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PyTorch Mobile provides a runtime environment to execute state-of-the-art machine learning models on mobile devices. Latency is reduced, privacy preserved, and models can run on mobile devices anytime, anywhere.

In this blog post, we provide a quick overview of 10 currently available PyTorch Mobile powered demo apps running various state-of-the-art PyTorch 1.9 machine learning models spanning images, video, audio and text.

It’s never been easier to deploy a state-of-the-art ML model to a phone. You don’t need any domain knowledge in Machine Learning and we hope one of the below examples resonates enough with you to be the starting point for your next project.

Python Quick & Easy

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists, the content is also available at Berkeley Python Numerical Methods.

Image segmentation model trained from scratch on the Oxford Pets dataset.

This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset.

We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation.

This example demonstrates how to implement a deep convolutional autoencoder for image denoising, mapping noisy digits images from the MNIST dataset to clean digits images. This implementation is based on an original blog post titled Building Autoencoders in Keras by François Chollet.

Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented, and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library.

As of December 2020 Python ranked third in TIOBE’s index of most popular programming languages, behind C and Java.

This example demonstrates two techniques for building memory-efficient recommendation models by reducing the size of the embedding tables, without sacrificing model effectiveness:

1. Quotient-remainder trick, by Hao-Jun Michael Shi et al., which reduces the number of embedding vectors to store, yet produces unique embedding vector for each item without explicit definition.
2. Mixed Dimension embeddings, by Antonio Ginart et al., which stores embedding vectors with mixed dimensions, where less popular items have reduced dimension embeddings.

An exploration of threads, processes, and coroutines in Python, with interesting examples that illuminate the differences between each.

Notice: This map is not a precise reflection of the state of the AI field, but just my subjective representation.

This is my first map as of the end of 2020 and will be extended in the future. It contains more than 200 words or phrases, so to describe all of them would be too extensive and overkill. Much more interesting (and useful for me) to tell how this map was gradually building in my head. I will not explain everything, just the main things, so it is normal not to understand something.

 Python 3.7.10 and 3.6.13,  the lastest security fix rollups for Python 3.7 and Python 3.6, are now available. You can find the release files, links to the changelogs, and more information here:

Learn how to build a robust and developer-friendly Python microservices infrastructure using gRPC and Kubernetes. You’ll also explore advanced topics such as interceptors and integration testing.
 

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists, the content is also available at Berkeley Python Numerical Methods.

Let's pretend we need to build a recommendation engine for an eCommerce web site.

There are basically two approaches you can take: content-based and collaborative-filtering. We'll look at some pros and cons of each approach, and then we'll dig into a simple implementation (ready for deployment on Heroku!) of a content-based engine.

I'm assuming you already know the basics of REST. If not, or if you want a quick refresh, I've written an introduction to Designing a RESTful Web API.

Flask is a microframework for Python based on Werkzeug, a WSGI utility library.

Flask Framework Cookbook takes you through a number of recipes that will help you understand the power of Flask and its extensions. You will start by seeing the different ways of configurations that a Flask application can make use of. You will learn how to work with templates and learn about the ORM and view layers. You will see how to write an admin interface followed by the debugging and logging of errors. Finally, you will learn about different deployment and post-deployment techniques on platforms such as Apache, Tornado, and Heroku.

This tutorial looks at how to develop and test an asynchronous API with FastAPI, Postgres, Pytest, and Docker using Test-Driven Development (TDD). We'll also use the Databases package for interacting with Postgres asynchronously.

This tutorial will show you how to rapidly deploy your machine learning models with FastAPI, Redis and Docker.

Authentication is definitely a hard and complicated problem. Protocols like OAuth2 try to make it simpler, but in fact they make it harder to understand for beginners, as the reference is quite complex and there aren’t a lot of good practices available online.

The new framework FastAPI is now our go-to web library for all our projects, as it is very efficient to develop with and it supports amazing typing out of the box. The only issue we have is dealing with authentication when using a JS Frontend in front of it. Let’s close this debate once and for all by describing the authentication scheme that I think everyone needs for a simple web application with FastAPI, using an external provider.

Official tutorial showing you how to use FastAPI with most of its features, step by step.

Beginner’s guide to ML solutions deployment maintaining concurrency + load testing. Discussion on the Data Scientist’s responsibilities in terms of deployment.

Flash is a collection of tasks for fast prototyping, baselining and fine-tuning scalable Deep Learning models, built on PyTorch Lightning.

Whether you are new to deep learning, or an experienced researcher, Flash offers a seamless experience from baseline experiments to state-of-the-art research. It allows you to build models without being overwhelmed by all the details, and then seamlessly override and experiment with Lightning for full flexibility. Continue reading to learn how to use Flash tasks to get state-of-the-art results in a flash.

Progress in the last decade shows that the success of an ML system depends largely on the data it was trained on. Instead of focusing on improving ML algorithms, most companies focus on managing and improving their data.

Despite the success of models using massive amounts of data, many are skeptical of the emphasis on data as the way forward. In the last three years, at every academic conference I attended, there were always some debates among famous academics on the power of mind (inductive biases such as intelligent architectural designs) vs. data.

Welcome to Practice Python! There are over 30 beginner Python exercises just waiting to be solved. Each exercise comes with a small discussion of a topic and a link to a solution.