Regression inference folder

operators.ml3d.regression_inference_folder(client, folder_in_folder_points='/folder_in_folder_points', folder_out_folder_predictions='/folder_out_folder_predictions', in_model_path='parameters_model', worker_instance_type='x2large', manager_instance_type='small', extension_in_folder_points='.laz', extension_out_folder_predictions='.laz', skip_existing_files=False)

regression_inference_folder(client,

in_folder_points=’/in_folder_points’,

out_folder_predictions=’/out_folder_predictions’,

in_model_path=’parameters_model’,

worker_instance_type=’x2large’,

manager_instance_type=”small”,

extension_in_folder_points=”.data_train/points”,

extension_out_folder_predictions=”.data_train/predictions”,

skip_existing_files = False )

Parameters:

• in_model_path – model path

• folder_in_folder_points – input directory with training data

• folder_out_folder_predictions – output directory with predictions

• worker_instance_type – cloud instance type of worker nodes

• manager_instance_type – cloud instance type of manager node

• extension_in_folder_points – File extension of files in folder for folder_in_folder_points

• extension_out_folder_predictions – File extension of files in folder for folder_out_folder_predictions

• skip_existing_files – skip files that already exist in the output folder