Rendering - Function

Rendering Images and Animations on Function.

Function can be used for much more than AI predictions. In this example, we will render an image using Blender on Function.

Implementing the Predictor

Create a rendering.ipynb notebook and add the following code cell to install system libraries that Blender requires:

# Install system library dependencies for Blender
!apt-get install build-essential subversion cmake       \
    libx11-dev libsm-dev libxxf86vm-dev libxcursor-dev  \
    libxi-dev libxrandr-dev libxinerama-dev libegl-dev  \
    libwayland-dev wayland-protocols libxkbcommon-dev   \
    libdbus-1-dev linux-libc-dev -y

Next, create a new code cell to install Blender for Python:

# Install Blender for Python
%pip install bpy

With these dependencies, we can now implement our prediction function. The function will simply render the default Blender scene with the Cycles renderer and return it:

from PIL import Image
from tempfile import mkstemp

def predict () -> Image.Image:
    # Load the default scene
    from bpy import context, data, ops
    ops.wm.read_homefile()    
    # Configure Cycles renderer to use all available GPUs
    context.scene.render.engine = "CYCLES"
    context.scene.cycles.samples = 16
    context.scene.cycles.device = "GPU"
    context.preferences.addons["cycles"].preferences.compute_device_type = "OPTIX"
    context.preferences.addons["cycles"].preferences.get_devices()
    for device in context.preferences.addons["cycles"].preferences.devices:
        device["use"] = True
    # Render to image
    _, render_path = mkstemp(suffix=".png")
    context.scene.render.filepath = render_path
    ops.render.render(write_still=True)
    # Load rendered image
    result = Image.open(render_path)
    # Return
    return result

Note that our prediction function takes no input parameters. Predictors are not required to accept or return anything.

Creating the Predictor

Now, let's provision the predictor on Function. We will be running our predictor on an A40 GPU to speed up rendering. Open a terminal and run the following command:

# Create the predictor on Function
fxn create @username/rendering rendering.ipynb --acceleration A40

Replace username with your Function username.

Rendering an Image

Once the predictor is active, run the following command in a terminal to render out an image:

# Render an image with our predictor
fxn predict @username/rendering

Replace username with your Function username.

rendering with blender