зеркало из https://github.com/microsoft/archai.git
add diagram.
This commit is contained in:
Chris Lovett
Gustavo Rosa
Родитель
e0412bd2d5
Коммит
b5cb577f52
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@ -28,6 +28,6 @@ jobs:
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output-filename: devskim-results.sarif
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- name: Upload scanning results
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uses: github/codeql-action/upload-sarif@v1
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uses: github/codeql-action/upload-sarif@v2
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with:
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sarif_file: devskim-results.sarif
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@ -0,0 +1,49 @@
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<?xml version="1.0" encoding="utf-8"?>
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<DirectedGraph xmlns="http://schemas.microsoft.com/vs/2009/dgml">
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<Nodes>
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<Node Id="..." Bounds="-891.259155273438,-397.317448474421,50,25.96" UseManualLocation="True" />
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<Node Id="...1" Bounds="-899.17041015625,-141.018983432007,50,25.96" Label="..." UseManualLocation="True" />
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<Node Id="AzureBlobStore" Bounds="-1193.71833333333,-519.02,106.03,25.96" Label="azure blob store" UseManualLocation="True" />
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<Node Id="AzureStatusTable" Bounds="-1193.71833333333,-445.02,112.973333333333,25.96" Label="azure status table" UseManualLocation="True" />
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<Node Id="JupyterNotebook" Bounds="-1396.73103800456,-328.529480438232,115.163333333333,25.96" Label="Jupyter Notebook" UseManualLocation="True" />
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<Node Id="KubernetesCluster" Bounds="-1208.17041666667,-308.193725585938,119.78,25.96" Label="Kubernetes Cluster" UseManualLocation="True" />
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<Node Id="QualcommDevice" Bounds="-769.718333333333,-534.029998779297,115.2,25.96" Label="Qualcomm device" UseManualLocation="True" />
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<Node Id="QualcommDevice1" Bounds="-771.540833333333,-453.524998168945,115.2,25.96" Label="Qualcomm device" UseManualLocation="True" />
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<Node Id="Runner.py" Bounds="-903.436666666667,-534.04,70.4533333333334,25.96" Label="runner.py" UseManualLocation="True" />
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<Node Id="Runner.py1" Bounds="-900.436666666666,-453.277548474121,70.4533333333334,25.96" Label="runner.py" UseManualLocation="True" />
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<Node Id="Runner.py2" Bounds="-899.452100016276,-313.899282537842,70.4533333333334,25.96" Label="runner.py" UseManualLocation="True" />
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<Node Id="Runner.py3" Bounds="-903.170422770182,-257.939182537842,70.4533333333334,25.96" Label="runner.py" UseManualLocation="True" />
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<Node Id="Runner.py4" Bounds="-901.449353434245,-196.979083432007,70.4533333333334,25.96" Label="runner.py" UseManualLocation="True" />
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<Node Id="SnpeQuantiation" Bounds="-760.452083333333,-251.989580438032,109.126666666667,25.96" Label="snpe quantiation" UseManualLocation="True" />
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<Node Id="SnpeQuantization" Bounds="-765.452083333333,-319.111862182617,114.553333333333,25.96" Label="snpe quantization" UseManualLocation="True" />
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<Node Id="Test_onnx" Bounds="-761.731038004557,-196.029480438032,70.6,25.96" Label="test_onnx" UseManualLocation="True" />
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<Node Id="Upload" Bounds="-1358,-486,56.9533333333334,25.96" Label="upload" UseManualLocation="True" />
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</Nodes>
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<Links>
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<Link Source="AzureBlobStore" Target="Runner.py" Bounds="-1087.68830810547,-518.622952023936,175.265282619028,9.66072548941548" />
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<Link Source="AzureBlobStore" Target="Runner.py1" Bounds="-1088.87310498647,-493.671486483734,179.682249037085,42.8785481723203" />
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<Link Source="AzureBlobStore" Target="Runner.py2" Bounds="-1123.20784148837,-493.060008544922,234.258958940773,173.798238915231" />
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<Link Source="AzureStatusTable" Target="JupyterNotebook" Bounds="-1308.85493100781,-419.059993286133,149.124540767732,86.0330250379735" />
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<Link Source="AzureStatusTable" Target="Runner.py" Bounds="-1100.05297851563,-505.965637207031,188.762878417969,60.9456481933594" />
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<Link Source="AzureStatusTable" Target="Runner.py1" Bounds="-1128.9501953125,-459.267211914063,219.597229003906,14.2472229003906" />
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<Link Source="AzureStatusTable" Target="Runner.py2" Bounds="-1125.57153320313,-419.059997558594,218.665649414063,102.314849853516" />
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<Link Source="KubernetesCluster" Target="Runner.py2" Bounds="-1088.39039550781,-300.030978319804,179.940110471371,3.61429271166003" />
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<Link Source="KubernetesCluster" Target="Runner.py3" Bounds="-1088.48298803671,-284.494147681096,176.453782358855,31.6319870791329" />
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<Link Source="KubernetesCluster" Target="Runner.py4" Bounds="-1115.36108846197,-282.233729858398,207.979818936008,82.0059196880921" />
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<Link Source="Runner.py" Target="AzureStatusTable" Bounds="-1131.49816894531,-516.163635253906,228.0615234375,69.8030700683594" />
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<Link Source="Runner.py" Target="QualcommDevice" Bounds="-832.983324788411,-521.057729225212,54.2649914734869,0.00347075341346681" />
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<Link Source="Runner.py1" Target="AzureStatusTable" Bounds="-1096.17529296875,-453.341644287109,195.738647460938,11.53271484375" />
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<Link Source="Runner.py1" Target="QualcommDevice1" Bounds="-829.983324788411,-440.436044683939,49.4425034983809,0.0808848954610539" />
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<Link Source="Runner.py2" Target="AzureStatusTable" Bounds="-1130.11853027344,-414.711029052734,230.666442871094,107.175262451172" />
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<Link Source="Runner.py2" Target="SnpeQuantization" Bounds="-828.998766682943,-303.91817535817,54.5517017881938,1.8222062802526" />
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<Link Source="Runner.py3" Target="SnpeQuantiation" Bounds="-832.717089436849,-243.665883447078,63.271065419621,2.32289765526392" />
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<Link Source="Runner.py4" Target="Test_onnx" Bounds="-830.996020100911,-183.759789886113,60.2651897407106,0.409381159973066" />
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<Link Source="Upload" Target="AzureBlobStore" Bounds="-1301.04664876302,-495.218628273773,98.4628791895163,17.2187510083821" />
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<Link Source="Upload" Target="AzureStatusTable" Bounds="-1301.04664876302,-466.951233796788,99.2138483678966,21.1438396495243" />
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</Links>
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<Properties>
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<Property Id="Bounds" DataType="System.Windows.Rect" />
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<Property Id="Label" Label="Label" Description="Displayable label of an Annotatable object" DataType="System.String" />
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<Property Id="UseManualLocation" DataType="System.Boolean" />
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</Properties>
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</DirectedGraph>
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Двоичный файл не отображается.
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После Ширина: | Высота: | Размер: 28 KiB |
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@ -5,7 +5,7 @@ one or more machines that are connected to Qualcomm 888 boards. Many thanks to
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Yatao Zhong for the original device code included in this test suite.
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The code is organized into:
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1. [Device Code](snpe/readme.md) that knows how to use the Qualcomm SNPE SDK to talk
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1. [SNPE Device Code](snpe/readme.md) that knows how to use the Qualcomm SNPE SDK to talk
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to the device, convert ONNX models to .dlc, quantize them, and test them on the board
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using the Android `adb` tool.
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@ -23,39 +23,46 @@ for Python 3.7 with the `requirements.txt` included here using:
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pip install -r requirements.txt
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```
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The SNPE SDK only works on Linux, so you need a Linux machine with this repo.
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Then follow additional setup in each of the above readmes.
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The SNPE SDK only works on Linux, so you need a Linux machine with this repo. Then follow additional
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setup in each of the above readmes.
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## Workflow
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The overall workflow looks like this. One or more Linux machines are
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setup as above and are running `azure/runner.py`. They look for work, and
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execute it in priority order where the prioritization is defined by the
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`find_work_prioritized` function in the runner. This prioritization
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maps to the columns of the status table as follows:
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The overall workflow looks like this. One or more Linux machines are setup as above and are running
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`azure/runner.py` including a Kubernetes cluster setup for quantization (see docker folder).
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Each instance of `runner.py` looks for work, and executes it in priority order where the
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prioritization is defined by the `find_work_prioritized` function in the runner. This
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prioritization maps to the columns of the status table as follows:
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1. **macs:** convert to .dlc and post Macs score and `snpe-dlc-viewer` output and do model quantization (runs on Linux) - priority 20
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1. **total_inference_avg** run `snpe_bench.py` with quantized model on Qualcomm device DSP - priority 30
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1. **f1_1k** compute f1 on quantized .dlc model on Qualcomm device DSP with a 1k test set - priority 40
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1. **f1_1k_f** compute f1 on floating point .dlc model on on Qualcomm device CPU with a 1k test set - priority 50
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1. **f1_onnx** compute f1 from onnxruntime on .onnx model on a 10k test set on Linux - priority 60
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1. **f1_10k** compute f1 on quantized model on a 10k test set - priority = total_inference_avg
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1. **f1_1k** compute f1 on quantized .dlc model on Qualcomm device DSP with a 1k test set - priority
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is the mean f1 score so that quicker models are prioritized.
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1. **f1_1k_f** compute f1 on floating point .dlc model on on Qualcomm device CPU with a 1k test set
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- priority 10 * the mean f1 score so that quicker models are prioritized.
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1. **f1_10k** compute f1 on quantized model on a 10k test set - priority = 100 * the mean f1 score
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so that quicker models are prioritized.
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Lower number means higher priority job and each machine will run the highest priority work first.
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You can override the priority of a specific job by passing a `--proprity` parameter on the `upload.py` script
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or by editing the Azure `status` table and adding a `priority` field to the JSON stored there.
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You can set any priority number you want, if you specify priority 0 it will run before anything else which
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can be handy if you have a cool new model that you want to bump to the top of the list.
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You can override the priority of a specific job by passing a `--proprity` parameter on the
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`upload.py` script or by editing the Azure `status` table and adding a `priority` field to the JSON
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stored there. You can set any priority number you want, if you specify priority 0 it will run before
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anything else which can be handy if you have a cool new model that you want to bump to the top of
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the list.
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Notice some of the above jobs can run on Linux and do not require Qualcomm device.
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So in order to maximize throughput on machines that do have a Qualcomm devices you
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can allocate other Linux machines with no Qualcomm devices to do the other work, namely, converting models,
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quantizing them, and running the `onnxruntime` test set.
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Notice some of the above jobs can run on Linux and do not require Qualcomm device. So in order to
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maximize throughput on machines that do have a Qualcomm devices you can allocate other Linux
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machines with no Qualcomm devices to do the other work, namely, converting models, quantizing them,
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and running the `onnxruntime` test set.
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Folks across your team can use the `azure/upload.py` to submit jobs and let them run. You can use
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`status.py` to monitor progress or look at the Azure `status` table. Various status messages are posted
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there so you can see which machine is doing what and is in what stage of the job.
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`status.py` to monitor progress or look at the Azure `status` table. Various status messages are
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posted there so you can see which machine is doing what and is in what stage of the job.
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Next you can go to the `notebook` page and get some pretty pictures of your Pareto Curve like this:
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