Huawei_Technology
/
mindspore-mindinsight
Not watched
1
0
Fork 0
Code
Releases 11 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.
Tree: d89dcb8406
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'd89dcb8406'
${ noResults }
mindspore-mindinsight/mindinsight/ui/src/locales/en-us.json

484 lines
22 kB
Raw Blame History 

  1. {

  2.   "public": {

  3.     "netWorkError": "Network or backend service error. Please check.",

  4.     "browserWarning": "Your browser may cause some functions to become invalid or unavailable. You are advised to use Chrome 65 or later.",

  5.     "timeout": "Timeout. Try again.",

  6.     "noData": "No data",

  7.     "reset": "Reset",

  8.     "tagFilterPlaceHolder": "Enter tag (regular expression supported)",

  9.     "dataError": "The obtained data is abnormal.",

  10.     "regIllegal": "Enter a correct search criterion.",

  11.     "stayTuned": "Coming soon...",

  12.     "select": "Select",

  13.     "search": "Search",

  14.     "enter": "Enter",

  15.     "remark": "Remarks",

  16.     "zhLanguage": "中",

  17.     "enLanguage": "EN",

  18.     "sure": "OK",

  19.     "clear": "Clear",

  20.     "cancel": "Cancel",

  21.     "selectAll": "All",

  22.     "deselectAll": "Clear",

  23.     "dataLoading": "Loading data..."

  24.   },

  25.   "symbols": {

  26.     "leftbracket": "(",

  27.     "rightbracket": ")",

  28.     "point": "·",

  29.     "slashes": "/"

  30.   },

  31.   "header": {

  32.     "refreshData": "Refresh Data",

  33.     "refreshingData": "Refreshing data...",

  34.     "timeReload": "Auto Refresh",

  35.     "timeSecond": "Seconds",

  36.     "timeReloadScope": "The automatic refresh interval ranges from 3 to 300 seconds."

  37.   },

  38.   "summaryManage": {

  39.     "summaryList": "Summary List",

  40.     "currentFolder": "Current folder:",

  41.     "sorting": "No.",

  42.     "summaryPath": "Summary Path",

  43.     "createTime": "Creation Time",

  44.     "updateTime": "Update Time",

  45.     "operation": "Operation",

  46.     "viewDashboard": "Training Dashboard",

  47.     "viewProfiler": "Profiling",

  48.     "modelTraceback": "Model Lineage",

  49.     "dataTraceback": "Dataset Lineage",

  50.     "comparePlate": "Comparison Dashboard",

  51.     "disableProfilerTip": "Failed to view profiling because no profiler log is available.",

  52.     "hardwareVisual": "Hardware Resources",

  53.     "openNewTab": "Open Link in New Tab"

  54.   },

  55.   "modelTraceback": {

  56.     "summaryPath": "Summary Path",

  57.     "trainSetPath": "Training Set Path",

  58.     "testSetPath": "Test Set Path",

  59.     "trainingSampleNum": "Training Samples",

  60.     "testSampleNum": "Testing Samples",

  61.     "showAllData": "Display All Data",

  62.     "network": "Network",

  63.     "optimizer": "Optimizer",

  64.     "lossFunc": "Loss Function",

  65.     "learningRate": "Learning Rate",

  66.     "modelSize": "Model Size",

  67.     "dataProcess": "Data Processing",

  68.     "noDataFound": "No data found.",

  69.     "click": "Click",

  70.     "showAllDataBtn": "Display All Data",

  71.     "viewAllData": "to view all data.",

  72.     "userDefined": "User-defined Data",

  73.     "metric": "Metrics",

  74.     "deviceNum": "Devices",

  75.     "mixedItemMessage": "This parameter contains multiple types of data and cannot be filtered.",

  76.     "displayColumn": "Displayed columns",

  77.     "hide": "Hide Record",

  78.     "unhide": "Unhide Record",

  79.     "totalHide": "Hidden records on this page: {n}",

  80.     "mustExist": "Mandatory",

  81.     "remarkValidation": "Remarks can contain 1 to 128 characters, including letters, digits, underscores (_), hyphens (-), and periods (.).",

  82.     "changeSuccess": "Modified successfully.",

  83.     "metricLabel": "Metric",

  84.     "userDefinedLabel": "User Defined",

  85.     "hyperLabel": "Hyper",

  86.     "otherLabel": "Other",

  87.     "remarkTips": "Note: Once the service is terminated, remarks and tags will be cleared."

  88.   },

  89.   "dataTraceback": {

  90.     "details": "Details",

  91.     "key": "KEY",

  92.     "value": "VALUE",

  93.     "dataTraceTips": "The data involves combination.",

  94.     "noDataFound": "No data found.",

  95.     "click": "Click",

  96.     "viewAllData": "to view all data."

  97.   },

  98.   "trainingDashboard": {

  99.     "trainingDashboardTitle": "Training Dashboard",

  100.     "calculationChart": "Computational Graph",

  101.     "dataMap": "Data Graph",

  102.     "trainingScalar": "Training Scalar Information",

  103.     "samplingData": "Data Sampling",

  104.     "imagesampleSwitch": "Switch Tag",

  105.     "invalidId": "Invalid training job.",

  106.     "summaryDirPath": "Summary path:",

  107.     "loadingTip": "Loading..."

  108.   },

  109.   "scalar": {

  110.     "titleText": "Scalar",

  111.     "tagSelectTitle": "Tag Selection",

  112.     "xAxisTitle": "Horizontal axis",

  113.     "smoothness": "Smoothness",

  114.     "step": "Step",

  115.     "selectAll": "All",

  116.     "relativeTime": "Relative time",

  117.     "absoluteTime": "Absolute time",

  118.     "fullScreen": "Full Screen",

  119.     "stepBack": "Rollback by Step",

  120.     "openOrCloseSelection": "Enable/Disable Box Selection",

  121.     "toggleYaxisScale": "Switch Y-axis Ratio",

  122.     "charTipHeadName": "Name",

  123.     "charTipTagName": "Tag",

  124.     "charTipHeadValue": "Value",

  125.     "charSmoothedValue": "Smoothness",

  126.     "comparison": "Scalar Synthesis",

  127.     "compareCancel": "Cancel Synthesis",

  128.     "open": "More",

  129.     "close": "Less",

  130.     "invalidData": "Invalid data exists.",

  131.     "restore": "Restore",

  132.     "currentThreshold": "Current threshold",

  133.     "deleteThreshold": "Delete Threshold",

  134.     "setThreshold": "Set Threshold",

  135.     "currentTag": "Current Tag",

  136.     "filterCriteria": "Filter",

  137.     "placeHolderThreshold": "Threshold",

  138.     "or": "Or",

  139.     "and": "And",

  140.     "greaterThan": "Greater than",

  141.     "lessThan": "Less than",

  142.     "applyAllSelectTag": "Apply to Selected Tags",

  143.     "placeHolderNumber": "Enter a number",

  144.     "noSpace": "Do not enter spaces.",

  145.     "sameCompare": "The comparison operator must be unique.",

  146.     "unreasonable": "The logic is improper.",

  147.     "info": "Information",

  148.     "isDelete": "Are you sure you want to delete the current threshold?"

  149.   },

  150.   "images": {

  151.     "titleText": "Image",

  152.     "tagSelectTitle": "Tag Selection",

  153.     "selectAll": "All",

  154.     "open": "More",

  155.     "close": "Less",

  156.     "step": "Step:",

  157.     "setBright": "Brightness",

  158.     "setContrast": "Contrast"

  159.   },

  160.   "histogram": {

  161.     "titleText": "Parameter Distribution",

  162.     "xAxisTitle": "Vertical axis",

  163.     "viewType": "Angle of View",

  164.     "centerValue": "Center Value",

  165.     "step": "Step",

  166.     "relativeTime": "Relative Time",

  167.     "absoluteTime": "Absolute Time",

  168.     "overlay": "Front",

  169.     "offset": "Top",

  170.     "fullScreen": "Full Screen"

  171.   },

  172.   "dataMap": {

  173.     "titleText": "Data Graph"

  174.   },

  175.   "tensors": {

  176.     "titleText": "Tensor",

  177.     "dimension": "Shape:",

  178.     "tensorType": "Data type:",

  179.     "viewTypeTitle": "View",

  180.     "chartViewType": "Table",

  181.     "histogramViewType": "Histogram",

  182.     "tensorDashboardLimitErrorMsg": "The requested data is too large. Go to the tensor page and try another dimension."

  183.   },

  184.   "graph": {

  185.     "titleText": "Computational Graph",

  186.     "downloadPic": "Download",

  187.     "fitScreen": "Fit to Screen",

  188.     "nodeInfo": "Node Information",

  189.     "legend": "Legend",

  190.     "nameSpace": "Namespace",

  191.     "operatorNode": "Operator Node",

  192.     "virtualNode": "Virtual Node",

  193.     "constantNode": "Constant Node",

  194.     "polymetric": "Aggregation Node",

  195.     "dataFlowEdge": "Data Flow Edge",

  196.     "controllDepEdge": "Control Dependency Edge",

  197.     "name": "Name",

  198.     "count": "Subnodes",

  199.     "type": "Type",

  200.     "attr": "Attribute",

  201.     "inputs": "Input",

  202.     "outputs": "Output",

  203.     "outputs_i": "Outputs_i",

  204.     "controlDependencies": "Control Edge",

  205.     "searchLoading": "Locating nodes... Please wait. The locating speed depends on the number of nodes. A large number of nodes will slow down the speed.",

  206.     "queryLoading": "Loading... Please wait.",

  207.     "fullScreen": "Full Screen",

  208.     "tooManyNodes": "Too many nodes to open.",

  209.     "inputNodeName": "Enter node name",

  210.     "guide": "User Guide",

  211.     "guideTitle1": "Introduction 1 of 3: Main Functions",

  212.     "guideTitle2": "Introduction 2 of 3: Node Types",

  213.     "guideTitle3": "Introduction 3 of 3: Edges",

  214.     "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.",

  215.     "guideContent12": "2. A computational graph can be displayed in full screen or saved as an SVG file.",

  216.     "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.",

  217.     "guideContent14": "4. In the node information, you can click an input or output node to go to the selected node.",

  218.     "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. ",

  219.     "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.",

  220.     "next": "Next",

  221.     "finish": "Complete",

  222.     "dataTooLarge": "Failed to open the graph because of too many nodes and edges.",

  223.     "tooManyChain": "The direct subnode depth exceeds 70 and cannot be expanded."

  224.   },

  225.   "operator": {

  226.     "titleText": "Profiling",

  227.     "currentCard": "Number of cards",

  228.     "pie": "Pie",

  229.     "bar": "Bar",

  230.     "allOperator": "All",

  231.     "classificationOperator": "Type",

  232.     "card": " ",

  233.     "searchByType": "Enter operator type",

  234.     "searchByName": "Enter operator name",

  235.     "operatorInfo": "Operator",

  236.     "kernelInfo": "Kernel",

  237.     "searchByCoreName": "Enter kernel name",

  238.     "searchByCoreFullName": "Enter operator full name"

  239.   },

  240.   "profiling": {

  241.     "profilingDashboard": "Profiling Dashboard",

  242.     "showAverage": "Average value",

  243.     "iterationGapTime": "Step interval",

  244.     "fpBpTime": "Forward and Backward Propagation",

  245.     "tailTime": "Step Tail",

  246.     "time": "Time",

  247.     "operatorTimeConAnalysis": "Operator Time Consumption Analysis",

  248.     "avgCost": "Average total consumed time:",

  249.     "getCost": "Average data obtaining time:",

  250.     "pushCost": "Average data push time:",

  251.     "lterationGap": "Step Interval",

  252.     "lterationTail": "Step Tail",

  253.     "minddataTitle": "Data Preparation Details",

  254.     "dataQueue": "Data Queues",

  255.     "smartHelper": "Helper",

  256.     "suggestions": "Suggestions",

  257.     "common-profiler_tutorial": {

  258.       "desc": "How Do I Use Profiler for Profiling?",

  259.       "anchor": ["desc"],

  260.       "url": [

  261.         "https://www.mindspore.cn/tutorial/en/master/advanced_use/performance_profiling.html"

  262.       ]

  263.     },

  264.     "step_trace-proposer_type_label": {

  265.       "desc": "Step trace performance optimization"

  266.     },

  267.     "step_trace-iter_interval": {

  268.       "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."

  269.     },

  270.     "common-proposer_type_label": {

  271.       "desc": "Profiling and optimization guide"

  272.     },

  273.     "minddata_pipeline-proposer_type_label": {

  274.       "desc": "Data processing performance optimization"

  275.     },

  276.     "minddata_pipeline-general": {

  277.       "desc": "The {n1} operator in the pipeline may have performance bottlenecks."

  278.     },

  279.     "minddata_pipeline-dataset_op": {

  280.       "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter."

  281.     },

  282.     "minddata_pipeline-generator_op": {

  283.       "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."

  284.     },

  285.     "minddata_pipeline-map_op": {

  286.       "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter. If the Python operator is used, you can optimize the training script."

  287.     },

  288.     "minddata_pipeline-batch_op": {

  289.       "desc": "For operator {n1}, you can increase the prefetch_size value."

  290.     },

  291.     "minddata_warning_op": {

  292.       "desc": "Based on the preceding determination, the operator {n1} can be optimized."

  293.     },

  294.     "minddata-proposer_type_label": {

  295.       "desc": "Step interval profiling"

  296.     },

  297.     "minddata_device_queue": {

  298.       "desc": "The ratio of empty queues on a host is {n1}/{n2}, and the ratio of full queues is {n3}/{n4}."

  299.     },

  300.     "minddata_get_next_queue": {

  301.       "desc": "The ratio of empty queues on a chip is {n1}/{n2}."

  302.     },

  303.     "millisecond": "ms",

  304.     "curCard": "Number of cards",

  305.     "stepTrace": "Step Trace",

  306.     "mindData": "Data Preparation",

  307.     "timeLine": "Timeline",

  308.     "rankOfOperator": "Operator Time Consumption Ranking",

  309.     "stepTraceDetail": "Step Trace Details",

  310.     "viewDetail": "Details",

  311.     "stepNum": "Steps",

  312.     "iterGapTimeLabel": "Time",

  313.     "iterGapRateLabel": "Ratio",

  314.     "fpBpTimeLabel": "Time",

  315.     "fpBpRateLabel": "Ratio",

  316.     "tailTimeLabel": "Time",

  317.     "tailRateLabel": "Ratio",

  318.     "operatorDetail": "Operator Details",

  319.     "times": "times",

  320.     "queueStep": "Queue Step Distribution",

  321.     "queueInfo": "Step Interval",

  322.     "pipeline": "Data Processing",

  323.     "pipelineTopTitle": "Average usage of queues between operators",

  324.     "pipelineMiddleTitle": "Queue relationship between operators",

  325.     "deviceQueueOp": "Data Transmission",

  326.     "deviceQueueOpTip": "Forward and Backward Propagation",

  327.     "getNext": "Data Obtaining Operator",

  328.     "connectorQuene": "Host Queues",

  329.     "getData": "Data Obtaining",

  330.     "opTotalTime": "Total operator execution time:",

  331.     "streamNum": "Number of executed flows:",

  332.     "opNum": "Number of operators:",

  333.     "opTimes": "Total operator execution times:",

  334.     "features": "Functions:",

  335.     "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.",

  336.     "iterationGapInfo": "Reads data from data queues. If this part takes a long time, you are advised to check the data processing for further analysis.",

  337.     "fpbpTitle": "Forward and Backward Propagation",

  338.     "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.",

  339.     "iterativeTailingTitle": "Step Tail",

  340.     "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.",

  341.     "statistics": "Statistics:",

  342.     "totalTime": "Total consumed time:",

  343.     "totalSteps": "Total steps:",

  344.     "fpbpTimeRatio": "Ratio of time consumed by forward and backward propagation:",

  345.     "iterationGapTimeRatio": "Ratio of time consumed by step interval:",

  346.     "iterativeTailingTimeRatio": "Ratio of time consumed by step tail:",

  347.     "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 forward and backward propagation get_next transmits the data to forward propagation.",

  348.     "dataProcessInfo": "By determining the empty host and data queues, you can preliminarily determine the stage where the performance is abnormal.",

  349.     "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 forward and backward propagation get_next.",

  350.     "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.",

  351.     "higherAnalysis": "Note: You can perform advanced analysis based on the time consumed by operators.",

  352.     "chipInfo": "Ratio of empty data queues on a chip:",

  353.     "hostIsEmpty": "Ratio of empty queues on a host:",

  354.     "hostIsFull": "Ratio of full queues on a host:",

  355.     "operatorInfo": "Operator information of {msg1} and {msg2}",

  356.     "workersNum": "Number of threads",

  357.     "queueDeepChartTitle": "{msg} Depth Line Chart",

  358.     "sampleInterval": "Sampling interval",

  359.     "queueTip1": "Ratio of full queues:",

  360.     "queueTip2": "Ratio of empty queues:",

  361.     "totalCapacity": "Total capacity",

  362.     "averageCapacity": "Average used capacity",

  363.     "FPMessage": "FP start operator:",

  364.     "BPMessage": "BP termination operator:",

  365.     "approximateTime": "Total duration ≈ ",

  366.     "stepInputTip": "Steps (an integer ranging from 1 to {max})",

  367.     "inputError": "Input parameter error. Please enter a positive integer ranging from 1 to {max}",

  368.     "defaultTip": "Average value (default)",

  369.     "downloadTimeline": "Download",

  370.     "timelineTips": {

  371.       "title1": "The timeline function helps you analyze the training process and displays the following information:",

  372.       "content11": "- Device(AI CPU or AI core) to which an operator is allocated for execution.",

  373.       "content12": "- Flow tiling policy of MindSpore on the network.",

  374.       "content13": "- Execution sequence and duration of an operator on a device.",

  375.       "title2": "How to view the timeline details?",

  376.       "content21": {

  377.         "part1": "Click ",

  378.         "part2": "Download",

  379.         "part3": " to save a file containing the timeline information to a local host."

  380.       },

  381.       "content22": "View the information using either Google plug-in (chrome://tracing) or Perfetto (https://ui.perfetto.dev/#!/viewer).",

  382.       "content23": {

  383.         "part1": "Select one of the preceding two tools, enter its address in an address box of a browser, and press ",

  384.         "part2": "Enter",

  385.         "part3": ". On the page that is displayed, click ",

  386.         "part4": "Load",

  387.         "part5": " in the upper left corner of the tracing tool or click ",

  388.         "part6": "Open trace file",

  389.         "part7": " in the left pane of the Perfetto tool."

  390.       },

  391.       "title3": "How to use the timeline?",

  392.       "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.",

  393.       "content32": "You can also locate an operator and view and analyze its execution time."

  394.     },

  395.     "countUnit": "times",

  396.     "unit": "ms/time",

  397.     "gpuunit": "us/time",

  398.     "chartTitle": "Average Time Consumption Ranking"

  399.   },

  400.   "hardwareVisual": {

  401.     "processor": "Ascend AI Processor",

  402.     "ram": "Memory",

  403.     "selectedCpu": "Selected CPUs:",

  404.     "allCpu": "Total CPUs:",

  405.     "chipNameTip": "Chip name",

  406.     "deviceIdTip": "Chip ID",

  407.     "availableTip": "Is chip available(for reference only)",

  408.     "healthTip": "Chip health status",

  409.     "ipTip": "Chip IP address",

  410.     "hbmTip": "Chip HBM usage",

  411.     "powerTip": "Chip real-time power",

  412.     "temperatureTip": "Chip real-time temperature",

  413.     "cpuUserTip": "Time for running in user mode (%)",

  414.     "cpuSystemTip": "Time for running in kernel mode (%)",

  415.     "cpuIdleTip": "Idle time (%)",

  416.     "cpuNiceTip": "Time for running low-priority processes (%)",

  417.     "cpuIowaitTip": "Time for waiting for I/O (%)",

  418.     "cpuIrqTip": "Time for processing hardware interrupts (%)",

  419.     "cpuSoftirqTip": "Time for processing software interrupts (%)",

  420.     "cpuStealTip": "Time occupied by other VMs (%)",

  421.     "cpuGuestTip": "Time for running the VM (%)",

  422.     "cpuGuestniceTip": "Time for running low-priority VMs (%)",

  423.     "cpuInterruptTip": "Time for processing hardware interrupts (%)",

  424.     "cpuDpcTip": "Time for remote calling (%)",

  425.     "noNpuInfo": "No Ascend AI processor information",

  426.     "normal": "Normal",

  427.     "generalWarn": "Minor warning",

  428.     "importantWarn": "Major warning",

  429.     "emergencyWarn": "Critical warning",

  430.     "noChip": "The chip does not exist or is not started.",

  431.     "availableFree": "The chip is available.",

  432.     "availableBusy": "The chip is occupied or unavailable.",

  433.     "failQueryChip": "An error occurs during chip information query.",

  434.     "name": "Name",

  435.     "npu": "ID",

  436.     "available": "Available",

  437.     "health": "Health",

  438.     "ipAddress": "IP Address",

  439.     "hbmUsage": "HBM-Usage(MB)",

  440.     "power": "Power(W)",

  441.     "temp": "Temp(℃)"

  442.   },

  443.   "components": {

  444.     "summaryTitle": "Training selection",

  445.     "tagSelectTitle": "Tag Selection",

  446.     "selectAll": "All",

  447.     "tagFilterPlaceHolder": "Enter tag (regular expression supported)",

  448.     "open": "More",

  449.     "close": "Less",

  450.     "gridIncorrectDataError": "A maximum of two-dimensionalarrays can be displayed.",

  451.     "gridAccuracy": "Decimal places are reserved.",

  452.     "inCorrectInput": "Invalid input.",

  453.     "gridTableNoData": "No data in the table.",

  454.     "cache": "CACHING"

  455.   },

  456.   "error": {

  457.     "50540000": "System error.",

  458.     "50540001": "Incorrect parameter type. Check whether the request parameter types meet the requirements.",

  459.     "50540002": "Incorrect parameter value. Check whether the request parameter values meet the requirements.",

  460.     "50540003": "Mandatory parameters are missing. Check whether all mandatory parameters meet the requirements.",

  461.     "50545001": "The API route resource does not exist.",

  462.     "50545002": "Incorrect HTTP method for requesting the API.",

  463.     "50545005": "The training job does not exist.",

  464.     "50545007": "Loading summary data... Please wait.",

  465.     "50545009": "The queried node is not in the graph. Please refresh.",

  466.     "5054500A": "Failed to decode the URL of the training job ID.",

  467.     "5054500C": "The computational graph does not exist. Please refresh.",

  468.     "5054500D": "The image data does not exist. Please refresh.",

  469.     "5054500E": "The scalar data does not exist. Please refresh.",

  470.     "5054500F": "The parameter distribution data does not exist. Please refresh.",

  471.     "50545010": "The requested data is not in the cache. Refresh.",

  472.     "50542082": "The model name is missing.",

  473.     "50542085": "Invalid model name.",

  474.     "50542215": "Incorrect query parameters.",

  475.     "50542216": "The summary log file is not found.",

  476.     "50542217": "Incorrect summary log path.",

  477.     "50542218": "Incorrect filtering parameter.",

  478.     "50545012": "The tensor data does not exist. Please refresh.",

  479.     "50545013": "The requested data is too large. Try another dimension.",

  480.     "50545014": "The queried tensor data has been replaced by new data. Please refresh.",

  481.     "50548001": "Ascend AI Processor information query timed out."

  482.   }

  483. }