UserWarning: Large object of size 168.32 MiB detected in task graph: 
  {'task': <Task: remove_bg>, 'state': None, 'upstre ... _parent': True}
Consider scattering large objects ahead of time
with client.scatter to reduce scheduler burden and 
keep data on workers

    future = client.submit(func, big_data)    # bad

    big_future = client.scatter(big_data)     # good
    future = client.submit(func, big_future)  # good
  warnings.warn(

@task
def list_folder(root_fld):
    """List all the image paths in the root_fld"""

@task
def load_image(image_path):
    """Reads an image from the given image_path"""

@task
def load_model():
    """Loads the Pytorch ML model and returns it"""

@task
def apply_model(img, model):
    """Runs inference of the given model on the give image and returns the result"""

executor = DaskExecutor(cluster_kwargs={"n_workers": 1, "threads_per_worker": 6})
with Flow("resolution-flow", executor=executor) as flow:
    root_fld = Parameter("root_fld",)
    image_paths = list_folder(root_fld)
    images = load_image.map(image_paths)
    model = load_model()
    inference_results = apply_model.map(images, unmapped(model))

map