Chronos: Add forecasters filter tags in tutorial page. (#6068)
* Update: add forecaster tags in tutorial page. * Bugfix: fix model tag bug. * Update: update js with pr comment.
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Juntao_Luo
GitHub
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commit
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3 changed files with 231 additions and 51 deletions
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@ -8,6 +8,10 @@
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margin: 0px 5px 10px 5px;
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}
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.hiddenline{
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display: none;
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}
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.choicebox{
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width: 200px;
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height: 20px;
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@ -48,4 +52,12 @@ summary p button{
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box-shadow: inset 0 1px 2px -1px hsl(0deg 0% 100% / 90%);
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color: #404040;
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font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;
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}
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.roundbutton{
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border-radius: 20px;
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}
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.showingForecaster i{
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font-weight: bolder;
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}
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@ -1,13 +1,22 @@
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$(document).ready(function(){
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$("#tutorial details").attr("open",true);
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$('#tutorial details').each(function(){
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$(this).addClass('forecasterSelected');
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$(this).addClass('filterSelected');
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});
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displaySelected();
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});
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//event when click the tags' buttons
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$("details p button").click(function(){
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var id = $(this).val();
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$("#"+id).trigger("click");
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});
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//func to show a tutorial
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function showTutorials(ids){
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ids.forEach(id => {
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$("#"+id).css("display","block");
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$("#"+id).attr("open","true");
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$("#"+id).next().css("display","block");
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$("#"+id).addClass("filterSelected");
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});
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}
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@ -23,16 +32,19 @@ function disCheck(ids){
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//event when click the checkboxes
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$(".checkboxes").click(function(){
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//reset all the details 'filterSelected' class
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$('#tutorial details').each(function(){
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$(this).removeClass('filterSelected');
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});
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//get all checked values
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//class checkboxes is specified to avoid selecting toctree checkboxes (arrows)
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var vals = [];
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$('.checkboxes:input:checkbox:checked').each(function (index, item) {
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vals.push($(this).val());
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});
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//reset display
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$("#tutorial details").css("display","none");
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$("#tutorial hr").css("display","none");
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//reset checkbox and button
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$("#tutorial button").prop("disabled",false);
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$("#tutorial input[type='checkbox']").prop("disabled",false);
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@ -42,17 +54,17 @@ $(".checkboxes").click(function(){
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//show tutorial according to checked values
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if(vals.length==0){
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//choose noting, show all tutorials
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$("#tutorial details").css("display","block");
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$("#tutorial details").attr("open",true);
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$("#tutorial hr").css("display","block");
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$('#tutorial details').each(function(){
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$(this).addClass('filterSelected');
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});
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}
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//chose something, disable invalid checkboxes and buttons accordingly.
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else if(vals.length==1){
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if(vals.includes("forecast")){
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var ids = ["ChronosForecaster","TuneaForecasting","AutoTSEstimator","AutoWIDE",
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"MultvarWIDE","MultstepWIDE","LSTMForecaster","AutoProphet","AnomalyDetection",
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var ids = ["ChronosForecaster","TuneaForecasting","AutoTS","AutoWIDE",
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"MultvarWIDE","MultstepWIDE","LSTMF","AutoPr","AnomalyDetection",
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"DeepARmodel","TFTmodel","hyperparameter","taxiDataset","distributedFashion",
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"ONNX","Quantize","TCMFForecaster","PenalizeUnderestimation",
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"ONNX","Quantize","TCMF","PenalizeUnderestimation",
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"GPUtrainingCPUacceleration"];
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showTutorials(ids);
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var disIds = ["simulation"];
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@ -71,7 +83,7 @@ $(".checkboxes").click(function(){
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disCheck(disIds);
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}
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else if(vals.includes("hyperparameter_tuning")){
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var ids = ["TuneaForecasting","AutoTSEstimator","AutoWIDE","AutoProphet",
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var ids = ["TuneaForecasting","AutoTS","AutoWIDE","AutoPr",
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"hyperparameter","taxiDataset","ONNX"];
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","quantization","distributed"];
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@ -90,13 +102,16 @@ $(".checkboxes").click(function(){
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disCheck(disIds);
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}
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else if(vals.includes("distributed")){
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var ids = ["distributedFashion","TCMFForecaster"];
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var ids = ["distributedFashion","TCMF"];
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","customized_model"];
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disCheck(disIds);
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}
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else if(vals.includes("customized_model")){
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var ids = ["AutoTSEstimator","DeepARmodel","TFTmodel", "GPUtrainingCPUacceleration"];
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var ids = ["AutoTS","DeepARmodel","TFTmodel", "GPUtrainingCPUacceleration"];
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ids.forEach(id => {
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var temp=$("#"+id);
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});
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
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disCheck(disIds);
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@ -104,7 +119,7 @@ $(".checkboxes").click(function(){
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}
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else if(vals.length==2){
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if(vals.includes("forecast") && vals.includes("hyperparameter_tuning")){
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var ids = ["TuneaForecasting","AutoTSEstimator","AutoWIDE","AutoProphet","hyperparameter","taxiDataset","ONNX","AutoTSEstimator"];
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var ids = ["TuneaForecasting","AutoTS","AutoWIDE","AutoPr","hyperparameter","taxiDataset","ONNX"];
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","quantization","distributed"];
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disCheck(disIds);
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@ -116,13 +131,13 @@ $(".checkboxes").click(function(){
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disCheck(disIds);
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}
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else if(vals.includes("forecast") && vals.includes("customized_model")){
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var ids = ["DeepARmodel","TFTmodel","AutoTSEstimator","GPUtrainingCPUacceleration"];
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var ids = ["DeepARmodel","TFTmodel","AutoTS","GPUtrainingCPUacceleration"];
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
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disCheck(disIds);
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}
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else if(vals.includes("forecast") && vals.includes("distributed")){
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var ids = ["distributedFashion","TCMFForecaster"];
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var ids = ["distributedFashion","TCMF"];
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","customized_model"];
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disCheck(disIds);
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@ -134,7 +149,7 @@ $(".checkboxes").click(function(){
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disCheck(disIds);
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}
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else if(vals.includes("hyperparameter_tuning") && vals.includes("customized_model")){
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var ids = ["AutoTSEstimator"];
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var ids = ["AutoTS"];
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
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disCheck(disIds);
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@ -154,7 +169,7 @@ $(".checkboxes").click(function(){
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}
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else if(vals.length==3){
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if(vals.includes("forecast") && vals.includes("hyperparameter_tuning") && vals.includes("customized_model")){
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var ids = ["AutoTSEstimator"];
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var ids = ["AutoTS"];
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showTutorials(ids);
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var disIds = ["anomaly_detection","simulation","onnxruntime","quantization","distributed"];
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disCheck(disIds);
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@ -166,12 +181,64 @@ $(".checkboxes").click(function(){
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disCheck(disIds);
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}
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}
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displaySelected();
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});
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//event when click the tags' buttons
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$("details p button").click(function(){
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var id = $(this).val();
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$("#"+id).trigger("click");
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//event when choosing forecasters
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$(".forecasters").click(function(){
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//reset all the details 'forecasterSelected' class
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$('#tutorial details').each(function(){
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$(this).removeClass('forecasterSelected');
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});
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//get the selected forecasters
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var forecasters = [];
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$('.forecasters:checked').each(function(){
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forecasters.push($(this).val());
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});
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if(forecasters.length===0){
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//no forecaster is checked, display all
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$('#tutorial details').each(function(){
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$(this).addClass('forecasterSelected');
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});
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$(".showingForecaster i").text("All Forecasters");
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}
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else{
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//mark selected forecasters
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$('#tutorial details').each(function(){
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var sons = [];
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$(this).find(".roundbutton").each(function(){
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sons.push($(this).val())
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});
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if(forecasters.every(val => sons.includes(val))){
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$(this).addClass('forecasterSelected');
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}
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});
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let fs = "";
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for(var i=0;i<forecasters.length;i++){
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fs += forecasters[i]+', ';
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}
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fs = fs.substring(0, fs.length - 2);
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$(".showingForecaster i").text(fs);
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}
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displaySelected();
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});
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// var allIds = ["forecast","anomaly_detection","simulation","hyperparameter_tuning","onnxruntime","quantization","distributed","customized_model"];
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function displaySelected(){
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$('#tutorial details').each(function(){
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if(($(this).hasClass("forecasterSelected")) && ($(this).hasClass("filterSelected"))){
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$(this).css("display","block");
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$(this).next().css("display","block");
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}
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else{
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$(this).css("display","none");
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$(this).next().css("display","none");
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}
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});
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}
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@ -21,13 +21,32 @@
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<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="distributed" id="distributed">distributed</div>
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<div class="choicebox"><input type="checkbox" class="checkboxes" name="choice" value="customized_model" id="customized_model">customized model</div>
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</div>
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<div class="hiddenline">
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="TCNForecaster" id="TCNForecaster">TCNForecaster</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="AutoTSEstimator" id="AutoTSEstimator">AutoTSEstimator</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="DBScanDetector" id="DBScanDetector">DBScanDetector</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="LSTMForecaster" id="LSTMForecaster">LSTMForecaster</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="AutoProphet" id="AutoProphet">AutoProphet</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="MTNetForecaster" id="MTNetForecaster">MTNetForecaster</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="DeepAR" id="DeepAR">DeepAR</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="AutoLSTM" id="AutoLSTM">AutoLSTM</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="Seq2SeqForecaster" id="Seq2SeqForecaster">Seq2SeqForecaster</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="DPGANSimulator" id="DPGANSimulator">DPGANSimulator</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="TCMFForecaster" id="TCMFForecaster">TCMFForecaster</div>
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<div class="choicebox"><input type="checkbox" class="forecasters" name="forecasters" value="TFT_model" id="TFT_model">TFT_model</div>
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</div>
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</div>
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</br>
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<div class="showingForecaster">Currently showing forcaster: <i>All Forecasters</i> (<span style="font-weight:bold;">Reclick</span> the tag of these forecasters to undo.)</div>
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</br>
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<details id="ChronosForecaster">
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<summary>
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<a href="./chronos-tsdataset-forecaster-quickstart.html">Predict Number of Taxi Passengers with Chronos Forecaster</a>
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<p>Tag: <button value="forecast">forecast</button></p>
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<p>Tag:
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<button value="forecast">forecast</button>
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<button value="TCNForecaster" class="roundbutton">TCNForecaster</button>
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</p>
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</summary>
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<img src="../../../_images/colab_logo_32px.png"><a href="https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb">Run in Google Colab</a>
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@ -39,7 +58,11 @@
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<details id="TuneaForecasting">
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<summary>
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<a href="./chronos-autotsest-quickstart.html">Tune a Forecasting Task Automatically</a>
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<p>Tag: <button value="forecast">forecast</button> <button value="hyperparameter_tuning">AutoML</button></p>
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<p>Tag:
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<button value="forecast">forecast</button>
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<button value="hyperparameter_tuning">AutoML</button>
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<button value="AutoTSEstimator" class="roundbutton">AutoTSEstimator</button>
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</p>
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</summary>
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<img src="../../../_images/colab_logo_32px.png"><a href="https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_autots_nyc_taxi.ipynb">Run in Google Colab</a>
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@ -51,7 +74,10 @@
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<details id="DetectAnomaly">
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<summary>
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<a href="./chronos-anomaly-detector.html">Detect Anomaly Point in Real Time Traffic Data</a>
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<p>Tag: <button value="anomaly_detection">anomaly detection</button></p>
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<p>Tag:
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<button value="anomaly_detection">anomaly detection</button>
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<button value="DBScanDetector" class="roundbutton">DBScanDetector</button>
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</p>
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</summary>
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<img src="../../../_images/colab_logo_32px.png"><a href="https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_minn_traffic_anomaly_detector.ipynb">Run in Google Colab</a>
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</details>
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<hr>
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<details id="AutoTSEstimator">
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<details id="AutoTS">
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<summary>
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<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb">Tune a Customized Time Series Forecasting Model with AutoTSEstimator.</a>
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<p>Tag: <button value="forecast">forecast</button> <button value="hyperparameter_tuning">AutoML</button> <button value="customized_model">customized model</button></p>
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<p>Tag:
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<button value="forecast">forecast</button>
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<button value="hyperparameter_tuning">AutoML</button>
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<button value="customized_model">customized model</button>
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<button value="AutoTSEstimator" class="roundbutton">AutoTSEstimator</button>
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</p>
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</summary>
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<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_customized_model.ipynb">View source on GitHub</a>
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<p>In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demonstrate how to use <span>AutoTSEstimator</span> to adjust the parameters of a customized model.</p>
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<details id="AutoWIDE">
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<summary>
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<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb">Auto Tune the Prediction of Network Traffic at the Transit Link of WIDE</a>
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<p>Tag: <button value="forecast">forecast</button> <button value="hyperparameter_tuning">AutoML</button></p>
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<p>Tag:
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<button value="forecast">forecast</button>
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<button value="hyperparameter_tuning">AutoML</button>
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<button value="AutoTSEstimator" class="roundbutton">AutoTSEstimator</button>
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</p>
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</summary>
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<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_autots_forecasting.ipynb">View source on GitHub</a>
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<p>In this notebook, we demostrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to use <span>AutoTS</span> in project <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span> to do time series forecasting in an automated and distributed way.</p>
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<details id="MultvarWIDE">
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<summary>
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<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb">Multivariate Forecasting of Network Traffic at the Transit Link of WIDE</a>
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<p>Tag: <button value="forecast">forecast</button></p>
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<p>Tag:
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<button value="forecast">forecast</button>
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<button value="LSTMForecaster" class="roundbutton">LSTMForecaster</button>
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</p>
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</summary>
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<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_model_forecasting.ipynb">View source on GitHub</a>
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<p>In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do univariate forecasting (predict only 1 series), and multivariate forecasting (predicts more than 1 series at the same time) using Project <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span>.</p>
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@ -93,17 +131,23 @@
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<details id="MultstepWIDE">
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<summary>
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<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb">Multistep Forecasting of Network Traffic at the Transit Link of WIDE</a>
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<p>Tag: <button value="forecast">forecast</button></p>
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<p>Tag:
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<button value="forecast">forecast</button>
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<button value="TCNForecaster" class="roundbutton">TCNForecaster</button>
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</p>
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</summary>
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<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/network_traffic/network_traffic_multivariate_multistep_tcnforecaster.ipynb">View source on GitHub</a>
|
||||
<p>In this notebook, we demonstrate a reference use case where we use the network traffic KPI(s) in the past to predict traffic KPI(s) in the future. We demostrate how to do multivariate multistep forecasting using Project <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span>.</p>
|
||||
</details>
|
||||
<hr>
|
||||
|
||||
<details id="LSTMForecaster">
|
||||
<details id="LSTMF">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction.ipynb">Stock Price Prediction with LSTMForecaster</a>
|
||||
<p>Tag: <button value="forecast">forecast</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="LSTMForecaster" class="roundbutton">LSTMForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction.ipynb">View source on GitHub</a>
|
||||
<p>In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 (data source). We demostrate how to do univariate forecasting using the past 80% of the total days' MMM price to predict the future 20% days' daily price.</p>
|
||||
|
|
@ -111,10 +155,14 @@
|
|||
</details>
|
||||
<hr>
|
||||
|
||||
<details id="AutoProphet">
|
||||
<details id="AutoPr">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb">Stock Price Prediction with ProphetForecaster and AutoProphet</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="hyperparameter_tuning">AutoML</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="hyperparameter_tuning">AutoML</button>
|
||||
<button value="AutoProphet" class="roundbutton">AutoProphet</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/fsi/stock_prediction_prophet.ipynb">View source on GitHub</a>
|
||||
<p>In this notebook, we demonstrate a reference use case where we use historical stock price data to predict the future price using the ProphetForecaster and AutoProphet. The dataset we use is the daily stock price of S&P500 stocks during 2013-2018 <span><a href="https://www.kaggle.com/camnugent/sandp500/">data source</a></span>.</p>
|
||||
|
|
@ -125,7 +173,10 @@
|
|||
<details id="Unsupervised">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb">Unsupervised Anomaly Detection for CPU Usage</a>
|
||||
<p>Tag: <button value="anomaly_detection">anomaly detection</button></p>
|
||||
<p>Tag:
|
||||
<button value="anomaly_detection">anomaly detection</button>
|
||||
<button value="DBScanDetector" class="roundbutton">DBScanDetector</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb">View source on GitHub</a>
|
||||
<p>We demonstrates how to perform anomaly detection based on Chronos's built-in <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#dbscandetector">DBScanDetector</a></span>, <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#aedetector">AEDetector</a></span> and <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#thresholddetector">ThresholdDetector</a></span>.</p>
|
||||
|
|
@ -135,7 +186,11 @@
|
|||
<details id="AnomalyDetection">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb">Anomaly Detection for CPU Usage Based on Forecasters</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="anomaly_detection">anomaly detection</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="anomaly_detection">anomaly detection</button>
|
||||
<button value="MTNetForecaster" class="roundbutton">MTNetForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/blob/main/python/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised_forecast_based.ipynb">View source on GitHub</a>
|
||||
<p>We demonstrates how to leverage Chronos's built-in models ie. MTNet, to do time series forecasting. Then perform anomaly detection on predicted value with <span><a href="../../PythonAPI/Chronos/anomaly_detectors.html#thresholddetector">ThresholdDetector</a></span>.</p>
|
||||
|
|
@ -145,7 +200,11 @@
|
|||
<details id="DeepARmodel">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/DeepAR">Help pytorch-forecasting improve the training speed of DeepAR model</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="customized_model">customized model</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="customized_model">customized model</button>
|
||||
<button value="DeepAR" class="roundbutton">DeepAR</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/DeepAR">View source on GitHub</a>
|
||||
<p>Chronos can help a 3rd party time series lib to improve the performance (both training and inferencing) and accuracy. This use-case shows Chronos can easily help pytorch-forecasting speed up the training of DeepAR model.</p>
|
||||
|
|
@ -155,7 +214,11 @@
|
|||
<details id="TFTmodel">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/TFT">Help pytorch-forecasting improve the training speed of TFT model</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="customized_model">customized model</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="customized_model">customized model</button>
|
||||
<button value="TFT_model" class="roundbutton">TFT Model</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/use-case/pytorch-forecasting/TFT">View source on GitHub</a>
|
||||
<p>Chronos can help a 3rd party time series lib to improve the performance (both training and inferencing) and accuracy. This use-case shows Chronos can easily help pytorch-forecasting speed up the training of TFT model.</p>
|
||||
|
|
@ -165,7 +228,11 @@
|
|||
<details id="hyperparameter">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/hpo/muti_objective_hpo_with_builtin_latency_tutorial.ipynb">Tune a Time Series Forecasting Model with multi-objective hyperparameter optimization.</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="hyperparameter_tuning">AutoML</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="hyperparameter_tuning">AutoML</button>
|
||||
<button value="TCNForecaster" class="roundbutton">TCNForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/hpo/muti_objective_hpo_with_builtin_latency_tutorial.ipynb">View source on GitHub</a>
|
||||
<p>In this notebook, we demostrate how to use <span>multi-objective hyperparameter optimization with built-in latency metric</span> in project <span><a href="https://github.com/intel-analytics/bigdl/tree/main/python/chronos/src/bigdl/chronos">Chronos</a></span> to do time series forecasting and achieve good tradeoff between performance and latency.</p>
|
||||
|
|
@ -175,7 +242,12 @@
|
|||
<details id="taxiDataset">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/auto_model">Auto tuning prophet on nyc taxi dataset</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="hyperparameter_tuning">AutoML</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="hyperparameter_tuning">AutoML</button>
|
||||
<button value="AutoLSTM" class="roundbutton">AutoLSTM</button>
|
||||
<button value="AutoProphet" class="roundbutton">AutoProphet</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/auto_model">View source on GitHub</a>
|
||||
<p>This example collection will demonstrate Chronos auto models (i.e. autolstm & autoprophet) perform automatic time series forecasting on nyc_taxi dataset. The auto model will search the best hyperparameters automatically.</p>
|
||||
|
|
@ -185,7 +257,12 @@
|
|||
<details id="distributedFashion">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/distributed">Use Chronos forecasters in a distributed fashion</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="distributed">distributed</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="distributed">distributed</button>
|
||||
<button value="Seq2SeqForecaster" class="roundbutton">Seq2SeqForecaster</button>
|
||||
<button value="TCNForecaster" class="roundbutton">TCNForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/distributed">View source on GitHub</a>
|
||||
<p>Users can easily train their forecasters in a distributed fashion to handle extra large dataset and speed up the process (training and data processing) by utilizing a cluster or pseudo-distribution on a single node. The functionality is powered by Project Orca.</p>
|
||||
|
|
@ -195,7 +272,13 @@
|
|||
<details id="ONNX">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/onnx">Use ONNXRuntime to speed-up forecasters' inferecing</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="onnxruntime">onnxruntime</button> <button value="hyperparameter_tuning">AutoML</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="onnxruntime">onnxruntime</button>
|
||||
<button value="hyperparameter_tuning">AutoML</button>
|
||||
<button value="AutoTSEstimator" class="roundbutton">AutoTSEstimator</button>
|
||||
<button value="Seq2SeqForecaster" class="roundbutton">Seq2SeqForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/onnx">View source on GitHub</a>
|
||||
<p>This example will demonstrate how to use ONNX to speed up the inferencing(prediction/evalution) on forecasters and AutoTSEstimator. In this example, onnx speed up the inferencing for ~4X.</p>
|
||||
|
|
@ -205,7 +288,11 @@
|
|||
<details id="Quantize">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/quantization">Quantize Chronos forecasters method to speed-up inference</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="quantization">quantization</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="quantization">quantization</button>
|
||||
<button value="TCNForecaster" class="roundbutton">TCNForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/quantization">View source on GitHub</a>
|
||||
<p>Users can easily quantize their forecasters to low precision and speed up the inference process (both throughput and latency) by on a single node. The functionality is powered by Project Nano.</p>
|
||||
|
|
@ -215,17 +302,24 @@
|
|||
<details id="SimualateTimeSeriesData">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/simulator">Simualate time series data with similar pattern as example data</a>
|
||||
<p>Tag: <button value="simulation">simulation</button></p>
|
||||
<p>Tag:
|
||||
<button value="simulation">simulation</button>
|
||||
<button value="DPGANSimulator" class="roundbutton">DPGANSimulator</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/simulator">View source on GitHub</a>
|
||||
<p>This example shows how to generate synthetic data with similar distribution as training data with the fast and easy DPGANSimulator API provided by Chronos.</p>
|
||||
</details>
|
||||
<hr>
|
||||
|
||||
<details id="TCMFForecaster">
|
||||
<details id="TCMF">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/tcmf">High dimension time series forecasting with Chronos TCMFForecaster</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="distributed">distributed</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="distributed">distributed</button>
|
||||
<button value="TCMFForecaster" class="roundbutton">TCMFForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/tcmf">View source on GitHub</a>
|
||||
<p>This example demonstrates how to use BigDL Chronos TCMFForecaster to run distributed training and inference for high dimension time series forecasting task.</p>
|
||||
|
|
@ -235,7 +329,10 @@
|
|||
<details id="PenalizeUnderestimation">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/loss/penalize_underestimation.ipynb">Penalize underestimation with LinexLoss</a>
|
||||
<p>Tag: <button value="forecast">forecast</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="TCNForecaster" class="roundbutton">TCNForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/loss/penalize_underestimation.ipynb">View source on GitHub</a>
|
||||
<p>This example demonstrates how to use TCNForecaster to penalize underestimation based on a built-in loss function LinexLoss.</p>
|
||||
|
|
@ -245,7 +342,11 @@
|
|||
<details id="GPUtrainingCPUacceleration">
|
||||
<summary>
|
||||
<a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/inference-acceleration">Accelerate the inference speed of model trained on other platform</a>
|
||||
<p>Tag: <button value="forecast">forecast</button> <button value="customized_model">customized model</button></p>
|
||||
<p>Tag:
|
||||
<button value="forecast">forecast</button>
|
||||
<button value="customized_model">customized model</button>
|
||||
<button value="TCNForecaster" class="roundbutton">TCNForecaster</button>
|
||||
</p>
|
||||
</summary>
|
||||
<img src="../../../_images/GitHub-Mark-32px.png"><a href="https://github.com/intel-analytics/BigDL/tree/main/python/chronos/example/inference-acceleration">View source on GitHub</a>
|
||||
<p>In this example, we show an example to train the model on GPU and accelerate the model by using onnxruntime on CPU.</p>
|
||||
|
|
|
|||
Loading…
Reference in a new issue