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Raspberry Pi now officially supports Google’s TensorFlow software

GOOGLE’S MACHINE-LEARNING software, TensorFlow, is now officially supported by the Raspberry Pi.

Since its launch in 2015, the software firm has had a goal to be “an open source machine learning framework for everyone“. But to do that, it has needed to run on as many of the platforms that people are using as possible.

“We’ve long supported Linux, MacOS, Windows, iOS, and Android, but despite the heroic efforts of many contributors, running TensorFlow on a Raspberry Pi has involved a lot of work,” the company’s software engineer, Pete Warden, said in a blog post on Medium.

However, thanks to a recent collaboration with the Raspberry Pi Foundation, it announced that the latest 1.9 release of TensorFlow can be installed from pre-built binaries using Python’s pip package system.

“We’re excited about this because the Raspberry Pi is used by many innovative developers, and is also widely used in education to introduce people to programming, so making TensorFlow easier to install will help open up machine learning to new audiences,” explained Warden.

“We’ve already seen platforms like DonkeyCar use TensorFlow and the Raspberry Pi to create self-driving toy cars, and we can’t wait to discover what new projects will be built now that we’ve reduced the difficulty.”

Founder of the Raspberry Pi project, Eben Upton, said the launch represents how vital it is that a modern computing education covers both fundamentals and forward-looking topics.

“With this in mind, we’re very excited to be working with Google to bring TensorFlow machine learning to the Raspberry Pi platform. We’re looking forward to seeing what fun applications kids of all ages create with it, and we agree,” he said.

TensorFlow explained that if you’re running Raspbian 9 (stretch), you can install it by running these two commands from a terminal:

sudo apt install libatlas-base-dev

pip3 install tensorflow

“You can then run python3 in a terminal, and use TensorFlow just as you would on any other platform,” it added.

Here’s a simple hello world example:

# Python

import tensorflow as tf

tf.enable_eager_execution()

hello = tf.constant(‘Hello, TensorFlow!’)

print(hello)

If the system outputs ‘Hello, TensorFlow!, then you are ready to begin writing TensorFlow programs, Warden said.

Both companies are hoping that the collaboration will see a lot more educational material and tutorials emerge that will help more people “explore the possibilities of machine learning on such a cost-effective and flexible device”. µ

Further reading

Source : Inquirer

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