Upsampling causing CudaException code 2 about facerecognitiondotnet HOT 8 CLOSED

using System;
using DlibDotNet;
using FaceRecognitionDotNet;

namespace test
{
    class Program
    {
        static void Main(string[] args)
        {
            const string filename = "gettyimages-906162-2048x2048.jpg";
            var fr = FaceRecognition.Create(@"D:\Works\OpenSource\FaceRecognitionDotNet\test\FaceRecognitionDotNet.Tests\bin\Release\netcoreapp2.0\Models");
            using (var image = FaceRecognition.LoadImageFile(filename))
                for (var num = 0; num < 5; num++)
                    try
                    {
                        foreach (var face in fr.FaceLocations(image, num, Model.Cnn))
                            Console.WriteLine($"numberOfTimesToUpsample={num}: {face.Left}, {face.Top}, {face.Right}, {face.Bottom}");
                    }
                    catch (CudaException e)
                    {
                        Console.WriteLine($"CUDA Error code: {e.ErrorCode}");
                    }
        }
    }
}

numberOfTimesToUpsample=0: 317, 285, 600, 568
numberOfTimesToUpsample=1: 321, 273, 614, 566
CUDA Error code: 2
CUDA Error code: 2
CUDA Error code: 2

from PIL import Image
import face_recognition

# Load the jpg file into a numpy array
image = face_recognition.load_image_file("gettyimages-906162-2048x2048.jpg")

# Find all the faces in the image using a pre-trained convolutional neural network.
# This method is more accurate than the default HOG model, but it's slower
# unless you have an nvidia GPU and dlib compiled with CUDA extensions. But if you do,
# this will use GPU acceleration and perform well.
# See also: find_faces_in_picture.py
for num in range(0, 5):
    face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=num, model="cnn")

    print("I found {} face(s) in this photograph.".format(len(face_locations)))

    for face_location in face_locations:

        # Print the location of each face in this image
        top, right, bottom, left = face_location
        print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))

>python test.py
I found 1 face(s) in this photograph.
A face is located at pixel location Top: 285, Left: 317, Bottom: 568, Right: 600
I found 1 face(s) in this photograph.
A face is located at pixel location Top: 273, Left: 321, Bottom: 567, Right: 615
Traceback (most recent call last):
  File "test.py", line 13, in <module>
    face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=num, model="cnn")
  File "D:\Works\Python\envs\face_recognition\lib\site-packages\face_recognition\api.py", line 116, in face_locations
    return [_trim_css_to_bounds(_rect_to_css(face.rect), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, "cnn")]
  File "D:\Works\Python\envs\face_recognition\lib\site-packages\face_recognition\api.py", line 100, in _raw_face_locations
    return cnn_face_detector(img, number_of_times_to_upsample)
RuntimeError: Error while calling cudaMalloc(&data, new_size*sizeof(float)) in file D:\Works\Lib\Dlib\19.17\dlib\cuda\gpu_data.cpp:218. code: 2, reason: out of memory