Machine learning is revolutionizing edge computing 🚀
In the last decade machine learning has advanced leaps and bounds. This is particularly true when it comes to computer vision and language.
Providing everything you need to accurately gather data, label, train, deploy, maintain and running real-time inference, Genki ML is the missing platform for time-series ML.
For the time being we focus on making the deployment and real-time performance of neural networks seamless. That means providing a convenient way to convert trained models from various formats to one that you can embed in your application, and use in your build step when converting from offline R&D, e.g., in
Python, to online performance, e.g., in
Command Line Interface
genkiml is the command line interface (CLI) that powers the Genki ML.
genkiml currently supports the conversion of formats such as
PyTorch into the Genki ML
Here is an example of how you can convert a fully connected Keras model using the
First we define a demo model:
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")
and then we point
genkiml to it and the CLI takes care of the rest
python genkiml.py fully_connected_keras_model
Quest 2 Demo
As long as you are working with time-series data, Genki ML is there for you!
We use the Wave smart ring as an example hardware, but note that Genki ML is hardware agnostic. The ring sends IMU data to the Quest where a surface detection model is used to detect whether the hand touches a surface or not.