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leondavileondavi
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Merge pull request #294 from leondavi/nerlplanner_fix
[NerlnetApp] Critical bugs in distributed running
2 parents e83e7ed + f8c4989 commit 3d4006a

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‎inputJsonsFiles/ConnectionMap/conn_synt_distributed_w5_c3_6r_3s_3d.json

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{
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"connectionsMap":
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{
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"r1":["mainServer", "r2"],
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"r2":["r3", "s1"],
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"r3":["r4", "c1","s2"],
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"r4":["r5", "c2","s3"],
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"r5":["r6", "c3"],
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"r6":["r3", "r4"]
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}
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}

‎inputJsonsFiles/DistributedConfig/dc_synt_distributed_w5_c3_6r_3s_3d.json

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{
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"nerlnetSettings": {
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"frequency": "50",
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"batchSize": "100"
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},
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"mainServer": {
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"port": "8844",
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"args": ""
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},
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"apiServer": {
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"port": "8845",
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"args": ""
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},
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"devices": [
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{
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"name": "mac",
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"ipv4": "192.168.0.248",
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"entities": "c3,r1,r4,r5,r6,s1,mainServer,apiServer"
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},
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{
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"name": "jet1",
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"ipv4": "192.168.0.226",
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"entities": "c1,r2,s2"
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},
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{
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"name": "jet2",
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"ipv4": "192.168.0.228",
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"entities": "c2,r3,s3"
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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": "8900",
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"policy": "0"
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},
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{
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"name": "r2",
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"port": "8902",
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"policy": "0"
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},
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{
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"name": "r3",
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"port": "8903",
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"policy": "0"
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},
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{
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"name": "r4",
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"port": "8904",
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"policy": "0"
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},
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{
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"name": "r5",
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"port": "8905",
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"policy": "0"
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},
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{
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"name": "r6",
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"port": "8906",
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"policy": "0"
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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": "8853",
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"frequency": "50",
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"policy": "0",
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"epochs": "1",
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"type": "0"
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},
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{
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"name": "s2",
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"port": "8854",
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"frequency": "50",
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"policy": "0",
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"epochs": "1",
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"type": "0"
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},
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{
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"name": "s3",
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"port": "8855",
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"frequency": "50",
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"policy": "0",
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"epochs": "1",
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"type": "0"
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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": "8846",
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"workers": "w1,w2"
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},
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{
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"name": "c2",
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"port": "8847",
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"workers": "w3,w4"
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},
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{
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"name": "c3",
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"port": "8851",
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"workers": "w5"
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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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"model_sha": "1f3078160da415bda29a65e6c854f938c291d38166d0cb6e89cec2fd81678613"
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},
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{
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"name": "w2",
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"model_sha": "1f3078160da415bda29a65e6c854f938c291d38166d0cb6e89cec2fd81678613"
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},
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{
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"name": "w3",
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"model_sha": "1f3078160da415bda29a65e6c854f938c291d38166d0cb6e89cec2fd81678613"
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},
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{
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"name": "w4",
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"model_sha": "1f3078160da415bda29a65e6c854f938c291d38166d0cb6e89cec2fd81678613"
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},
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{
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"name": "w5",
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"model_sha": "1f3078160da415bda29a65e6c854f938c291d38166d0cb6e89cec2fd81678613"
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}
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],
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"model_sha": {
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"1f3078160da415bda29a65e6c854f938c291d38166d0cb6e89cec2fd81678613": {
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"modelType": "0",
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"_doc_modelType": " nn:0 | approximation:1 | classification:2 | forecasting:3 | image_classification:4 | text_classification:5 | text_generation:6 | auto_association:7 | autoencoder:8 | ae_classifier:9 |",
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"layersSizes": "5,16,8,3",
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"_doc_layersSizes": "List of postive integers [L0, L1, ..., LN]",
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"layerTypesList": "1,3,3,3",
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"_doc_LayerTypes": " Default:0 | Scaling:1 | CNN:2 | Perceptron:3 | Pooling:4 | Probabilistic:5 | LSTM:6 | Reccurrent:7 | Unscaling:8 | Bounding:9 |",
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"layers_functions": "1,7,7,11",
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"_doc_layers_functions_activation": " Threshold:1 | Sign:2 | Logistic:3 | Tanh:4 | Linear:5 | ReLU:6 | eLU:7 | SeLU:8 | Soft-plus:9 | Soft-sign:10 | Hard-sigmoid:11 |",
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"_doc_layer_functions_pooling": " none:1 | Max:2 | Avg:3 |",
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"_doc_layer_functions_probabilistic": " Binary:1 | Logistic:2 | Competitive:3 | Softmax:4 |",
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"_doc_layer_functions_scaler": " none:1 | MinMax:2 | MeanStd:3 | STD:4 | Log:5 |",
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"lossMethod": "2",
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"_doc_lossMethod": " SSE:1 | MSE:2 | NSE:3 | MinkowskiE:4 | WSE:5 | CEE:6 |",
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"lr": "0.01",
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"_doc_lr": "Positve float",
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"epochs": "1",
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"_doc_epochs": "Positve Integer",
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"optimizer": "5",
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"_doc_optimizer": " GD:0 | CGD:1 | SGD:2 | QuasiNeuton:3 | LVM:4 | ADAM:5 |",
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"optimizerArgs": "none",
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"_doc_optimizerArgs": "String",
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"infraType": "0",
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"_doc_infraType": " opennn:0 | wolfengine:1 |",
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"distributedSystemType": "0",
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"_doc_distributedSystemType": " none:0 | fedClientAvg:1 | fedServerAvg:2 |",
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"distributedSystemArgs": "none",
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"_doc_distributedSystemArgs": "String",
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"distributedSystemToken": "none",
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"_doc_distributedSystemToken": "Token that associates distributed group of workers and parameter-server"
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}
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}
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}

‎inputJsonsFiles/experimentsFlow/exp_synt_distributed_w5_c3_6r_3s_3d.json

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{
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"experimentName": "synthetic_3_gausians",
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"batchSize": 100,
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"csvFilePath": "/tmp/nerlnet/data/NerlnetData-master/nerlnet/synthetic/synthetic_full.csv",
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"numOfFeatures": "5",
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"numOfLabels": "3",
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"headersNames": "Norm(0:1),Norm(4:1),Norm(10:3)",
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"Phases":
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[
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{
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"phaseName": "training_phase",
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"phaseType": "training",
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"sourcePieces":
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[
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{
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"sourceName": "s1",
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"startingSample": "0",
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"numOfBatches": "200",
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"workers": "w1,w2"
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},
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{
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"sourceName": "s2",
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"startingSample": "20000",
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"numOfBatches": "200",
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"workers": "w3,w4"
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},
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{
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"sourceName": "s3",
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"startingSample": "40000",
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"numOfBatches": "200",
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"workers": "w5,w1,w2,w3,w4"
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}
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]
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},
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{
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"phaseName": "prediction_phase",
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"phaseType": "prediction",
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"sourcePieces":
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[
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{
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"sourceName": "s1",
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"startingSample": "40000",
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"numOfBatches": "300",
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"workers": "w1,w2,w3,w4"
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},
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{
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"sourceName": "s2",
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"startingSample": "40000",
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"numOfBatches": "300",
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"workers": "w1,w2,w3,w4,w5"
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},
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{
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"sourceName": "s3",
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"startingSample": "40000",
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"numOfBatches": "300",
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"workers": "w5"
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}
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]
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}
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]
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}
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‎src_erl/NerlnetApp/src/Init/jsonHandler.erl

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ApplicationPid ! {jsonAddress,{lists:nth(1, Data),lists:nth(2, Data)}};
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_Other ->
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{ok,Body,_} = cowboy_req:read_body(Req0), %% shouldn't be here, files expected
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io:format("got Req: ~p~nData: ~p~n",[Req0, Body])
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io:format("Error - Got an unknown request: ~p~nData: ~p~n",[Req0, Body])
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end,
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Reply = io_lib:format("nerlnet starting", []),

‎src_erl/NerlnetApp/src/MainServer/ackHandler.erl

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dataReady -> gen_server:cast(Main_genserver_Pid, {sourceAckDataReady,Body}); %% when source data is ready
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sourceDone -> gen_server:cast(Main_genserver_Pid, {sourceDone,Body}); %% when source finished casting
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clientAck -> gen_server:cast(Main_genserver_Pid, {clientAck,Body}); %% when client received message (new state)
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jsonReceived -> gen_server:cast(Main_genserver_Pid, {jsonReceived,Body}) %% when other devices got the json and ready to start
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jsonReceived -> gen_server:cast(Main_genserver_Pid, {jsonReceived,Body}); %% when other devices got the json and ready to start
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apiserver_ack_validation -> Main_genserver_Pid ! {apiserver_ack_validation, Body} % This ack validates transmission with flask
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end,
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Reply = io_lib:format("Body Received: ~p ~n ", [Body]),
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Req = cowboy_req:reply(200,

‎src_erl/NerlnetApp/src/MainServer/initHandler.erl

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Decoded_body = binary_to_list(Body),
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%Decoded_body = read_all_data(Req0),
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% io:format("GOT DATA: ~p~n",[Decoded_body]),
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[SourceName, _WorkersStr, _Epochs, _Data] = string:split(Decoded_body, "#", all),
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[Index, TotalSources, SourceName, _WorkersStr, _Epochs, _Data] = string:split(Decoded_body, "#", all),
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%WorkersList = string:split(WorkersStr, ",", all),
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gen_server:cast(Main_genServer_Pid,{initCSV, SourceName, Body}),
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gen_server:cast(Main_genServer_Pid,{initCSV, Index, TotalSources, SourceName, Body}),
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%[Source|WorkersAndInput] = re:split(binary_to_list(Body), "#", [{return, list}]),
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%{Workers,SourceData} = getWorkerInput(WorkersAndInput,[]),
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