feat: csv chart automation

docs/charts-csv-to-json/.gitignore

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+node_modules

docs/charts-csv-to-json/README.md

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+# csv to json conversion module for benchmarks
+
+## Application logic (draft)
+
+The principle is to pre-generate the conversion of the benchmarks csv to json so that the client only has to consume the data.
+
+### Client side
+
+On the client side, we only fetch converted json data, hosted on the static github pages. 
+
+Note:
+- In the case of a download failure, default json data, with a speed of 0, is generated to avoid any anomalies and display the problem.
+- However, a real data json file, the last one generated, should always be present on the site and accessible through the fetch.
+
+### Node module for converting csv to json
+
+First it is necessary to copy the new benchmarks csv file into the `input` directory of the module.
+The module reads and parses the `csv` file and generates a `benchmark.json` file in the `/javascripts` directory of the site, before it is generated into a static site and deployed.
+
+If an error occurs during this process, no json file will be generated, only error logs.
+The default json file, or the previously generated file, will be used. In the worst case, 0 data will be displayed by the client.
+
+## Point of attention to solve
+
+Beware, the integration of the module in the `/docs` directory generates some side effects.
+Some `node_modules` files potentially conflict with the site build (`The following pages exist in the docs directory, but are not included in the "nav" configuration:`) and pollute the static site `sitemap.xml`.
+
+A solution is to be found.
+
+## To do
+
+- Find a solution to not pollute the build and the sitemap.xml
+- Integrate the conversion in the build and deployment chain
+- Perform unit tests to convert csv to json
+- Bonus: typescript integration with Vite.
+
+## Node version used
+
+`lts/hydrogen`
+

docs/charts-csv-to-json/app.js

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+import fs from 'fs';
+import Papa from 'papaparse'
+
+const filepath = 'input/data.csv'
+const resultFilepath = '../javascripts/benchmark.json'
+const header = 'engine,batch,sequence,speed'
+
+function writeResult(result) {
+    try {
+        fs.writeFileSync(resultFilepath, result);
+        console.log('csv conversion has been saved!');
+    } catch (error) {
+        console.error(`an error occurred while writing the json file :\n
+            ${error}`);
+    }
+}
+
+function convertCSVtoJSON (){
+    try {
+        const csv = fs.readFileSync(filepath, 'utf8');
+        const csvWithHeader = `${header}\n${csv.trim()}`;
+
+        Papa.parse(csvWithHeader, {
+            complete: function (json) {
+                console.log('csv conversion complete!');
+                writeResult(JSON.stringify(json.data))
+            },
+            error: function (error) {
+                console.error(`an error occurred while parsing the csv file :\n
+                ${JSON.stringify(error)}`);
+            },
+            delimiter: ',',
+            header: true,
+            dynamicTyping: true,
+        })
+    } catch (error) {
+        console.error(`an error occurred while reading the csv file :\n
+        ${JSON.stringify(error)}`);
+    }
+}
+
+convertCSVtoJSON();

docs/charts-csv-to-json/input/data.csv

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+Inductor,1,16,0.0018
+Inductor,1,32,0.0020
+Inductor,1,64,0.0020
+Inductor,1,128,0.0025
+Inductor,1,256,0.0030
+Inductor,1,384,0.0036
+Inductor,1,512,0.0048
+Inductor,8,16,0.0023
+Inductor,8,32,0.0027
+Inductor,8,64,0.0039
+Inductor,8,128,0.0065
+Inductor,8,256,0.0117
+Inductor,8,384,0.0156
+Inductor,8,512,0.0212
+Inductor,32,16,0.0039
+Inductor,32,32,0.0062
+Inductor,32,64,0.0108
+Inductor,32,128,0.0177
+Inductor,32,256,0.0357
+AITemplate,1,16,0.0012
+AITemplate,1,32,0.0012
+AITemplate,1,64,0.0013
+AITemplate,1,128,0.0015
+AITemplate,1,256,0.0020
+AITemplate,1,384,0.0022
+AITemplate,1,512,0.0031
+AITemplate,8,16,0.0013
+AITemplate,8,32,0.0017
+AITemplate,8,64,0.0026
+AITemplate,8,128,0.0043
+AITemplate,8,256,0.0076
+AITemplate,8,384,0.0115
+AITemplate,8,512,0.0149
+AITemplate,32,16,0.0026
+AITemplate,32,32,0.0043
+AITemplate,32,64,0.0073
+AITemplate,32,128,0.0127
+AITemplate,32,256,0.0242
+TensorRT,1,16,0.0010
+TensorRT,1,32,0.0010
+TensorRT,1,64,0.0011
+TensorRT,1,128,0.0013
+TensorRT,1,256,0.0016
+TensorRT,1,384,0.0026
+TensorRT,1,512,0.0026
+TensorRT,8,16,0.0011
+TensorRT,8,32,0.0015
+TensorRT,8,64,0.0019
+TensorRT,8,128,0.0036
+TensorRT,8,256,0.0064
+TensorRT,8,384,0.0139
+TensorRT,8,512,0.0139
+TensorRT,32,16,0.0020
+TensorRT,32,32,0.0031
+TensorRT,32,64,0.0054
+TensorRT,32,128,0.0103
+TensorRT,32,256,0.0210
+DeepSpeed,1,16,0.0009
+DeepSpeed,1,32,0.0008
+DeepSpeed,1,64,0.0009
+DeepSpeed,1,128,0.0012
+DeepSpeed,1,256,0.0019
+DeepSpeed,1,384,0.0023
+DeepSpeed,1,512,0.0032
+DeepSpeed,8,16,0.0011
+DeepSpeed,8,32,0.0016
+DeepSpeed,8,64,0.0025
+DeepSpeed,8,128,0.0051
+DeepSpeed,8,256,0.0106
+DeepSpeed,8,384,0.0161
+DeepSpeed,8,512,0.0219
+DeepSpeed,32,16,0.0025
+DeepSpeed,32,32,0.0050
+DeepSpeed,32,64,0.0097
+DeepSpeed,32,128,0.0176
+DeepSpeed,32,256,0.0374
+Baseline,1,16,0.00795173406600952
+Cuda graphs,1,16,0.00106519103050232
+Nvfuser,1,16,0.00355699706077576
+Kernl,1,16,0.000644994974136352
+ONNX Runtime,1,16,0.00239814496040344
+Baseline,1,128,0.00808307266235352
+Cuda graphs,1,128,0.00161654901504517
+Nvfuser,1,128,0.00363480401039124
+Kernl,1,128,0.00145257699489594
+ONNX Runtime,1,128,0.00245641589164734
+Baseline,1,256,0.00817633533477783
+Cuda graphs,1,256,0.00291786408424377
+Nvfuser,1,256,0.00357921099662781
+Kernl,1,256,0.00198752295970917
+ONNX Runtime,1,256,0.00270905590057373
+Baseline,1,384,0.00817633533477783
+Cuda graphs,1,384,0.00291786408424377
+Nvfuser,1,384,0.00357921099662781
+Kernl,1,384,0.00198752295970917
+ONNX Runtime,1,384,0.00270905590057373
+Baseline,1,512,0.00850767040252686
+Cuda graphs,1,512,0.00442398595809937
+Nvfuser,1,512,0.00375320100784302
+Kernl,1,512,0.00254801301956177
+ONNX Runtime,1,512,0.003773845911026
+Baseline,8,16,0.00851109409332275
+Cuda graphs,8,16,0.00164371204376221
+Nvfuser,8,16,0.00403249311447144
+Kernl,8,16,0.00136490595340729
+ONNX Runtime,8,16,0.0024571259021759
+Baseline,8,128,0.00816122531890869
+Cuda graphs,8,128,0.00626455688476562
+Nvfuser,8,128,0.00544208192825317
+Kernl,8,128,0.00442465305328369
+ONNX Runtime,8,128,0.00574171686172485
+Baseline,8,256,0.0135426902770996
+Cuda graphs,8,256,0.0131534404754639
+Nvfuser,8,256,0.0103799543380737
+Kernl,8,256,0.00705874919891357
+ONNX Runtime,8,256,0.0111610059738159
+Baseline,8,384,0.0192422294616699
+Cuda graphs,8,384,0.0189292831420898
+Nvfuser,8,384,0.0148918361663818
+Kernl,8,384,0.0105360803604126
+ONNX Runtime,8,384,0.0155409851074219
+Baseline,8,512,0.0277956104278564
+Cuda graphs,8,512,0.0273300457000732
+Nvfuser,8,512,0.0203310241699219
+Kernl,8,512,0.0137086231231689
+ONNX Runtime,8,512,0.0207632541656494
+Baseline,32,16,0.00819225311279297
+Cuda graphs,32,16,0.00312871694564819
+Nvfuser,32,16,0.00410407495498657
+Kernl,32,16,0.00239948511123657
+ONNX Runtime,32,16,0.00305436611175537
+Baseline,32,128,0.019238655090332
+Cuda graphs,32,128,0.0191719589233398
+Nvfuser,32,128,0.0172065410614014
+Kernl,32,128,0.0130825481414795
+ONNX Runtime,32,128,0.017692741394043
+Baseline,32,256,0.0431598892211914
+Cuda graphs,32,256,0.042514949798584
+Nvfuser,32,256,0.0349777145385742
+Kernl,32,256,0.025843318939209
+ONNX Runtime,32,256,0.0348241157531738