"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",
"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.",
