Models are exported using the
genkiml CLI. The input is a model/checkpoint file and the output is the exported version of the model along with C++ code to run and easily integrate it in an existing codebase. This tool has support ONNX and Keras models
After installing the required packages run (see Installation CLI)
python genkiml.py path/to/model
This will output a zip archive to the desired
--output-path. If no output path is provided the zip archive is put into the current folder.
Example using a Keras model
Let's define a simple fully connected network with an input with the shape
(batch_size, 100) and
2 outputs and save it to disk
import tensorflow as tf model = tf.keras.models.Sequential([ tf.keras.layers.Dense(256, input_shape=(100,)), tf.keras.layers.ReLU(), tf.keras.layers.Dense(256), tf.keras.layers.ReLU(), tf.keras.layers.Dense(2) ]) model.save("fully_connected_keras_model")
Then simply point the
genkiml CLI to the model.
python genkiml.py fully_connected_keras_model
This will output a file in the current folder,
genkiml_cpp.zip, that contains the exported model along with the runtime.