train_input.json

The train_input.json file is passed to custom learning blocks as the value for the --input-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
}