train_input.json

The train_input.json file is passed to custom learning blocks as the value for the --input-file <file> argument. It contains configuration information for model training options that you may want to use within your training script, if applicable. The specification is shown below.

File structure

type CreateKerasTrainModelOptions = {
    classes: string[],
    mode: 'classification' | 'regression' | 'object-detection' | 'visual-anomaly' | 'anomaly-gmm';
    printHWInfo: boolean | undefined,
    inputShapeString: string,
    yType: 'npy' | 'structured';
    trainTestSplit: number,
    stratifiedTrainTest: boolean,
    onlineDspConfig: OnlineDspConfig | undefined;
    convertInt8: boolean,
    profileInt8: boolean,
    skipEmbeddingsAndMemory: boolean,
    objectDetectionLastLayer: 'mobilenet-ssd' | 'fomo' | 'yolov2-akida' | 'yolov5' | 'yolov5v5-drpai' |
    'yolox' | 'yolov7' | 'tao-retinanet' | 'tao-ssd' | 'tao-yolov3' | 'tao-yolov4' | undefined;
    objectDetectionAugmentation: boolean | undefined,
    objectDetectionBatchSize: number | undefined,
    syntiantTarget?: boolean,
    maxTrainingTimeSeconds: number,
    remainingGpuComputeTimeSeconds: number,
    isEnterpriseProject: boolean,
    flattenDataset: boolean,
    akidaModel: boolean,
    akidaEdgeModel: boolean,
    skipMemoryProfiling: boolean,
    tensorboardLogging: boolean,
    customValidationSplit: boolean,
    validationMetadataKey?: string,
    customVariantsToProfile: CustomVariantInferenceJobModelVariant[] | undefined;
};

File example

{
    "classes": [
        "idle",
        "snake",
        "updown",
        "wave"
    ],
    "mode": "classification",
    "printHWInfo": false,
    "inputShapeString": "(39,)",
    "yType": "npy",
    "trainTestSplit": 0.2,
    "stratifiedTrainTest": false,
    "convertInt8": true,
    "profileInt8": true,
    "skipEmbeddingsAndMemory": false,
    "objectDetectionAugmentation": false,
    "syntiantTarget": false,
    "maxTrainingTimeSeconds": 604800,
    "remainingGpuComputeTimeSeconds": null,
    "isEnterpriseProject": true,
    "flattenDataset": false,
    "akidaModel": false,
    "akidaEdgeModel": false,
    "skipMemoryProfiling": false,
    "tensorboardLogging": false,
    "customValidationSplit": false
}