FPGA-accelerated ML on MindsDB Lightwood

InAccel framework can be used to speedup ML training/prediction over the queries of MindsDB

Import the required libraries

import pandas as pd
from lightwood import Predictor
from inaccel_sklearn_mixer import LogisticRegression

Load historical data

df = pd.read_csv('https://mindsdb-example-data.s3.eu-west-2.amazonaws.com/home_rentals.csv')

Train a new predictor using FPGAs

predictor = Predictor(config)

Make predictions

df = df.drop([x['name'] for x in config['output_features']], axis=1)
results = predictor.predict(when_data=df)




Applications Acceleration instantly

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

An introduction to Computer Vision using transfer learning in fast.ai — Aircraft Classification

7 Reasons to Learn Machine Learning

Who’s That Pokémon?

Step by Step approach of deploying ML model on Cloud with Azure

All the single neurons

Generating Drug Names with a Markov Chain

A Comprehensive Guide to Natural Language Generation

Making Fairness an Intrinsic Part of Machine Learning

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store


Applications Acceleration instantly

More from Medium

DataTrends 2022 — by Virginie Marelli

A summary and some thoughts on GSoC 2021

Using Recommender Systems to Improve Job Search with the JumpStart Platform

End to End ML pipelines with MLflow