Running face_encodings in parallel gives RuntimeError: cudaGetDevice()... reason: initialization error

• face_recognition version: 1.2.3
• Python version: 3.7.2
• Operating System: Scientific Linux 7.6

I have dlib installed with GPU support.

>>> import dlib
>>> dlib.DLIB_USE_CUDA
True

I'm using batch_face_locations() to get image locations. Then for each location that was pulled out of the batch, I'm getting the encodings using face_encodings(). I time both of these operations, and the time get the encodings is about 3x longer than the time to get the locations. I supposed that I could speed up the time to get the encodings by getting them all in parallel. So I tried something like this:

import multiprocessing as mp
import face_recognition

def get_encoding(frame, face_locations, return_queue):
    encode = face_recognition.face_encodings(frame, face_locations)
    return_queue.put(encode)

all_batch_face_locations = ... # the frames and associated batch_face_locations returned for all images in my dataset

encodings = []
for frames, batch_face_locs in all_batch_face_locations:
    # get the encodings for the current batch of images in parallel
    procs = []
    queues = []
    for frame_number_in_batch, face_locations in enumerate(batch_face_locs):
        q = mp.Queue()
        p = mp.Process(
            target=get_encoding, 
            args=(frames[frame_number_in_batch], face_locations, q))
        p.start()
        procs.append(p)
        queues.append(q)
        
    for p, q in zip(procs, queues):
        p.join()
        encoding = q.get()
        encodings.append(encoding)

Yet this gives me an error:

...
RuntimeError: Error while calling cudaGetDevice(&the_device_id) in file /tmp/pip-install-2vh9r_rp/dlib/dlib/cuda/gpu_data.cpp:178. code: 3, reason: initialization error

Now I can't actually find anywhere that says that face_recognition.face_encodings() uses the GPU. Even the dlib documentation for the function that face_recognition eventually calls doesn't mention it. But it seems to be using it nonetheless.

I see references in other issues (#98 #374 #649) to running face_encodings() on multiple CPU cores, and I'd at least like to try and experiment with that to see if I can get some improvement. Is there something I'm missing to allow me to run batch_face_locations on the GPU and face_encodings on the CPU? Or, if not, is there some way to also run the encodings on the GPU in batches?

Start by reproducing the multiprocessing example with face_recognition.batch_face_locations() and face_encodings() on the listed Python and dlib setup. Review the referenced issues (#98, #374, and #649) and the dlib.shape_predictor documentation; done means an agreed, tested execution path or clear documentation of the supported CPU/GPU behavior.