90 Days With The Pinebook Pro

So earlier this year I reviewed the PineBook Pro. I was pretty impressed with it at the time, but am I still enamored with this $200 laptop? I’ve now been using it for at least 90 days. I know that because I haven’t rebooted it in 90 days. Really.

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90 Days With The Pinebook Pro

So earlier this year I reviewed the PineBook Pro. I was pretty impressed with it at the time, but am I still enamored with this $200 laptop? I’ve now been using it for at least 90 days. I know that because I haven’t rebooted it in 90 days. Really.

#linux #open-source #programming #arm #pinebook

How to Speed Up Your Day-To-Day Work in Python

Use all the computing power available to you

In my day-to-day data work, I routinely find myself running a lot of for loops. These can take minutes to complete, which isn’t necessarily a long time, but looping is embarrassingly parallelizable. We can do better.

In this article, I will discuss how to make more efficient use of your time when working in Python. Whether you work on a laptop or a high-performance computer (HPC), you can speed up your workflow by taking full advantage of all the computing power available to you. This can be achieved with the Dask and Dask-jobqueue libraries. This post will discuss how to create and use a dask cluster on your local computer and an HPC.

Dask is a Python library for parallel computing and dask-jobqueue lets you interact with job schedulers, such as Slurm, from a Jupyter Notebook. Dask makes simple things are easier and complex things are possible and itsnumpy and pandas-like API makes writing code familiar to Pythonic data practitioners.

Table of contents

• Installation
• Setup Dask cluster on a laptop
• Setup Dask cluster on an HPC
• Submit work to the cluster
• Dask LabExtension
• Final thoughts

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