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leondavihalfway258
leondavi
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halfway258
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Worker ets (#185)
* [WORKER] Clean redundatnt prints and code * [GENWORKER] WIP implement workers and change worker to ets support * [GENWORKER] WIP add new hrl * update phase * federated flow * federated avg weights * full federated flow * json stantard * final federated flow * CI pass? * CI pass * comments * working CI version * federeated nif compatability * remove print * synth exp --------- Co-authored-by: halfway258 <[email protected]>
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‎NerlnetTest.sh

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#!/bin/bash
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# set -e # TODO remove
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# cd src_py/apiServer
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# python3 experiment_flow_test.py
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# cd -
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function print()
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{

‎inputJsonFiles/Architecture/ArchExample.json

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{
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"NerlNetSettings":
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{
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"frequency": "5",
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"batchSize": "10"
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}
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,
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"devices": [
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{
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"host": "192.168.0.108",
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"entities": "mainServer,c1,c2,s1,r1,nerlGUI,apiServer",
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"desc": "entity names are atoms (start with lowercase letter). special entities have immutable names"
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}
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],
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"apiServer":
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{
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"host": "192.168.0.108",
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"port": "8095",
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"args": ""
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}
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,
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"nerlGUI":
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{
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"host": "192.168.0.108",
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"port": "8096",
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"args": ""
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}
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,
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"mainServer":
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{
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"host": "192.168.0.108",
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"port": "8080",
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"args": ""
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}
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,
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"workers": [
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{
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"name": "w1",
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"modelType_desc": "E_APPROXIMATION = 1, E_CLASSIFICATION = 2, E_FORECASTING = 3 , E_NCODER_DECODER = 4, E_CUSTOMNN = 5, E_AE = 6, E_AEC = 7, E_FEDERATED_CLIENT = 8, E_FEDERATED_SERVER = 9",
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"modelType": "8",
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"scalingMethod_desc": "Non-Scaling = 1 , MinimumMaximum = 2 , MeanStandardDeviation = 3 , StandardDeviation = 4 , Logarithm = 5",
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"scalingMethod": "1",
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"layerTypesList_desc": "E_LAYER_TYPE_DEFAULT(perceptron) = 0, E_LAYER_TYPE_SCALING = 1, E_LAYER_TYPE_CONVOLUTIONAL = 2 , E_LAYER_TYPE_PERCEPTRON = 3 , E_LAYER_TYPE_POOLING = 4 , E_LAYER_TYPE_PROBABILISTIC = 5 , E_LAYER_TYPE_LSTM = 6 , E_LAYER_TYPE_RECURRENT = 7 , E_LAYER_TYPE_UNSCALING = 8 , E_LAYER_TYPE_BOUNDING = 9 ",
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"layerTypesList": "[4,1,1,1,0,0,0]",
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"layersSizes": "[4,1,1,13,10,6,5]",
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"layersActivationFunctions_desc": "Threshold = 1, SymmetricThreshold = 2 ,Logistic = 3 ,HyperbolicTangent = 4 ,Linear = 5,RectifiedLinear = 6 ,ExponentialLinear = 7 ,ScaledExponentialLinear = 8 ,SoftPlus = 9 ,SoftSign = 10 ,HardSigmoid = 11",
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"layersActivationFunctions": "[4,1,1,6,6,11,11]",
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"lossMethod_desc": "SUM_SQUARED_ERROR = 1, MSE = 2 /* MSE - Mean Squared Error */, E_LOSS_METHOD_NSE = 3 /* NSE - Normalized Squared Error */, MINKOWSKI_ERROR = 4, WSE = 5 /* WSE - Weighted Squared Error */, CEE = 6 /* CEE - Cross Entropy Error */",
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"lossMethod": "2",
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"learningRate": "0.01",
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"optimizer_desc": "0- NONE, 1- SGD, 2- MINI_BATCH_SGD, 3- MOMENTUM, 4- NAG, 5- ADAGRAD, 6- ADAM",
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"optimizer":"6",
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"federatedServer_desc": "federatedServer = name of fedServerWorker (if modelType==8), syncCount = after how many batches to sync/update weights",
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"federatedServer": "w2",
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"syncCount": "10"
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},
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{
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"name": "w2",
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"_comment0": "E_APPROXIMATION = 1, E_CLASSIFICATION = 2, E_FORECASTING = 3 , E_NCODER_DECODER = 4, E_CUSTOMNN = 5, E_AE = 6, E_AEC = 7, E_FEDERATED_CLIENT = 8, E_FEDERATED_SERVER = 9",
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"modelType": "9",
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"scalingType_comment1": "Non-Scaling = 1 , MinimumMaximum = 2 , MeanStandardDeviation = 3 , StandardDeviation = 4 , Logarithm = 5",
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"scalingMethod": "1",
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"_commnet_layer_type": "E_LAYER_TYPE_DEFAULT(perceptron) = 0, E_LAYER_TYPE_SCALING = 1, E_LAYER_TYPE_CONVOLUTIONAL = 2 , E_LAYER_TYPE_PERCEPTRON = 3 , E_LAYER_TYPE_POOLING = 4 , E_LAYER_TYPE_PROBABILISTIC = 5 , E_LAYER_TYPE_LSTM = 6 , E_LAYER_TYPE_RECURRENT = 7 , E_LAYER_TYPE_UNSCALING = 8 , E_LAYER_TYPE_BOUNDING = 9 ",
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"layerTypesList": "[4,1,1,1,0,0,0]",
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"layersSizes": "[4,1,1,13,10,6,5]",
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"_comment2": "Activation functions explain: Threshold = 1, SymmetricThreshold = 2 ,Logistic = 3 ,HyperbolicTangent = 4 ,Linear = 5,RectifiedLinear = 6 ,ExponentialLinear = 7 ,ScaledExponentialLinear = 8 ,SoftPlus = 9 ,SoftSign = 10 ,HardSigmoid = 11",
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"layersActivationFunctions": "[4,1,1,6,6,11,11]",
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"_comment4": "LOSS: SUM_SQUARED_ERROR = 1, MSE = 2 /* MSE - Mean Squared Error */, E_LOSS_METHOD_NSE = 3 /* NSE - Normalized Squared Error */, MINKOWSKI_ERROR = 4, WSE = 5 /* WSE - Weighted Squared Error */, CEE = 6 /* CEE - Cross Entropy Error */",
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"lossMethod": "2",
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"_comment5": "TODO explain learningRate",
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"learningRate": "0.01",
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"_comment3": "OptimizerExplain: 0- NONE, 1- SGD, 2- MINI_BATCH_SGD, 3- MOMENTUM, 4- NAG, 5- ADAGRAD, 6- ADAM",
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"optimizer":"6",
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"_comment6": "Federated options: federatedServer = name of fedServerWorker, syncCount = after how many batches to sync/update weights",
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"federatedServer": "w2",
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"syncCount": "10"
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}
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],
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"clients": [
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{
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"name": "c1",
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"port": "8081",
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"workers": "w1"
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},
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{
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"name": "c2",
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"port": "8082",
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"workers": "w2"
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}
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],
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"sources": [
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{
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"name": "s1",
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"port": "8091",
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"method_desc": "methods allowed: '1': sends each sample to all clients listed, '2': round robin between all clients",
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"method": "1"
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}
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],
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"routers": [
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{
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"name": "r1",
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"port": "8084"
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}
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]
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}

‎inputJsonFiles/Architecture/arch_1PC1WorkerSynth.json

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{
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"NerlNetSettings":
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{
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"frequency": "5",
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"batchSize": "50"
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}
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,
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"devices": [
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{
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"host": "192.168.0.108",
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"entities": "mainServer,c1,s1,r1,nerlGUI,apiServer"
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}
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],
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"apiServer":
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{
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"host": "192.168.0.108",
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"port": "8095",
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"args": ""
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}
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,
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"nerlGUI":
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{
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"host": "192.168.0.108",
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"port": "8096",
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"args": ""
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}
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,
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"mainServer":
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{
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"host": "192.168.0.108",
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"port": "8080",
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"args": ""
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}
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,
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"workers": [
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{
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"name": "w1",
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"modelId": "1",
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"_comment0": "E_APPROXIMATION = 1, E_CLASSIFICATION = 2, E_FORECASTING = 3 , E_NCODER_DECODER = 4, E_CUSTOMNN = 5, E_AE = 6, E_AEC = 7",
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"modelType": "5",
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"scalingType_comment1": "Non-Scaling = 1 , MinimumMaximum = 2 , MeanStandardDeviation = 3 , StandardDeviation = 4 , Logarithm = 5",
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"scalingMethod": "1",
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"_commnet_layer_type": "E_LAYER_TYPE_DEFAULT(perceptron) = 0, E_LAYER_TYPE_SCALING = 1, E_LAYER_TYPE_CONVOLUTIONAL = 2 , E_LAYER_TYPE_PERCEPTRON = 3 , E_LAYER_TYPE_POOLING = 4 , E_LAYER_TYPE_PROBABILISTIC = 5 , E_LAYER_TYPE_LSTM = 6 , E_LAYER_TYPE_RECURRENT = 7 , E_LAYER_TYPE_UNSCALING = 8 , E_LAYER_TYPE_BOUNDING = 9 ",
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"layerTypesList": "[3,1,1,1,0,0]",
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"layersSizes": "[3,1,1,5,3,3]",
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"_comment2": "Activation functions explain: Threshold = 1, SymmetricThreshold = 2 ,Logistic = 3 ,HyperbolicTangent = 4 ,Linear = 5,RectifiedLinear = 6 ,ExponentialLinear = 7 ,ScaledExponentialLinear = 8 ,SoftPlus = 9 ,SoftSign = 10 ,HardSigmoid = 11",
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"layersActivationFunctions": "[3,1,1,6,6,11]",
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"federatedMode": "0",
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"countLimit": "10",
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"_comment4": "LOSS: SUM_SQUARED_ERROR = 1, MSE = 2 /* MSE - Mean Squared Error */, E_LOSS_METHOD_NSE = 3 /* NSE - Normalized Squared Error */, MINKOWSKI_ERROR = 4, WSE = 5 /* WSE - Weighted Squared Error */, CEE = 6 /* CEE - Cross Entropy Error */",
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"lossMethod": "2",
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"_comment5": "TODOTODO explain learningRate",
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"learningRate": "0.01",
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"_comment3": "OptimizerExplain: 0- NONE, 1- SGD, 2- MINI_BATCH_SGD, 3- MOMENTUM, 4- NAG, 5- ADAGRAD, 6- ADAM",
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"optimizer":"6"
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}
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],
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"clients": [
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{
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"name": "c1",
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"port": "8081",
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"workers": "w1"
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}
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],
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"sources": [
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{
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"name": "s1",
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"port": "8091",
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"method": "1",
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"COMMENTS": "method allowed: '1': sends each exapme to all clients listed, '2': round robin between all clients"
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}
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],
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"routers": [
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{
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"name": "r1",
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"port": "8084"
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}
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]
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}

‎inputJsonFiles/Architecture/arch_1PC2Clients4WorkerHealth_david.json

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{
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"NerlNetSettings":
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{
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"frequency": "5",
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"frequency": "100",
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"batchSize": "10"
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}
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"_comment5": "TODOTODO explain learningRate",
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"learningRate": "0.01",
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"_comment3": "OptimizerExplain: 0- NONE, 1- SGD, 2- MINI_BATCH_SGD, 3- MOMENTUM, 4- NAG, 5- ADAGRAD, 6- ADAM",
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"optimizer":"6",
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"features": "13",
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"labels": "5"
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"optimizer":"6"
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},
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{
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"name": "w2",

‎inputJsonFiles/Architecture/arch_1PC2WorkerFed.json

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{
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"NerlNetSettings":
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{
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"frequency": "5",
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"batchSize": "10"
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}
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,
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"devices": [
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{
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"host": "192.168.0.108",
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"entities": "mainServer,c1,c2,s1,r1,nerlGUI,apiServer"
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}
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],
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"apiServer":
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{
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"host": "192.168.0.108",
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"port": "8095",
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"args": ""
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}
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,
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"nerlGUI":
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{
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"host": "192.168.0.108",
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"port": "8096",
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"args": ""
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}
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,
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"mainServer":
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{
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"host": "192.168.0.108",
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"port": "8080",
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"args": ""
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}
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,
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"workers": [
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{
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"name": "w1",
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"_comment0": "E_APPROXIMATION = 1, E_CLASSIFICATION = 2, E_FORECASTING = 3 , E_NCODER_DECODER = 4, E_CUSTOMNN = 5, E_AE = 6, E_AEC = 7, E_FEDERATED_CLIENT = 8, E_FEDERATED_SERVER = 9",
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"modelType": "8",
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"scalingType_comment1": "Non-Scaling = 1 , MinimumMaximum = 2 , MeanStandardDeviation = 3 , StandardDeviation = 4 , Logarithm = 5",
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"scalingMethod": "1",
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"_commnet_layer_type": "E_LAYER_TYPE_DEFAULT(perceptron) = 0, E_LAYER_TYPE_SCALING = 1, E_LAYER_TYPE_CONVOLUTIONAL = 2 , E_LAYER_TYPE_PERCEPTRON = 3 , E_LAYER_TYPE_POOLING = 4 , E_LAYER_TYPE_PROBABILISTIC = 5 , E_LAYER_TYPE_LSTM = 6 , E_LAYER_TYPE_RECURRENT = 7 , E_LAYER_TYPE_UNSCALING = 8 , E_LAYER_TYPE_BOUNDING = 9 ",
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"layerTypesList": "[4,1,1,1,0,0,0]",
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"layersSizes": "[4,1,1,13,10,6,5]",
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"_comment2": "Activation functions explain: Threshold = 1, SymmetricThreshold = 2 ,Logistic = 3 ,HyperbolicTangent = 4 ,Linear = 5,RectifiedLinear = 6 ,ExponentialLinear = 7 ,ScaledExponentialLinear = 8 ,SoftPlus = 9 ,SoftSign = 10 ,HardSigmoid = 11",
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"layersActivationFunctions": "[4,1,1,6,6,11,11]",
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"_comment4": "LOSS: SUM_SQUARED_ERROR = 1, MSE = 2 /* MSE - Mean Squared Error */, E_LOSS_METHOD_NSE = 3 /* NSE - Normalized Squared Error */, MINKOWSKI_ERROR = 4, WSE = 5 /* WSE - Weighted Squared Error */, CEE = 6 /* CEE - Cross Entropy Error */",
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"lossMethod": "2",
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"_comment5": "TODO explain learningRate",
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"learningRate": "0.01",
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"_comment3": "OptimizerExplain: 0- NONE, 1- SGD, 2- MINI_BATCH_SGD, 3- MOMENTUM, 4- NAG, 5- ADAGRAD, 6- ADAM",
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"optimizer":"6",
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"_comment6": "Federated options: federatedServer = name of fedServerWorker, syncCount = after how many batches to sync/update weights",
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"federatedServer": "w2",
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"syncCount": "10"
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},
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{
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"name": "w2",
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"_comment0": "E_APPROXIMATION = 1, E_CLASSIFICATION = 2, E_FORECASTING = 3 , E_NCODER_DECODER = 4, E_CUSTOMNN = 5, E_AE = 6, E_AEC = 7, E_FEDERATED_CLIENT = 8, E_FEDERATED_SERVER = 9",
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"modelType": "9",
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"scalingType_comment1": "Non-Scaling = 1 , MinimumMaximum = 2 , MeanStandardDeviation = 3 , StandardDeviation = 4 , Logarithm = 5",
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"scalingMethod": "1",
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"_commnet_layer_type": "E_LAYER_TYPE_DEFAULT(perceptron) = 0, E_LAYER_TYPE_SCALING = 1, E_LAYER_TYPE_CONVOLUTIONAL = 2 , E_LAYER_TYPE_PERCEPTRON = 3 , E_LAYER_TYPE_POOLING = 4 , E_LAYER_TYPE_PROBABILISTIC = 5 , E_LAYER_TYPE_LSTM = 6 , E_LAYER_TYPE_RECURRENT = 7 , E_LAYER_TYPE_UNSCALING = 8 , E_LAYER_TYPE_BOUNDING = 9 ",
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"layerTypesList": "[4,1,1,1,0,0,0]",
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"layersSizes": "[4,1,1,13,10,6,5]",
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"_comment2": "Activation functions explain: Threshold = 1, SymmetricThreshold = 2 ,Logistic = 3 ,HyperbolicTangent = 4 ,Linear = 5,RectifiedLinear = 6 ,ExponentialLinear = 7 ,ScaledExponentialLinear = 8 ,SoftPlus = 9 ,SoftSign = 10 ,HardSigmoid = 11",
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"layersActivationFunctions": "[4,1,1,6,6,11,11]",
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"_comment4": "LOSS: SUM_SQUARED_ERROR = 1, MSE = 2 /* MSE - Mean Squared Error */, E_LOSS_METHOD_NSE = 3 /* NSE - Normalized Squared Error */, MINKOWSKI_ERROR = 4, WSE = 5 /* WSE - Weighted Squared Error */, CEE = 6 /* CEE - Cross Entropy Error */",
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"lossMethod": "2",
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"_comment5": "TODO explain learningRate",
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"learningRate": "0.01",
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"_comment3": "OptimizerExplain: 0- NONE, 1- SGD, 2- MINI_BATCH_SGD, 3- MOMENTUM, 4- NAG, 5- ADAGRAD, 6- ADAM",
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"optimizer":"6",
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"_comment6": "Federated options: federatedServer = name of fedServerWorker, syncCount = after how many batches to sync/update weights",
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"federatedServer": "w2",
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"syncCount": "10"
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}
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],
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"clients": [
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{
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"name": "c1",
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"port": "8081",
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"workers": "w1"
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},
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{
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"name": "c2",
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"port": "8082",
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"workers": "w2"
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}
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],
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"sources": [
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{
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"name": "s1",
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"port": "8091",
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"COMMENTS": "method allowed: '1': sends each exapme to all clients listed, '2': round robin between all clients",
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"method": "1"
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}
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],
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"routers": [
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{
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"name": "r1",
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"port": "8084"
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}
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]
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}

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