import { ModelInformation, RunnerClassifyResponseSuccess, RunnerSetThresholdResponse, SetRunnerBlockThreshold } from './linux-impulse-runner-types';
export * from './linux-impulse-runner-types';
export declare class LinuxImpulseRunner {
    private _path;
    private _runner;
    private _helloResponse;
    private _runnerEe;
    private _id;
    private _stopped;
    private _socket;
    private _inputShm;
    private _freeformOutputShm;
    private _verbose;
    private _shmBehavior;
    private _valgrind;
    private _stdout;
    private _tempDir;
    /**
     * Start a new impulse runner
     * @param path Path to the runner's executable
     */
    constructor(path: string, opts?: {
        valgrind?: boolean;
        verbose?: boolean;
        shmBehavior?: 'auto' | 'always' | 'never';
        throwOnShmInitFailed?: boolean;
    });
    /**
     * Initialize the runner
     * This returns information about the model
     */
    init(modelPath?: string): Promise<ModelInformation>;
    /**
     * Whether we should warm up the GPU for this model
     * @param modelInfo Model information returned from EIM (hello message response)
     */
    shouldWarmGpu(modelInfo: ModelInformation): boolean;
    /**
     * Stop the classification process
     */
    stop(): Promise<string>;
    /**
     * Get information about the model, this is only available
     * after the runner has been initialized
     */
    getModel(): ModelInformation;
    /**
     * Classify data
     * @param data An array of numbers, already formatted according to the rules in
     *             https://docs.edgeimpulse.com/docs/running-your-impulse-locally-1
     */
    classify(data: number[], timeout?: number): Promise<RunnerClassifyResponseSuccess>;
    /**
     * Classify data (continuous mode, pass in slice_size data)
     * @param data An array of numbers, already formatted according to the rules in
     *             https://docs.edgeimpulse.com/docs/running-your-impulse-locally-1
     */
    classifyContinuous(data: number[], timeout?: number): Promise<RunnerClassifyResponseSuccess>;
    setLearnBlockThreshold(obj: SetRunnerBlockThreshold): Promise<RunnerSetThresholdResponse>;
    /**
     * Set a parameter for the model
     * @param parameters An object containing the parameters to set
     */
    setParameter(parameters: {
        [key: string]: number | string | boolean;
    }): Promise<void>;
    private sendHello;
    private send;
    /**
     * Whether a file exists (Node.js API cannot be converted using promisify)
     * @param path
     */
    private exists;
    private runTensorRTWarmup;
    private mapClassifyResponseSuccess;
    private cleanupTempDirAsync;
}