@@ -123,6 +151,7 @@ export default {
data() {
return {
isShowInp: false,
+ isLanguage: true,
timeReloadValue: this.$store.state.timeReloadValue,
newReloadValue: this.$store.state.timeReloadValue,
isShowHardwareInp: false,
@@ -142,6 +171,16 @@ export default {
},
set(val) {},
},
+ isChinese() {
+ let isChinese = false;
+ if (
+ localStorage.getItem('milang') &&
+ localStorage.getItem('milang') === 'zh-cn'
+ ) {
+ isChinese = true;
+ }
+ return isChinese;
+ },
// set and get isHardwareTimeReload status
isHardwareTimeReload: {
get() {
@@ -258,6 +297,10 @@ export default {
return this.$route.path;
}
},
+ changeLanguage(lan) {
+ localStorage.setItem('milang', lan);
+ window.location.reload();
+ },
},
};
@@ -271,6 +314,19 @@ export default {
display: flex;
color: $headerColor;
flex-shrink: 0;
+ .md-header-language {
+ width: 100px;
+ line-height: 64px;
+ .spanLine {
+ margin: 0 5px;
+ }
+ .spanLanguage {
+ cursor: pointer;
+ }
+ .active {
+ color: #00a5a7;
+ }
+ }
// logo style
.cl-header-logo {
width: 161px;
@@ -332,7 +388,7 @@ export default {
.cl-header-nav {
margin-left: 50px;
- flex: 1.5;
+ flex: 2.2;
.el-menu {
border-bottom: none;
@@ -341,6 +397,10 @@ export default {
font-size: 16px;
color: #fff;
padding-top: 4px;
+ max-width: 20%;
+ overflow: hidden;
+ white-space: nowrap;
+ text-overflow: ellipsis;
}
.el-menu--horizontal > .el-menu-item.is-active {
color: #00a5a7 !important;
diff --git a/mindinsight/ui/src/locales/en-us.json b/mindinsight/ui/src/locales/en-us.json
new file mode 100644
index 0000000000000000000000000000000000000000..13c5df4e9fd52d8fc0d814fa19f834261dd4492c
--- /dev/null
+++ b/mindinsight/ui/src/locales/en-us.json
@@ -0,0 +1,470 @@
+{
+ "public": {
+ "netWorkError": "Network or backend service error. Please check.",
+ "browserWarning": "Your browser may cause some functions to become invalid or unavailable. You are advised to use Chrome 65 or later.",
+ "timeout": "Timeout. Try again.",
+ "noData": "No data",
+ "reset": "Reset",
+ "tagFilterPlaceHolder": "Enter tag (regular expression supported)",
+ "dataError": "The obtained data is abnormal.",
+ "regIllegal": "Enter a correct search criterion.",
+ "stayTuned": "Coming soon...",
+ "select": "Select",
+ "search": "Search",
+ "enter": "Enter",
+ "remark": "Remarks",
+ "zhLanguage": "中",
+ "enLanguage": "EN",
+ "sure": "OK",
+ "clear": "Clear",
+ "cancel": "Cancel",
+ "selectAll": "All",
+ "deselectAll": "Clear",
+ "dataLoading": "Loading data..."
+ },
+ "symbols": {
+ "leftbracket": "(",
+ "rightbracket": ")",
+ "point": "·",
+ "slashes": "/"
+ },
+ "header": {
+ "refreshData": "Refresh Data",
+ "refreshingData": "Refreshing data...",
+ "timeReload": "Auto Refresh",
+ "timeSecond": "Seconds",
+ "timeReloadScope": "The automatic refresh interval ranges from 3 to 300 seconds."
+ },
+ "summaryManage": {
+ "summaryList": "Summary List",
+ "currentFolder": "Current folder:",
+ "sorting": "No.",
+ "summaryPath": "Summary Path",
+ "createTime": "Creation Time",
+ "updateTime": "Update Time",
+ "operation": "Operation",
+ "viewDashboard": "Training Dashboard",
+ "viewProfiler": "Profiling",
+ "modelTraceback": "Model Lineage",
+ "dataTraceback": "Dataset Lineage",
+ "comparePlate": "Comparison Dashboard",
+ "disableProfilerTip": "Failed to view profiling because no profiler log is available.",
+ "hardwareVisual": "Hardware Resources",
+ "paramDetails": "Parameter Details",
+ "trainingParamDetails": "Training Parameter Details(Directory)"
+ },
+ "modelTraceback": {
+ "summaryPath": "Summary Path",
+ "trainSetPath": "Training Set Path",
+ "testSetPath": "Test Set Path",
+ "trainingSampleNum": "Training Samples",
+ "testSampleNum": "Testing Samples",
+ "showAllData": "Display All Data",
+ "network": "Network",
+ "optimizer": "Optimizer",
+ "lossFunc": "Loss Function",
+ "learningRate": "Learning Rate",
+ "modelSize": "Model Size",
+ "dataProcess": "Data Processing",
+ "noDataFound": "No data found.",
+ "click": "Click",
+ "showAllDataBtn": "Display All Data",
+ "viewAllData": "to view all data.",
+ "userDefined": "User-defined Data",
+ "metric": "Metrics",
+ "deviceNum": "Devices",
+ "mixedItemMessage": "This parameter contains multiple types of data and cannot be filtered.",
+ "displayColumn": "Displayed columns",
+ "hide": "Hide Record",
+ "unhide": "Unhide Record",
+ "totalHide": "Hidden records on this page: {n}",
+ "mustExist": "Mandatory",
+ "remarkValidation": "Remarks can contain 1 to 128 characters, including letters, digits, underscores (_), hyphens (-), and periods (.).",
+ "changeSuccess": "Modified successfully.",
+ "metricLabel": "Metric",
+ "userDefinedLabel": "User Defined",
+ "hyperLabel": "Hyper",
+ "otherLabel": "Other",
+ "remarkTips": "Note: Once the service is terminated, remarks and tags will be cleared."
+ },
+ "dataTraceback": {
+ "details": "Details",
+ "key": "KEY",
+ "value": "VALUE",
+ "dataTraceTips": "The data involves combination.",
+ "noDataFound": "No data found.",
+ "click": "Click",
+ "viewAllData": "to view all data."
+ },
+ "trainingDashboard": {
+ "trainingDashboardTitle": "Training Dashboard",
+ "calculationChart": "Computational Graph",
+ "dataMap": "Data Graph",
+ "trainingScalar": "Training Scalar Information",
+ "samplingData": "Data Sampling",
+ "imagesampleSwitch": "Switch Tag",
+ "invalidId": "Invalid training job.",
+ "summaryDirPath": "Summary path:",
+ "loadingTip": "Loading..."
+ },
+ "scalar": {
+ "titleText": "Scalar",
+ "tagSelectTitle": "Tag Selection",
+ "xAxisTitle": "Horizontal axis",
+ "smoothness": "Smoothness",
+ "step": "Step",
+ "selectAll": "All",
+ "relativeTime": "Relative time",
+ "absoluteTime": "Absolute time",
+ "fullScreen": "Full Screen",
+ "stepBack": "Rollback by Step",
+ "openOrCloseSelection": "Enable/Disable Box Selection",
+ "toggleYaxisScale": "Switch Y-axis Ratio",
+ "charTipHeadName": "Name",
+ "charTipTagName": "Tag",
+ "charTipHeadValue": "Value",
+ "charSmoothedValue": "Smoothness",
+ "comparison": "Scalar Synthesis",
+ "compareCancel": "Cancel Synthesis",
+ "open": "More",
+ "close": "Less",
+ "invalidData": "Invalid data exists.",
+ "restore": "Restore",
+ "currentThreshold": "Current threshold",
+ "deleteThreshold": "Delete Threshold",
+ "setThreshold": "Set Threshold",
+ "currentTag": "Current Tag",
+ "filterCriteria": "Filter",
+ "placeHolderThreshold": "Threshold",
+ "or": "Or",
+ "and": "And",
+ "greaterThan": "Greater than",
+ "lessThan": "Less than",
+ "applyAllSelectTag": "Apply to Selected Tags",
+ "placeHolderNumber": "Enter a number",
+ "noSpace": "Do not enter spaces.",
+ "sameCompare": "The comparison operator must be unique.",
+ "unreasonable": "The logic is improper.",
+ "info": "Information",
+ "isDelete": "Are you sure you want to delete the current threshold?"
+ },
+ "images": {
+ "titleText": "Image",
+ "tagSelectTitle": "Tag Selection",
+ "selectAll": "All",
+ "open": "More",
+ "close": "Less",
+ "step": "Step:",
+ "setBright": "Brightness",
+ "setContrast": "Contrast"
+ },
+ "histogram": {
+ "titleText": "Parameter Distribution",
+ "xAxisTitle": "Vertical axis",
+ "viewType": "Angle of View",
+ "centerValue": "Center Value",
+ "step": "Step",
+ "relativeTime": "Relative Time",
+ "absoluteTime": "Absolute Time",
+ "overlay": "Front",
+ "offset": "Top",
+ "fullScreen": "Full Screen"
+ },
+ "dataMap": {
+ "titleText": "Data Graph"
+ },
+ "tensors": {
+ "titleText": "Tensor",
+ "dimension": "Shape:",
+ "tensorType": "Data type:",
+ "viewTypeTitle": "View",
+ "chartViewType": "Table",
+ "histogramViewType": "Histogram",
+ "tensorDashboardLimitErrorMsg": "The requested data is too large. Go to the tensor page and try another dimension."
+ },
+ "graph": {
+ "titleText": "Computational Graph",
+ "downloadPic": "Download",
+ "fitScreen": "Fit to Screen",
+ "nodeInfo": "Node Information",
+ "legend": "Legend",
+ "nameSpace": "Namespace",
+ "operatorNode": "Operator Node",
+ "virtualNode": "Virtual Node",
+ "constantNode": "Constant Node",
+ "polymetric": "Aggregation Node",
+ "dataFlowEdge": "Data Flow Edge",
+ "controllDepEdge": "Control Dependency Edge",
+ "name": "Name",
+ "count": "Subnodes",
+ "type": "Type",
+ "attr": "Attribute",
+ "inputs": "Input",
+ "outputs": "Output",
+ "outputs_i": "Outputs_i",
+ "controlDependencies": "Control Edge",
+ "searchLoading": "Locating nodes... Please wait. The locating speed depends on the number of nodes. A large number of nodes will slow down the speed.",
+ "queryLoading": "Loading... Please wait.",
+ "fullScreen": "Full Screen",
+ "tooManyNodes": "Too many nodes to open.",
+ "inputNodeName": "Enter node name",
+ "guide": "User Guide",
+ "guideTitle1": "Introduction 1 of 3: Main Functions",
+ "guideTitle2": "Introduction 2 of 3: Node Types",
+ "guideTitle3": "Introduction 3 of 3: Edges",
+ "guideContent11": "1. In a computational graph display area, you can view a computational graph, zoom in or out a computational graph by scrolling the mouse wheel, and drag a computational graph.",
+ "guideContent12": "2. A computational graph can be displayed in full screen or saved as an SVG file.",
+ "guideContent13": "3. In the function area on the right, you can switch to view computational graphs of different files or search for nodes in a computational graph.",
+ "guideContent14": "4. In the node information, you can click an input or output node to go to the selected node.",
+ "guideContent2": "Node types of a computational graph include namespace node, operator node, virtual node, aggregation node, and constant node. \"Default\" indicates forward propagation, and \"Gradients\" indicates backward propagation. ",
+ "guideContent3": "Data edges and control edges exist in a computational graph. A data edge indicates the data input, and a control edge indicates the execution dependency between node.",
+ "next": "Next",
+ "finish": "Complete",
+ "dataTooLarge": "Failed to open the graph because of too many nodes and edges.",
+ "tooManyChain": "The direct subnode depth exceeds 70 and cannot be expanded."
+ },
+ "operator": {
+ "titleText": "Profiling",
+ "currentCard": "Number of cards",
+ "pie": "Pie",
+ "bar": "Bar",
+ "operatorStatistics": "Operator Statistics",
+ "operatorTypeStatistics": "Operator Type Statistics",
+ "allOperator": "All",
+ "classificationOperator": "Type",
+ "card": " ",
+ "searchByType": "Enter operator type",
+ "searchByName": "Enter operator name"
+ },
+ "profiling": {
+ "profilingDashboard": "Profiling Dashboard",
+ "showAverage": "Average value",
+ "iterationGapTime": "Step interval",
+ "time": "Time",
+ "operatorTimeConAnalysis": "Operator Time Consumption Analysis",
+ "avgCost": "Average total consumed time:",
+ "getCost": "Average data obtaining time:",
+ "pushCost": "Average data push time:",
+ "lterationGap": "Step Interval",
+ "lterationTail": "Step Tail",
+ "minddataTitle": "Data Preparation Details",
+ "dataQueue": "Data Queues",
+ "smartHelper": "Helper",
+ "suggestions": "Suggestions",
+ "common-profiler_tutorial": {
+ "desc": "How Do I Use Profiler for Profiling?",
+ "anchor": ["desc"],
+ "url": [
+ "https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/performance_profiling.html"
+ ]
+ },
+ "step_trace-proposer_type_label": {
+ "desc": "Step trace performance optimization"
+ },
+ "step_trace-iter_interval": {
+ "desc": "After the praph mode and dataset sink mode are enabled, if the average step interval is greater than {n1} ms, the process from data processing to computational graph execution can be optimized."
+ },
+ "common-proposer_type_label": {
+ "desc": "Profiling and optimization guide"
+ },
+ "minddata_pipeline-proposer_type_label": {
+ "desc": "Data processing performance optimization"
+ },
+ "minddata_pipeline-general": {
+ "desc": "The {n1} operator in the pipeline may have performance bottlenecks."
+ },
+ "minddata_pipeline-dataset_op": {
+ "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter."
+ },
+ "minddata_pipeline-generator_op": {
+ "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter or optimize the training script. If the performance is not optimized, you can replace the operator with the MindRecordDataset operator."
+ },
+ "minddata_pipeline-map_op": {
+ "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter. If the Python operator is used, you can optimize the training script."
+ },
+ "minddata_pipeline-batch_op": {
+ "desc": "For operator {n1}, you can increase the prefetch_size value."
+ },
+ "minddata_warning_op": {
+ "desc": "Based on the preceding determination, the operator {n1} can be optimized."
+ },
+ "minddata-proposer_type_label": {
+ "desc": "Step interval profiling"
+ },
+ "minddata_device_queue": {
+ "desc": "The ratio of empty queues on a host is {n1}/{n2}, and the ratio of full queues is {n3}/{n4}."
+ },
+ "minddata_get_next_queue": {
+ "desc": "The ratio of empty queues on a chip is {n1}/{n2}."
+ },
+ "millisecond": "ms",
+ "curCard": "Number of cards",
+ "stepTrace": "Step Trace",
+ "mindData": "Data Preparation",
+ "timeLine": "Timeline",
+ "rankOfOperator": "Operator Time Consumption Ranking",
+ "stepTraceDetail": "Step Trace Details",
+ "viewDetail": "Details",
+ "stepNum": "Steps",
+ "iterGapTimeLabel": "Time",
+ "iterGapRateLabel": "Ratio",
+ "fpBpTimeLabel": "Time",
+ "fpBpRateLabel": "Ratio",
+ "tailTimeLabel": "Time",
+ "tailRateLabel": "Ratio",
+ "operatorDetail": "Operator Details",
+ "times": "times",
+ "queueStep": "Queue Step Distribution",
+ "queueInfo": "Step Interval",
+ "pipeline": "Data Processing",
+ "pipelineTopTitle": "Average usage of queues between operators",
+ "pipelineMiddleTitle": "Queue relationship between operators",
+ "deviceQueueOp": "Data Transmission",
+ "deviceQueueOpTip": "Data Transmission Operator",
+ "getNext": "Data Obtaining Operator",
+ "connectorQuene": "Host Queues",
+ "getData": "Data Obtaining",
+ "opTotalTime": "Total operator execution time:",
+ "streamNum": "Number of executed flows:",
+ "opNum": "Number of operators:",
+ "opTimes": "Total operator execution times:",
+ "features": "Functions:",
+ "iterationInfo": "The step trace displays the duration of each step from the start of the previous iteration to the end of the step. The main time is divided into three parts: step interval, forward and backward propagation, and step tail.",
+ "iterationGapInfo": "Reads data from data queues. If this part takes a long time, you are advised to check the data processing for further analysis.",
+ "fpbpTitle": "Forward and Backward Propagation",
+ "fpbpInfo": "Executes the forward and backward operators on the network, which carry the main calculation work of a step. If this part takes a long time, you are advised to check the operator statistics or timeline for further analysis.",
+ "iterativeTailingTitle": "Step Tail",
+ "iterativeTailingInfo": "Performs parameter aggregation and update operations in multi-card scenarios. If the operations take a long time,you are advised to check the time consumed by all_reduce and the parallel status.",
+ "statistics": "Statistics:",
+ "totalTime": "Total consumed time:",
+ "totalSteps": "Total steps:",
+ "fpbpTimeRatio": "Ratio of time consumed by forward and backward propagation:",
+ "iterationGapTimeRatio": "Ratio of time consumed by step interval:",
+ "iterativeTailingTimeRatio": "Ratio of time consumed by step tail:",
+ "dataProcess": "This shows the data processing. Data is stored in the host queue during data processing, and then stored in the data queue on a chip during data transmission. Finally, the data transmission operator get_next transmits the data to forward propagation.",
+ "dataProcessInfo": "By determining the empty host and data queues, you can preliminarily determine the stage where the performance is abnormal.",
+ "analysisOne": "1. If the step interval is long and some batches of the data queue on a chip are empty, the performance is abnormal during data processing and transmission. Otherwise, locate the internal problem of the data transmission operator get_next.",
+ "analysisTwo": "2. If the performance is abnormal during data processing and transmission, check the host queue. If the host queue is empty at a high probability, the exception may occur during data transmission.",
+ "higherAnalysis": "Note: You can perform advanced analysis based on the time consumed by operators.",
+ "chipInfo": "Ratio of empty data queues on a chip:",
+ "hostIsEmpty": "Ratio of empty queues on a host:",
+ "hostIsFull": "Ratio of full queues on a host:",
+ "operatorInfo": "Operator information of {msg1} and {msg2}",
+ "workersNum": "Number of threads",
+ "queueDeepChartTitle": "{msg} Depth Line Chart",
+ "sampleInterval": "Sampling interval",
+ "queueTip1": "Ratio of full queues:",
+ "queueTip2": "Ratio of empty queues:",
+ "totalCapacity": "Total capacity",
+ "averageCapacity": "Average used capacity",
+ "FPMessage": "FP start operator:",
+ "BPMessage": "BP termination operator:",
+ "approximateTime": "Total duration ≈ ",
+ "stepInputTip": "Steps (an integer ranging from 1 to {max})",
+ "inputError": "Input parameter error. Please enter a positive integer ranging from 1 to {max}",
+ "defaultTip": "Average value (default)",
+ "downloadTimeline": "Download",
+ "timelineTips": {
+ "title1": "The timeline function helps you analyze the training process and displays the following information:",
+ "content11": "- Device(AI CPU or AI core) to which an operator is allocated for execution.",
+ "content12": "- Flow tiling policy of MindSpore on the network.",
+ "content13": "- Execution sequence and duration of an operator on a device.",
+ "title2": "How to view the timeline details?",
+ "content21": {
+ "part1": "Click ",
+ "part2": "Download",
+ "part3": " to save a file containing the timeline information to a local host."
+ },
+ "content22": "View the information using either Google plug-in (chrome://tracing) or Perfetto (https://ui.perfetto.dev/#!/viewer).",
+ "content23": {
+ "part1": "Select one of the preceding two tools, enter its address in an address box of a browser, and press ",
+ "part2": "Enter",
+ "part3": ". On the page that is displayed, click ",
+ "part4": "Load",
+ "part5": " in the upper left corner of the tracing tool or click ",
+ "part6": "Open trace file",
+ "part7": " in the left pane of the Perfetto tool."
+ },
+ "title3": "How to use the timeline?",
+ "content31": "You can analyze whether the flow tiling policy is proper and whether the step interval and tail time are too long based on the timeline information.",
+ "content32": "You can also locate an operator and view and analyze its execution time."
+ },
+ "unit": "ms/time"
+ },
+ "hardwareVisual": {
+ "processor": "Ascend AI Processor",
+ "ram": "Memory",
+ "selectedCpu": "Selected CPUs:",
+ "allCpu": "Total CPUs:",
+ "chipNameTip": "Chip name",
+ "deviceIdTip": "Chip ID",
+ "availableTip": "Available or not(for reference only)",
+ "healthTip": "Chip health index",
+ "ipTip": "Chip IP address",
+ "aicoreTip": "Chip usage",
+ "hbmTip": "Used HBM memory",
+ "powerTip": "Chip power consumption",
+ "temperatureTip": "Chip temperature",
+ "cpuUserTip": "Time for running in user mode (%)",
+ "cpuSystemTip": "Time for running in kernel mode (%)",
+ "cpuIdleTip": "Idle time (%)",
+ "cpuNiceTip": "Time for running low-priority processes (%)",
+ "cpuIowaitTip": "Time for waiting for I/O (%)",
+ "cpuIrqTip": "Time for processing hardware interrupts (%)",
+ "cpuSoftirqTip": "Time for processing software interrupts (%)",
+ "cpuStealTip": "Time occupied by other VMs (%)",
+ "cpuGuestTip": "Time for running the VM (%)",
+ "cpuGuestniceTip": "Time for running low-priority VMs (%)",
+ "cpuInterruptTip": "Time for processing hardware interrupts (%)",
+ "cpuDpcTip": "Time for remote calling (%)",
+ "noNpuInfo": "No Ascend AI processor information",
+ "normal": "Normal",
+ "generalWarn": "Minor warning",
+ "importantWarn": "Major warning",
+ "emergencyWarn": "Critical warning",
+ "noChip": "The chip does not exist or is not started.",
+ "availableFree": "The chip is available.",
+ "availableBusy": "The chip is occupied or unavailable.",
+ "failQueryChip": "An error occurs during chip information query.",
+ "faliQuery": "Query error"
+ },
+ "components": {
+ "summaryTitle": "Training selection",
+ "tagSelectTitle": "Tag Selection",
+ "selectAll": "All",
+ "tagFilterPlaceHolder": "Enter tag (regular expression supported)",
+ "open": "More",
+ "close": "Less",
+ "gridIncorrectDataError": "A maximum of two-dimensionalarrays can be displayed.",
+ "gridAccuracy": "Decimal places are reserved.",
+ "inCorrectInput": "Invalid input.",
+ "gridTableNoData": "No data in the table."
+ },
+ "error": {
+ "50540000": "System error.",
+ "50540001": "Incorrect parameter type. Check whether the request parameter types meet the requirements.",
+ "50540002": "Incorrect parameter value. Check whether the request parameter values meet the requirements.",
+ "50540003": "Mandatory parameters are missing. Check whether all mandatory parameters meet the requirements.",
+ "50545001": "The API route resource does not exist.",
+ "50545002": "Incorrect HTTP method for requesting the API.",
+ "50545005": "The training job does not exist.",
+ "50545007": "Loading summary data... Please wait.",
+ "50545009": "The queried node is not in the graph. Please refresh.",
+ "5054500A": "Failed to decode the URL of the training job ID.",
+ "5054500C": "The computational graph does not exist. Please refresh.",
+ "5054500D": "The image data does not exist. Please refresh.",
+ "5054500E": "The scalar data does not exist. Please refresh.",
+ "5054500F": "The parameter distribution data does not exist. Please refresh.",
+ "50545010": "The requested data is not in the cache. Refresh.",
+ "50542082": "The model name is missing.",
+ "50542085": "Invalid model name.",
+ "50542215": "Incorrect query parameters.",
+ "50542216": "The summary log file is not found.",
+ "50542217": "Incorrect summary log path.",
+ "50542218": "Incorrect filtering parameter.",
+ "50545012": "The tensor data does not exist. Please refresh.",
+ "50545013": "The requested data is too large. Try another dimension.",
+ "50545014": "The queried tensor data has been replaced by new data. Please refresh.",
+ "50548001": "Ascend AI Processor information query timed out."
+ }
+}
diff --git a/mindinsight/ui/src/locales/zh-cn.json b/mindinsight/ui/src/locales/zh-cn.json
index a847ede606ee855731975b1a62a3ea3a63cb051c..9da74ee31b6aca2830b64854baab39b3d0b96014 100644
--- a/mindinsight/ui/src/locales/zh-cn.json
+++ b/mindinsight/ui/src/locales/zh-cn.json
@@ -367,9 +367,21 @@
"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。",
"title2": "如何查看时间线：",
- "content21": "要查看时间线的详细信息，您可以点击 \"下载\" 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。",
+ "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": "选择一个工具，在浏览器地址栏输入地址并回车，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 \"load\"，Perfetto工具点击左侧栏 \"Open trace file\"）。",
+ "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。"
diff --git a/mindinsight/ui/src/main.js b/mindinsight/ui/src/main.js
index 2640b1b4e6989e1726315779e192d549ce848421..d63a48d00dd122860fac71f8eb9d2a3839f8e137 100644
--- a/mindinsight/ui/src/main.js
+++ b/mindinsight/ui/src/main.js
@@ -23,9 +23,18 @@ import './assets/css/element.css';
import './assets/css/reset.scss';
import i18n from './i18n';
import $ from 'jquery';
+import locale from '../node_modules/element-ui/lib/locale/lang/en';
+import localezh from '../node_modules/element-ui/lib/locale/lang/zh-CN';
+if (
+ localStorage.getItem('milang') &&
+ localStorage.getItem('milang') !== 'zh-cn'
+) {
+ Vue.use(ElementUI, {locale});
+} else {
+ Vue.use(ElementUI, {localezh});
+}
window.$ = window.jQuery = $;
-Vue.use(ElementUI);
Vue.prototype.$bus = new Vue();
@@ -47,7 +56,6 @@ router.onError((error) => {
// forbidden showing production tip
Vue.config.productionTip = false;
-
/**
* Check the browser version
* @return {Boolen}
diff --git a/mindinsight/ui/src/views/train-manage/data-process.vue b/mindinsight/ui/src/views/train-manage/data-process.vue
index 243aad6b00edc9e13cb3827bc25233ebd754b9f7..eda2d88f66fe2817a85bd73b5c84d1f637a72637 100644
--- a/mindinsight/ui/src/views/train-manage/data-process.vue
+++ b/mindinsight/ui/src/views/train-manage/data-process.vue
@@ -316,6 +316,7 @@ export default {
noData: true,
size: null,
initOver: false,
+ deviceId: null,
},
dataQueueChart: {
// Data queue chart object
@@ -373,6 +374,7 @@ export default {
// Average rate chart object
id: 'average-rate',
chartDom: null,
+ deviceId: null,
},
queueDeepChart: {
// Queue deep chart object
@@ -438,14 +440,20 @@ export default {
* Tabs switch
*/
handleClick() {
- if (this.activeName === 'pipeLine') {
- if (!Object.keys(this.allGraphData).length) {
- this.$nextTick(() => {
- this.queryAverageRate();
- });
- }
+ if (
+ this.activeName === 'pipeLine' &&
+ this.averageRateChart.deviceId !== this.currentCard
+ ) {
+ this.queryAverageRate();
+ } else if (
+ this.activeName === 'queueInfo' &&
+ this.connectQueueChart.deviceId !== this.currentCard
+ ) {
+ this.init();
}
- this.resizeCallback();
+ this.$nextTick(() => {
+ this.resizeCallback();
+ });
},
init() {
this.queryProcessSummary();
@@ -652,6 +660,7 @@ export default {
device_id: this.currentCard,
train_id: this.trainId,
};
+ this.connectQueueChart.deviceId = this.currentCard;
RequestService.queryProcessSummary(params).then(
(res) => {
if (res && res.data) {
@@ -732,6 +741,7 @@ export default {
device_id: this.currentCard,
},
};
+ this.averageRateChart.deviceId = this.currentCard;
RequestService.queryOpQueue(params).then(
(res) => {
this.initOver = true;
diff --git a/mindinsight/ui/src/views/train-manage/graph.vue b/mindinsight/ui/src/views/train-manage/graph.vue
index cbcdc010c18bb046448f058cceaa090b1458b7fb..904a02ffea2386652ed433cc7ee3255612eb092b 100644
--- a/mindinsight/ui/src/views/train-manage/graph.vue
+++ b/mindinsight/ui/src/views/train-manage/graph.vue
@@ -42,17 +42,17 @@ limitations under the License.
+ :title="$t('header.refreshingData')" />
+
+ 
