在AICPU上运行的算子的时间线。",
      "content46": "Process Communication Op：包含通信算子执行的时间线。",
      "content47": "Process HOSTCPU Op：包含在HOSTCPU上执行的算子的时间线。",
      "content48": "Process Op Overlap Analyse：所有计算算子与通信算子合并后的时间线，可用于分析通信时间占比。",
      "content49": "Merged Computation Op：为所有计算（AICORE、AICPU、HOSTCPU）算子合并后的时间线。",
      "content410": "Merged Communication Op：为所有通信算子合并后的时间线。",
      "content411": "Pure Communication Op：纯通信时间（通信算子的执行时间去除与计算算子时间重叠部分后的时间线）。",
      "content412": "Free Time：空闲时间（既没有通信算子也没有计算算子在执行的时间线）。"
    },
    "pynativeTimelineTips": {
      "content11": "- 算子分配到哪个设备 (Ascend/HOSTCPU) 执行；",
      "content12": "- 算子在Device上的执行序列和执行时长。",
      "content31": "可以具体定位到某个算子，查看分析它的执行时间。",
      "content41": "Step：训练迭代数。",
      "content42": "Ascend Op：在Ascend上执行的算子。",
      "content43": "HOSTCPU Op：在HOSTCPU上执行的算子。"
    },
    "countUnit": "次",
    "unit": "ms/次",
    "gpuunit": "us/次",
    "chartTitle": "平均耗时排名",
    "userUtilization": "用户利用率",
    "sysUtilization": "系统利用率",
    "ioUtilization": "IO利用率",
    "idleUtilization": "空闲利用率",
    "avgUserUtilization": "平均用户利用率：",
    "avgSysUtilization": "平均系统利用率：",
    "avgIOUtilization": "平均IO利用率：",
    "avgIdleUtilization": "平均空闲利用率：",
    "avgWaitingProcess": "平均等待线程数：",
    "avgSwitchCount": "平均上下文切换次数：",
    "logicCores": "CPU逻辑核数：",
    "allOperators": "所有算子",
    "currentOperator": "当前算子",
    "cpuStepInputTip": "请输入step范围 （1~{max}的正整数，且起始step小于等于结束step）",
    "startStep": "起始step：",
    "endStep": "结束step：",
    "filterStep": "筛选",
    "resetStep": "重置",
    "cpuUtilization": "CPU利用率",
    "cpuStepTip": "Step的范围为1~{max}的正整数",
    "structuralCpuUtil": "整机CPU利用率",
    "processCpuUtil": "进程CPU利用率",
    "operatorCpuUtil": "算子CPU利用率",
    "utilizationTitle": "利用率（%）",
    "executiveOverview": "执行总览",
    "trainingPerformance": "训练性能",
    "resourceUtilization": "资源利用",
    "strategyPerception": "策略感知",
    "strategyReference": "详细教程请参考",
    "strategyTutorials": "策略感知教程",
    "strategyTutorialUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_of_cluster.html#策略感知",
    "rankSelector": "Stage 选择",
    "parallelStrategy": "并行方式",
    "hasStrategy": "含切分策略算子",
    "redistribution": "重排布算子",
    "gradientAggregate": "梯度合并算子",
    "recomputeGraph": "重计算图",
    "forwardGraph": "正向图",
    "backwardGraph": "反向图",
    "bipartiteExtractTips": "选择需要抽取通信算子的计算图",
    "resetTips": "点击将图调整至画布中央",
    "bipartiteExtractSelector": "图选择",
    "noneNodesTips": "暂无选中结点",
    "trainingPipeline": "流水线并行视图",
    "specialNodeCnt": "特殊算子统计",
    "nodeAttribute": "结点属性",
    "updateButton": "更新",
    "memory": {
      "memoryDetailLink": "内存使用情况",
      "overView": "内存分配概览",
      "currentCard": "当前卡片",
      "memoryAssign": "内存分配次数",
      "memoryRelease": "内存释放次数",
      "totalMemory": "总可用内存",
      "staticMenory": "静态内存",
      "memoryPeak": "内存使用峰值",
      "memoryGiBUnit": "GiB",
      "memoryMiBUnit": "MiB",
      "memoryKiBUnit": "KiB",
      "memoryByteUnit": "B",
      "usedMemory": "内存使用",
      "operatorMemoryAssign": "算子内存分配",
      "tensorName": "张量名称",
      "totalTensorMemoryAssign": "张量大小",
      "format": "格式",
      "tensorType": "张量类型",
      "dataType": "数据类型",
      "shapes": "形状",
      "chartXaxisUnit": "执行序号",
      "chartYaxisUnit": "大小（GiB）",
      "curOperaterId": "当前算子执行序",
      "curOperator": "当前算子",
      "curOperatorMemorySize": "当前算子占用内存",
      "curMemorySize": "当前占用的总内存",
      "memoryChanged": "内存变化",
      "lifeCycle": "生命周期",
      "fpStart": "前向",
      "bpEnd": "后向"
    },
    "isHeterogeneous": "暂不支持异构训练场景",
    "scopeNameNum": "展示算子ScopeName层数: ",
    "flopsScopeTipOne": "节点移动：通过拖动scope节点，将其移动到指定位置。",
    "flopsScopeTipTwo": "重置：将图形恢复到初始状态。",
    "flopsScopeTipThree": "选中：点击选中某一个scope节点，其关联的父子节点长期高亮，再次点击可取消高亮。",
    "operatorShapeStep": "算子耗时（按迭代）",
    "opeatorFeatures": "算子耗时（按迭代）展示的是不同类型的算子在指定step下的耗时，可以通过筛选框选择不同类型的算子进行展示。",
    "gpuOpeatorFeatures": "算子耗时详情（按迭代）分析组件对不同迭代中各类型算子的执行时间进行统计展示，可以快速了解训练各迭代中各类型算子的耗时与算子耗时按迭代展开的耗时详情。",
    "operatorSelectTitle": "筛选指定算子类型：",
    "timeConsume": "耗时",
    "operatorShapeDetail": "算子耗时详情（按迭代）",
    "operatorShapeTip": "算子耗时详情（按迭代）是展示指定类型的算子耗时及shape信息，默认展示3条记录， 算子shape信息可以点击平滑曲线图上对应点来显示。",
    "operatorFilterTitle": "筛选指定算子名称：",
    "operatorShapeTitle": "算子耗时详情",
    "notSupportTitle": "暂不支持动态shape网络",
    "expertNoOpAdvice": "专家系统：AI CPU算子不需要优化",
    "expertOpAdvice": "专家系统：AI CPU算子需要优化",
    "adviceDetails": [
      "1.修改模型代码，避免AI CPU算子操作。",
      "2.融合AI CPU算子，减少AI CPU和AI Core算子频繁切换。",
      "3.更改模型结构，例如，从INT64更改为FP16。",
      "4.优化耗时的AI CPU算子的性能。"
    ],
    "dataVersionTips": "profiler数据来源于MindSpore版本{ms_version}，MindInsight版本为{mi_version}，建议两者保持一致"
  },
  "profilingGPU": {
    "minddata_get_next_queue": {
      "desc": "数据队列为空比例{n1}/{n2}，非空比例{n3}/{n4}。"
    },
    "minddata_device_queue": {
      "desc": "主机侧队列为空比例{n1}/{n2}，非空比例{n3}/{n4}。"
    },
    "dataProcess": "该图展示了数据处理阶段的流程，数据通过数据处理阶段存入主机队列，再通过数据传输阶段存入数据队列，最终由数据传输算子get_next发送给前向训练使用。",
    "dataProcessInfo": "综合分析该阶段的流程，通过判断主机队列和数据队列为空的情况就可以初步判断可能出现性能异常的阶段。",
    "analysisOne": "1、如果迭代间隙较长，并且数据队列部分batch为空，那么可能由于数据处理和数据传输阶段导致的性能异常，参考2，反之则定位数据传输算子get_next内部问题；",
    "analysisTwo": "2、如果通过1定位为数据处理、数据传输阶段异常，则查看主机队列情况，如果大概率为空，则可能为数据处理阶段导致异常，如果大概率不为空，则可能数据传输阶段异常；",
    "chipInfo": "数据队列为空比例："
  },
  "profilingCluster": {
    "clusterStepView": "集群迭代轨迹概览",
    "clusterCommView": "集群通信概览",
    "clusterView": "集群概览",
    "parallelStrategyView": "并行策略视图",
    "mareyView": "Marey视图",
    "timeOverview": "时间总览视图",
    "namescope": "命名空间",
    "hiddenEdge": "隐藏边",
    "specialTypeOperator": "特殊类型算子：",
    "operator": "算子",
    "parameter": "参数",
    "fpbpTitle": "前向反向传播",
    "FLOPs": "FLOPs",
    "ratio": "比例",
    "p2pcomm": "点对点通信",
    "averageCondition": "一个Stage内的平均情况",
    "openBrush": "是否打开时间线刷选",
    "showHiddenEdges": "是否展示隐藏边",
    "collectiveCommunication": "集合通信",
    "sendOperator": "Send算子",
    "receiveOperator": "Receive算子",
    "parallelShardOperator": "含切分策略算子",
    "redistributionOperator": "重排布算子",
    "gradientAggOperator": "梯度合并算子",
    "memory": "内存",
    "nodelinkGraph": "节点链接图：",
    "totalcomm": "总通信花费",
    "commProportion": "通信时间/(通信时间+等待时间)",
    "matrixDescription": "矩阵：",
    "commTime": "通信时间",
    "traffic": "流量",
    "bandwidth": "带宽",
    "totalTrainingTime": "各设备总训练时间",
    "averageCommTime": "所有设备平均通信时间",
    "averageWaitingTime": "所有设备平均等待时间",
    "commWaitTime": "通信/等待时间（ms）",
    "totalTrainingTimeMs": "总训练时间（ms）",
    "rankID": "逻辑卡号",
    "host": "主机",
    "peakMem": "内存峰值",
    "capaCity": "最大可用内存",
    "maximumPeakRatio": "最大峰值占比",
    "unit": "KIB",
    "timeTitle": "时间（ms）",
    "stepChartTitle": "集群迭代轨迹",
    "commChartTitle": "集群通信",
    "memoryHeatMapTitle": "内存热力图分析",
    "flopsHeatMapTitle": "FLOPs热力图分析",
    "opName": "算子名称",
    "waitCost": "等待时长",
    "waitCostExplanation": "又名同步时间。卡间进行通信前，首先会进行同步，确保通信的两张卡同步完成，再进行通信。等待时间计算方式为统计所有Notify Wait算子总耗时并减去RDMA链路通信时间中的Notify Wait算子耗时。",
    "commCost": "通信时长",
    "commCostExplanation": "表示通信算子的通信耗时。如果通信耗时过长，有可能是某条链路有问题，可以通过链路带宽定位到具体的链路。通信时间计算方式为统计SDMA链路（server内通信）和RDMA链路（server间通信）的通信算子总耗时。",
    "commPerformance": "通信性能",
    "linkInfo": "链路信息",
    "linkInfoExplanation": "将该卡作为源卡或目的卡，显示链路信息。链路信息包括通信时间、通信量、带宽（通信量除以通信时间）、链路类型。",
    "commSize": "通信量",
    "linkType": "链路类型",
    "linkTypeExplanation": "链路类型包含SDMA链路（server内通信链路）和RDMA链路（server间通信链路）。",
    "linkRange": "链路关系",
    "bandWidth": "带宽",
    "startID": "源逻辑卡号",
    "endID": "目的逻辑卡号",
    "deviceId": "设备",
    "granuLarity": "粒度",
    "mainTipTitle": "功能说明：",
    "mainTipPartOne": "该功能以热力图的形式为用户提供集群场景下的内存使用概况，反映了集群内每张训练卡的内存峰值在训练卡最大可用内存中的占比。",
    "mainTipPartTwo": "内存峰值占比 = 内存峰值 / 训练卡最大可用内存值（内存峰值占比最大值为1）",
    "mainTipPartThree": "粒度：代表不同卡的内存峰值占比，在划分对应色值时的粗细程度。",
    "mainTipPartFour": "您也可以在界面上看到该次训练的集群概况，比如该次训练使用了几张训练卡。点击某个训练卡可以跳转到该张卡的内存详情页面。",
    "flopsTipPartOne": "该功能以热力图的形式为用户提供集群场景下的每个设备的FLOPs(浮点运算次数)数据。",
    "flopsTipPartTwo": "热力图反映了设备之间FLOPs的相对大小，每个设备对应的矩形块颜色代表当前设备FLOPs与所有设备中最大FLOPs的比值。",
    "flopsTipPartThree": "粒度：代表划分不同设备FLOPs数据对应色值的粗细程度。",
    "flopsTipPartFour": "您也可以在界面上看到该次训练的集群概况，比如该次训练使用了几个设备。点击某个设备可以跳转到该设备的算子详情页面。",
    "communicationAloneTime": "纯通信时间",
    "computationTime": "计算时间",
    "receiveAloneTime": "纯通信时间（仅包含Receive算子）",
    "stageTime": "stage时间",
    "collectiveCommunicationAlone": "纯通信时间（不包含Receive算子）",
    "iterationGapTimeTip": "主要负责从数据队列中读取数据，如果该部分耗时较长，建议前往数据处理部分进一步分析。",
    "fpBpTimeTip": "执行网络中的前向算子以及反向算子，承载了一个step主要的计算工作，如果该部分耗时较长，建议前往算子统计或时间线中进一步分析。",
    "tailTimeTip": "主要在多卡场景下执行参数聚合、参数更新操作，如果该部分耗时较长，建议查看AllReduce算子耗时以及并行情况。",
    "stageTimeTip": "各stage的耗时时长，该值为step时长减去该step内Receive通信算子的时长。通过该指标可以查看哪个stage的耗时最长。",
    "communicationAloneTimeTip": "只有通信算子执行、计算算子不执行的时间段。如果该部分耗时较长，说明通信耗时对性能影响较大。",
    "receiveAloneTimeTip": "只有点对点(Receive)通信算子执行、计算算子不执行的时间段。该时间段反映的是Pipeline并行各stage间的不同步情况。",
    "computationTimeTip": "AICore算子执行的时间总和，用于判断是否有慢的卡。时间越长，说明对应的卡执行速度越慢。",
    "collectiveCommunicationAloneTip": "只有除Receive通信算子外的其它通信算子执行、计算算子不执行的时间段。该时间段占的比例越大，需要考虑是否可以调整该stage内算子的切分策略，降低该时间段的耗时时长。",
    "flopsRateLabel": "当前设备FLOPs与所有设备中最大FLOPs的比值",
    "step_interval": "卡{rank_id}的迭代间隙为{val}ms，比各卡平均值高{percent}%，可能会影响整体性能，请到该卡单机页面重点关注数据准备阶段。",
    "flops_per_second": "卡{rank_id}的FLOPS(每秒浮点运算次数)值为{val}G/s，比各卡平均值低{percent}%，可能会影响整体性能，请重点关注该卡。",
    "cluster_link": "卡{src_rank}到卡{des_rank}的通信链路({link_type})带宽为{val}KB/s，比{link_type}链路平均值低{percent}%，可能会影响整体性能，请重点关注该链路。",
    "parallel_strategy": {
      "data_parallel_communication_rate": "从迭代拖尾时间和迭代轨迹中可得出该模型的通信耗时占比为{val}%，超过经验阈值{threshold}%，可尝试参考MindSpore分布式并行教程设置all_reduce_fusion_config参数，调整梯度 AllReduce 融合切分策略，降低通信耗时。",
      "model_parallel_communication_rate": "该模型的通信耗时占比为{val}%，超过经验阈值{threshold}%，可以尝试通过集群下面的“策略感知”页面分析并优化算子切分策略，降低通信耗时。",
      "pipeline_parallel_between_stage": "stage间通信(仅Receive算子)耗时占比为{val}%，超过经验阈值{threshold}%，可以尝试结合时间线重点分析Receive算子耗时长的原因。",
      "pipeline_parallel_within_stage": "stage{stage}内通信(除Receive算子外)耗时占比为{val}%，超过经验阈值{threshold}%，可以结合时间线分析通信算子耗时长的原因，尝试优化算子切分策略或其他方式降低通信算子耗时。",
      "pipeline_parallel_flops": "stage{stage}内卡的平均计算量为{val}M，比各stage平均值高{percent}%，可以尝试优化stage切分策略，使各stage计算量达到均衡。"
    },
    "step_interval_title": "数据准备性能分析",
    "flops_per_second_title": "卡计算性能分析",
    "cluster_link_title": "卡之间链路带宽性能分析",
    "parallel_strategy_title": "通信算子切分合理性分析",
    "clusterPerformanceTest": "如何使用Profiler进行集群性能调试",
    "clusterGuideUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_of_cluster.html",
    "clusterGPUGuideUrl": "https://www.mindspore.cn/mindinsight/docs/zh-CN/master/performance_profiling_of_cluster.html"
  },
  "components": {
    "summaryTitle": "训练选择",
    "tagSelectTitle": "标签选择",
    "selectAll": "全选",
    "tagFilterPlaceHolder": "请输入需要的标签(支持正则表达式)",
    "trainFilterPlaceHolder": "请输入需要的训练(支持正则表达式)",
    "open": "展开",
    "close": "折叠",
    "gridIncorrectDataError": "当前只支持最多二维数组的展示",
    "gridAccuracy": "保留小数位",
    "inCorrectInput": "无效输入",
    "gridTableNoData": "表格无数据",
    "value": "数值",
    "dimsFilterInputTitle": "维度选择",
    "dimsFilterInputTip": "维度输入值可以是具体的索引（和Python的索引含义一致，支持负号）或者冒号\":\"，其中冒号\":\"表示当前维度的所有值。Tensor列数不超过1000列，且总个数不超过10万。",
    "category": "分类",
    "scientificCounting": "科学计数",
    "accuracyTips": "如果数值展示不全，可以拖拽表头的分隔线查看",
    "startText": "起",
    "endText": "终",
    "optimize": "优化"
  },
  "debugger": {
    "debugger": "调试器",
    "nodeTypes": "节点类型",
    "nodeList": "节点列表",
    "watchList": "监测点列表",
    "watchPoint": "监测点",
    "continue": "继续",
    "pause": "暂停",
    "terminate": "结束",
    "selectCondition": "请选择条件",
    "inputStep": "请输入轮次值",
    "inputTip": "小于等于{total_step_num}的正整数",
    "curHitNode": "已发现的命中的监测点",
    "backstageStatus": "后台运行状态是",
    "view": "查看",
    "deleteWatchpointConfirm": "此操作将删除当前监测点，是否继续？",
    "clearWatchpointConfirm": "此操作将删除所有监测点，是否继续？",
    "ternimateConfirm": "此操作将结束当前状态，是否继续？",
    "createWP": "新建监测点",
    "successCreateWP": "新建监测点成功",
    "successDeleteWP": "删除监测点成功",
    "pendingTipsOff":  "等待数据加载中...",
    "pendingTips": "等待训练连接...",
    "nextNodeTip": "当前已经是最后一个节点",
    "previousNodeTip": "当前已经是第一个节点",
    "serviceError": "连接后端服务失败，请检查后刷新页面",
    "debuggerError": "连接后端服务失败，请检查调试器服务是否正确启动",
    "tolerance": "容忍度",
    "curValue": "当前值",
    "compareToPre": "上一步对比",
    "stepTip": "0表示训练初始状态",
    "stepTipOffline": "（起始轮次从1开始编号，注意Dump配置文件中的iteration字段的编号从0开始，两者概念不同,数值上差一。）",
    "toSummeryList": "返回训练列表",
    "clientIp": "训练端地址",
    "deviceId": "卡号",
    "currentStep": "当前轮次",
    "step": "轮次",
    "currentNode": "当前节点",
    "previousNode": "上一个节点",
    "nextNode": "下一个节点",
    "tensorMsg": "张量信息",
    "dType": "类型",
    "shape": "形状",
    "value": "数值",
    "largeDataTip": "请求的数据超过上限10万，请尝试切片或查询其他维度。",
    "continueTo": "运行到该节点",
    "inf": "INF：",
    "negativeInf": "-INF：",
    "max": "最大值：",
    "min": "最小值：",
    "mean": "平均值：",
    "nan": "NAN：",
    "zero": "零：",
    "positiveNum": "正数：",
    "negativeNum": "负数：",
    "true": "TRUE：",
    "false": "FALSE：",
    "all": "全部",
    "tensorTip": "张量",
    "recheck": "重新检查（只针对目前有张量值的监测点）",
    "clearWatchpoint": "清空监测点",
    "nodeType": {
      "all": "全部节点",
      "weight": "权重节点",
      "gradient": "梯度节点",
      "activation": "激活节点"
    },
    "recheckSuccess": "重新检查成功",
    "curStatisticsLabel": "当前轮次：",
    "preStatisticsLabel": "上一个轮次：",
    "diffStatisticsLabel": "对比结果：",
    "graphName": "图名称",
    "tensorDiagram": "张量关系图",
    "selectDetail": "单击张量选中，双击张量查看详情",
    "operator": "算子",
    "optimizationOrientation": "调优向导",
    "tuningAdvice": "调优建议",
    "setValue": "设置值",
    "actualValue": "实际值",
    "noWatchPoint": "没有命中的监测点",
    "tensorTuningRule": {
      "operator_real_data_validation": "真实数据单算子验证",
      "loss_overflow": "检查loss值溢出（NAN，INF）",
      "weight_condition_collection": "检查权重",
      "weight_initialization": "检查权重初始值",
      "weight_overflow": "检查权重溢出",
      "weight_too_large": "检查过大权重",
      "weight_too_small": "检查过小权重",
      "weight_not_changed": "检查未变化权重",
      "weight_change_too_large": "检查权重变化过大",
      "weight_change_too_small": "检查权重变化过小",
      "gradient_condition_collection": "检查梯度",
      "gradient_vanishing": "检查梯度消失",
      "gradient_too_large": "检查梯度过大",
      "gradient_exploding": "检查梯度爆炸",
      "activation_condition_collection": "检查激活值",
      "activation_range": "检查激活值范围",
      "tensor_condition_collection": "检查张量",
      "tensor_overflow": "检查张量溢出",
      "operator_overflow": "检查计算过程溢出",
      "tensor_initialization": "检查张量初始值",
      "tensor_too_large": "检查过大张量",
      "tensor_too_small": "检查过小张量",
      "tensor_range": "检查张量值范围",
      "tensor_all_zero": "检查张量是否全为0",
      "tensor_change_too_large": "检查张量变化过大",
      "tensor_change_too_small": "检查张量变化过小",
      "tensor_not_changed": "检查未变化张量",
      "zero_percentage_ge": "0值比例>=",
      "abs_mean_gt": "绝对值的平均值>",
      "abs_mean_lt": "绝对值的平均值<",
      "range_start_inclusive": "范围下界（含）",
      "range_end_inclusive": "范围上界（含）",
      "range_percentage_lt": "在范围中的值所占百分比<",
      "range_percentage_gt": "在范围中的值所占百分比>",
      "rtol": "相对容忍度",
      "abs_mean_update_ratio_gt": "平均变化比例值>",
      "abs_mean_update_ratio_lt": "平均变化比例值<",
      "param": "阈值",
      "max_min_lt": "MAX-MIN <",
      "max_min_gt": "MAX-MIN >"
    },
    "tensorTuningAdvice": {
      "operator_real_data_validation": [
        "真实数据单算子验证",
        "算子可能存在问题。您可以参考以下思路进行检查：",
        ["1.请检查算子实现是否存在错误。"]
      ],
      "loss_overflow": [
        "检查loss值溢出（NAN，INF）",
        "loss存在溢出现象。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.检查网络中的张量是否存在溢出，找到第一个溢出的张量后进行进一步分析"]
      ],
      "weight_initialization": [
        "检查权重初始值",
        "权重初始值可能存在问题。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查脚本中的权重初始化算法是否存在问题"]
      ],
      "weight_overflow": [
        "检查权重溢出",
        "权重值存在溢出现象。这可能是一个异常现象。您可以参考以下思路进行检查：",
        [
          "1.请检查学习率是否过大",
          "2.请检查梯度是否过大",
          "3.请检查优化器算法是否正确"
        ]
      ],
      "weight_too_large": [
        "检查过大权重",
        "权重值大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：",
        [
          "1.请检查学习率是否正确",
          "2.请检查梯度是否正确",
          "3.请检查优化器算法是否正确",
          "4.请检查更新前的权重是否过大"
        ]
      ],
      "weight_too_small": [
        "检查过小权重",
        "权重值小于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：",
        [
          "1.请检查学习率是否正确",
          "2.请检查梯度是否正确",
          "3.请检查优化器算法是否正确",
          "4.请检查更新前的权重是否过小"
        ]
      ],
      "gradient_vanishing": [
        "检查梯度消失",
        "存在梯度消失现象。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "gradient_too_large": [
        "检查梯度过大",
        "梯度值大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "gradient_exploding": [
        "检查梯度爆炸",
        "梯度值溢出。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "activation_range": [
        "检查激活值范围",
        "激活值范围不在设置的范围中。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "tensor_overflow": [
        "检查张量溢出",
        "张量溢出。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "operator_overflow": [
        "检查计算过程溢出",
        "算子计算过程中发生了溢出。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "tensor_too_large": [
        "检查过大张量",
        "张量大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "tensor_too_small": [
        "检查过小张量",
        "张量小于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "tensor_range": [
        "检查张量值范围",
        "张量值范围不在设置的范围中。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "tensor_all_zero": [
        "检查张量是否全为0",
        "张量中有太多0。这可能是一个异常现象。您可以参考以下思路进行检查：",
        ["1.请检查算子的输入张量是否正常"]
      ],
      "weight_not_changed": [
        "检查未变化权重",
        "权重未更新。这可能是一个异常现象。您可以参考以下思路进行检查：",
        [
          "1.请检查学习率是否过小",
          "2.请检查梯度是否过小",
          "3.请检查优化器算法是否正确",
          "4.请检查是否固定了此权重"
        ]
      ],
      "weight_change_too_large": [
        "检查权重变化过大",
        "权重变化大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：",
        [
          "1.请检查学习率是否正确",
          "2.请检查梯度是否正确",
          "3.请检查优化器算法是否正确"
        ]
      ],
      "weight_change_too_small": [
        "检查权重变化过小",
        "权重变化小于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：",
        [
          "1.请检查学习率是否正确",
          "2.请检查梯度是否正确",
          "3.请检查优化器算法是否正确"
        ]
      ]
    },
    "noAdvice": "没有可用的建议",
    "curStep": "显示当前轮次",
    "preStep": "显示上一轮次",
    "compareResult": "显示对比结果",
    "recommendTip": "是否检查常见异常现象?",
    "recommendDetail": "检查张量全为零、梯度消失、权重更新过大等常见异常现象。",
    "use": "使用",
    "notUse": "不使用",
    "outdateTip": "监测点列表或者当前轮次发生修改，结果可能过时，请重新检查或执行后续轮次训练。",
    "versionConflictTip": "MindSpore和MindInsight版本不匹配，MindSpore版本：{msv}；MindInsight版本：{miv}；",
    "checkTips": {
      "nan": "张量中含有NaN，",
      "inf": "张量中含有+/-INF，",
      "no_prev_tensor": "找不到上一个迭代的值，",
      "out_of_mem": "张量大小超出内存限制，",
      "no_history": "图执行历史文件缺失，",
      "cannotCheck": "无法检查。"
    },
    "stateTips": {
      "waiting": "就绪",
      "running": "运行中",
      "sending": "等待命令执行",
      "pending": "等待后台连接"
    },
    "stateMsg": {
      "running": "训练运行中",
      "sending": "等待命令执行，请稍候"
    },
    "paramErrorMsg": {
      "errorType": "请输入数值",
      "percentError": "百分数值不能小于0或大于100",
      "rangeError": "范围下界值不能大于上界值",
      "nonnegative": "此参数值不能小于0",
      "allPositive": "此参数值必须大于0",
      "watchOverflow": "训练开始前需开启异步全量溢出监测功能"
    },
    "paramValueTip": "设置值为：{value}",
    "logicCard": "逻辑卡",
    "inpStepTip": "可输入当前轮次：0~{total_step_num}",
    "stackList": "堆栈列表",
    "searchConditions": "搜索条件",
    "nodeName": "节点名称",
    "stackInfo": "堆栈信息",
    "inputStack": "请输入堆栈信息",
    "search": "搜索",
    "downloadTip": "文件正在加载，大小约为{fileSize}，请稍候",
    "noOfflineGraphData": "计算图加载失败，这可能是由于summary目录下离线dump数据中的graphs文件缺失或损坏，请尝试重新dump数据。",
    "largeDataLoading": "张量数据较大，正在加载中...",
    "overSize": "请求的张量过大，数值总大小超过1GiB,无法显示。",
    "noOfflineGraphName": "图名称缺失。",
    "state": "状态",
    "stateInfo": {
      "waiting": "就绪：等待用户操作。",
      "running": "运行中：正在执行训练或检查监测点。若模型较大或检测数据较多，会需要更多时间，请耐心等候。",
      "sending": "等待命令执行：前一个命令尚未完成，新命令正在等待前一条命令执行完毕。",
      "pending": "等待后台连接：等待新训练连接或正在初始化调试环境。"
    },
    "back": "回退",
    "graphExecutionHistory": "图执行历史",
    "graphExecutionStep": "图执行轮次",
    "hasData": "是否有数据",
    "forward": "前进",
    "yes": "是",
    "no": "否",
    "hasDataTip": "表示该轮次有张量数据被保存",
    "currentStepTip": "表示当前图执行轮次",
    "noDistinguish": "不区分",
    "newGraphName": "收到新图：{graphNames}。（注意：新图上暂无任何监测点，如有需要，请自行设置。）",
    "noExexutionGraph": "execution_order目录下的图执行历史文件缺失,无法查看图执行历史。",
    "graphExecutionHistoryTip": "图执行历史文件：保存该图执行的历史轮次信息",
    "noExecutionHistoryFile": "图执行历史文件缺失",
    "stackTip": "有部分算子由框架生成，这些算子可能关联到框架本身的代码，或者没有代码可以关联，均为正常现象。这些算子例如TupleGetItem、Depend、UpdateState等。",
    "createWpTip": {
      "explain": "监测点用于对一组节点设置检测规则。",
      "example": "例如，创建“检查过大张量”监测点，设置检查参数为max > 100，执行检查后即可找出“最大值大于100”的张量。"
    },
    "graphNameExplain": "与节点列表中的图名称不同，此处只展示训练中执行过的图的名称列表。",
    "graphExecutionCountTip": "图执行轮次：该图执行的轮次号\n轮次计算公式：\nround_id = sink_num * sink_size + loop_id\nsink_num：当前所有图累计下发次数\nsink_size：当前图每次下发执行的循环次数\nloop_id：循环的序号\n如：假设一次训练中，目前已经下发了4次图，在新一轮的epoch中图0需要执行6个迭代，分2次下发，每次下发执行3次。\n则该场景下，图0会产生6个轮次号，其中sink_size = 3。\n轮次号分别为：\n12 = 4 * 3 + 0\n13 = 4 * 3 + 1\n14 = 4 * 3 + 2\n15 = 5 * 3 + 0\n16 = 5 * 3 + 1\n17 = 5 * 3 + 2",
    "nodeIsLargeTip": "该训练保存的图节点数据超过{maxGraphNodeSize}，请减少图文件数量重新进入或使用离线调试器API进行数据分析。",
    "dataVersionTips": "dump数据来源于MindSpore版本{ms_version}，MindInsight版本为{mi_version}，建议两者保持一致"
  },
  "explain": {
    "explain": "模型解释",
    "explainSummary": "解释列表",
    "explainSummaryCurrentFolder": "解释日志根路径：",
    "summaryPath": "解释日志路径",
    "title": "显著图可视化",
    "conterfactualInterpretation": "反事实解释",
    "explainMethod": "解释方法",
    "viewScore": "查看评分",
    "fetch": "筛选",
    "minConfidence": "概率阈值",
    "confidenceRange": "概率（区间）",
    "imgSort": "图片排序",
    "default": "默认",
    "byProbability": "概率值降序",
    "byUncertainty": "不确定性值降序",
    "uncertainty": "不确定性",
    "superposeImg": "叠加于原图",
    "originalPicture": "原始图片",
    "forecastTag": "预测标签",
    "forecastTagPosibility": "预测标签 (概率)",
    "tag": "标签",
    "filterImg": "图片筛选",
    "tagTip": "查询包含任一筛选标签的图片数据（仅显示有数据的标签）",
    "typeTip": "查询包含任一预测类型的图片数据",
    "labelTip": "仅显示有数据的标签",
    "confidence": "概率",
    "forecastTagTip": "当推理图片带有正确标签时，标签行会显示下列三种旗标",
    "TP": "TP，代表True Positive，被判定为正样本，事实上也是正样本；",
    "FN": "FN，代表False Negative，被判定为负样本，但事实上是正样本；",
    "FP": "FP，代表False Positive，被判定为正样本，但事实上是负样本；",
    "coverfactualInterpretation": "遮掩反事实解释",
    "viewExplanation": "查看解释",
    "maskingProcess": "逐层遮掩过程",
    "mainTipTitle": "功能说明:",
    "mainTipPartOne": "本功能对模型分类的依据进行可视化。选定待解释模型、图片和标签后，解释方法计算得到原始图像中每个像素对选定标签的贡献度，以类似热力图的显著图进行可视。显著图颜色越亮，表示对应区域对于模型预测选定标签的贡献越多；颜色越暗，该区域对选定标签的贡献越小。",
    "mainTipPartTwo": "显著图可以帮助我们了解深度神经网络推理时，和指定标签有关系的特征。当模型推理依据和期望不同时，可以参考显著图对模型进行调试，让模型依据合理的特征进行推理。",
    "mainTipPartThree": "主流生成显著图的解释方法可以参考论文3.2节Local Methods:",
    "mainTipPartFour": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9050829",
    "noExplainer": "请选择解释方法",
    "minConfidenceTip": "预测标签的概率阈值，当一个标签的输出概率大于阈值时就会被记为预测标签。",
    "noData": "数据正在加载，请稍后刷新页面",
    "predictionType": "预测类型",
    "disableSaliencyMapTip": "无显著图可视日志，无法查看",
    "disableHOCTip": "无反事实解释日志，无法查看",
    "hocMinConfidenceTip": "对预测概率大于预测阈值的标签进行显示解释",
    "imageList": "图片列表",
    "dropEmptySwitch": "隐藏",
    "dropEmptySwitchTip": "隐藏没有解释结果的数据",
    "hocTitleTip": "逐步收缩显示范围，以寻找出预测概率大于阈值的最小显示区域"
  },
  "metric": {
    "scoreSystem": "评分体系",
    "comprehensive": "综合评估",
    "classify": "分类评估",
    "singleMethod": "单方法多指标",
    "multiMethod": "单指标多方法",
    "interpretation": "解释方法",
    "measurement": "度量方法",
    "disableMetricTip": "该度量方法的结果与标签无关，不支持查看分类评估结果",
    "seeInterpretation": "您正在查看解释方法 ",
    "seeMeasurement": "您正在查看度量方法 ",
    "showGrade": " 在不同标签类型上的分数",
    "evaluationScore": "评价分数",
    "weightAllocatgion": "权重配置",
    "compositeScore": "综合得分",
    "metric": "评分维度",
    "inputWeightScore": "请输入权重分数(0-1)",
    "weightSumNotNull": "权重总和不能为空",
    "weightError": "权重计算出错",
    "weightSum": "权重总和必须为1",
    "radarChart": "解释方法对比雷达图",
    "comprehensiveTooltip": "综合评估页面支持对不同评分维度配置权重，用于计算解释方法的综合评分，以便对解释方法进行排序筛选。解释方法在不同评分维度上各有优势，可以参考各个维度的介绍，根据实际需求对权重进行调整，有侧重地评估解释方法。本功能默认对各个维度设置相同的权重。",
    "classifyTooltip": "分类评估将数据集按照标签进行分组，在不同标签的数据上分别度量解释方法。",
    "scoreSystemtooltipOne": "功能说明：",
    "scoreSystemtooltiptwo": "评分体系提供多个维度对解释方法进行评分。",
    "scoreSystemtooltipthree": "评分维度：",
    "scoreSystemtooltipfour": "  -Faithfulness: 该维度评估解释结果对模型的忠实度，即显著图是否正确反映模型分类依据。给定显著图，按照一定规则（如从亮到暗）对原始图像中的相应位置进行修改，将修改后的图像传给模型依次进行推理，如果显著图越亮的区域对选定标签影响越大，则解释结果忠实度越高；反之，解释结果可能无法反映模型的推理依据。当前有三种主流方案度量解释的faithfulness：Naive Faithfulness，Insertion AUC，Deletion AUC。",
    "scoreSystemtooltipfive": "  -Localization: 该维度借助数据集的bounding box对显著图高亮区域的定位准确性进行评估，选定一组目标和相关标签，如果解释方法准确高亮了图像中和选定标签相关的区域，高亮区域和选定目标的bounding box应当有较大重合度。Localization通过评估高亮区域和bounding box的重合度，对解释结果的定位能力进行评估。",
    "scoreSystemtooltipsix": "-ClassSensitivity：该维度通过比较置信度最大和最小标签的显著图相关性，对显著图的标签敏感度进行评估，模型对不同标签的推理依据应该有区别，合理的解释方法应该为不同的标签输出不同的显著图。ClassSensitivity通过计算最大和最小置信度标签的显著图的相关系数，评估解释结果的标签敏感度。",
    "scoreSystemtooltipseven": "-Robustness: 该维度对解释方法的鲁棒性进行评估，即在原始图像中加入少量噪声时，显著图的变化程度。当原始图像加入少量噪声但不影响推理结果的情况下，如果显著图几乎没有变化，则解释方法的鲁棒性越强，解释结果越稳定。",
    "errorTipTitle": "异常场景：",
    "errorTipText": "出现NaN时，请查看运行日志。"
  },
  "lossAnalysis": {
    "titleText": "损失函数多维分析",
    "isopleth": "等值线图",
    "reliefMap": "地形图",
    "threeDiagram": "3D图",
    "mapColorSelection": "颜色选择",
    "pathCurve": "轨迹曲线",
    "lineColor": "颜色",
    "lineWidth": "宽度",
    "setLineNum": "等高线条数设置",
    "playTrackAnimation": "播放轨迹动画",
    "restore": "还原",
    "downloadPic": "下载",
    "fullScreen": "全屏",
    "fitScreen": "适应屏幕",
    "stepSelection": "步骤选择",
    "intervalRange": "请选择区间范围",
    "visualSettings": "视觉显示设置",
    "surfaceTransparency": "表面透明度",
    "lightIntensity": "光照强度",
    "illuminationPoint": "光照点位置",
    "alpha": "alpha",
    "beta": "beta",
    "distance": "距离",
    "cameraPosition": "相机位置",
    "cameraRotationAngle": "相机旋转角度",
    "basicTrainingInfo": "训练基础信息",
    "network": "网络",
    "optimizer": "优化器",
    "learning_rate": "学习率",
    "decomposition": "降维方式",
    "ratio": "采样点分辨率",
    "unit": "单位",
    "selectedInterval": "已选区间",
    "step_per_epoch": "每个epoch中的迭代",
    "lossValue": "损失值",
    "trainingStep": "训练\n步数",
    "dataSelectTip": "请选择右侧需要对比的图形",
    "dataToolTip": "本功能右侧折线图为实际训练损失值曲线，页面左侧大图为训练损失曲面的近似呈现，因数据随机性等原因，具体数值与实际训练损失值不一定完全一致",
    "basicTrainingTip": "如果部分基础训练信息缺失，请检查是否正确收集溯源数据。",
    "graph3DOperateTip": "按住鼠标左键，可旋转3D图，按住鼠标中键，可平移3D图"
  },
  "lossCompare": {
    "titleText": "损失函数图形对比",
    "visualiation": "可视化",
    "compareMode": "对比方式",
    "tiled": "平铺",
    "superimposed": "叠加",
    "colorMatching": "配色方案选择",
    "trainInfo": "训练信息",
    "evaluateInfo": "评估指标",
    "conpoint": "模型收敛点",
    "accuracy": "准确率：",
    "high": "高",
    "low": "低",
    "diagram3DSVLimitTip": "最多支持两个图形对比，请先取消勾选再选择",
    "more": "查看更多",
    "trainingTip": "如果部分基础训练信息或评估指标缺失，请检查是否正确收集溯源数据。"
  },
  "operatorPrecision": {
    "title": "降精度算子分析",
    "placeTitle": "请输入需要搜索的算子名称",
    "searchBtn": "搜索",
    "download": "导出",
    "view": "查看",
    "opName": "算子名称",
    "fullName": "算子完整节点名称",
    "opType": "算子类型",
    "precisionFlag": "升/降精度标识",
    "isReduceOp": "是否为降精度算子",
    "input": "输入",
    "output": "输出",
    "yes": "是",
    "no": "否"
  },
  "error": {
    "50540000": "系统错误",
    "50540001": "参数类型错误，请检查请求参数类型是否都符合要求",
    "50540002": "参数值错误，请检查请求参数值是否都符合要求",
    "50540003": "参数缺失必需的参数，请检查请求参数是否必填项都满足",
    "50545001": "API 路由资源不存在",
    "50545002": "请求API的HTTP方法错误",
    "50545005": "训练作业不存在",
    "50545007": "Summary数据正在被加载，请等待Summary数据加载结束",
    "50545009": "查询的节点不在图中，请刷新",
    "5054500A": "训练作业ID进行URL解码失败",
    "5054500C": "计算图不存在，请刷新",
    "5054500D": "图片数据不存在，请刷新",
    "5054500E": "标量数据不存在，请刷新",
    "5054500F": "参数分布图数据不存在，请刷新",
    "50545010": "请求的数据不在缓存中，请刷新",
    "50545017": "请在训练脚本中收集Summary，保存损失函数图形数据",
    "50542082": "模型名称缺失",
    "50542085": "模型名称不合法",
    "50542215": "查询参数错误",
    "50542216": "Summary日志文件未找到",
    "50542217": "Summary日志路径错误",
    "50542218": "筛选参数错误",
    "50545012": "张量数据不存在，请刷新。",
    "50545013": "请求的数据超过上限10万，请尝试切片或查询其他维度。",
    "50545014": "查询的张量数据已被新数据替换，请刷新。",
    "50545016": "请求的张量过大，数值总个数超过1000万，无法显示。",
    "50546083": "性能数据目录不存在",
    "50546084": "Profiler作业不存在",
    "50546189": "不支持展示该并行策略模式，仅支持数据并行，模型并行，Pipeline并行",
    "5054618A": "提供的并行策略数据存在异常数据",
    "5054618B": "并行策略数据不存在, 请检查Profiler目录中是否存在parallel_strategy_<rank_id>.json文件",
    "5054618C": "解析并行策略数据文件出现错误.",
    "50548001": "昇腾AI处理器信息查询超时",
    "5054B080": "参数类型错误，请检查输入参数类型",
    "5054B081": "参数值错误，请检查输入参数",
    "5054B180": "监测点创建失败，请暂停训练后重试",
    "5054B181": "监测点更新失败，请暂停训练后重试",
    "5054B182": "监测点删除失败，请暂停训练后重试",
    "5054B183": "后台训练运行中，请稍后重试",
    "5054B184": "操作过快，后台服务已暂停。",
    "5054B189": "请勿重复设置。",
    "5054B083": "监测点创建失败，请勿使用已失效规则。",
    "5054B202": "未找到调试器离线服务器模块",
    "5054B203": "未找到Ascend toolkit包，请安装并正确设置`ASCEND_TOOLKIT_PATH`环境变量。",
    "5054B283": "已有张量正在下载队列，请稍后再试",
    "5054B286": "Summary 目录下dump数据的.dump_metadata/data_dump.json文件缺失或已损坏，请检查该目录下的dump数据完整性。",
    "5054000C": "内存不足，请在释放内存后重试。"
  }
}
