While installing Discourse on a free tier instance in a Google Cloud Compute Instance, I was following this discourse install tutorial with only minor adjustments:
- Used Ubuntu 20.04 LTS minimal
- Standard Disk (default is Balanced)
- e2.micro instance
However, when I was waiting for Step 9 to complete (after running
./discourse-setup) and everything being built, it would fail with an ESOCKETTIMEDOUT error related to
yarn. The last message that tries to run is
[ ! -d 'node_modules' ] || su discourse -c 'yarn install --production && yarn cache clean'.
Here’s what is going wrong. Yarn has a default timeout that is fine if you are using the minimum recommended for Discourse, but with a micro instance, it takes too long. To fix this, you have to manually edit one of the install scripts that runs for the new Docker container Discourse is building.
Fatal Error: Disconnected: No supported authentication methods available.
I lost so many hours to that little error. Here was the problem:
I followed the steps in this lovely PyCharm tutorial which walks you through creating your ssh key, saving it to a profile, and applying it to your GitHub account. This was successful in getting the terminal working. However, the error noted above was happening when I tried to use PyCharm’s Git integration and pull or push with the GitHub repository.
I recently received the reMarkable 2 paper tablet and it is everything it promised. I’m excited to use it for work and personal adventures, but for work I wanted to create a document template that would allow me to always make content that is pixel perfect for the tablet. This way there is no scaling necessary and I can even leave in margins for the menu options to stay visible.
So, here it is: reMarkable 2 OTT Template
This was designed for OpenOffice or LibreOffice, but since it is an open format, I’m sure it can be opened by Microsoft and other document programs. Once you open the file, you can save it as a template using the File menu. After that, it’s simply a matter of selecting File > New > Template and selecting reMarkable 2 from the available templates! When you’re done, Export to PDF and transfer to your reMarkarable using their convenient applications.
So I created a ton of podcasts. Well, I created a ton of trailers technically. Oh, and websites. And 1 episode.
Anyway, here are all of the show pages:
This is more notes and reference than an in-depth tutorial, but after spending a few hours trying different things, here’s how to get it all set up. Remember, just as Discourse recommends, a t2.micro instance only has 1GB of memory, so if you intend to grow things to an Internet-wide audience, you should use a t2.small instance instead.
As I continue my adventures in machine learning through the FastAI courses, I wanted to explore the concept of dropout rate. If you would like to see the Jupyter Notebook used for these tests, including full annotations about what/why, check out my machine learning github project. Specifically the Testing Dropout Rates (small images).ipynb.
Really quickly, dropout rate is a method in Convolutional Neural Networks (CNNs) of removing neurons (e.g. in the first layer of an image this would be individual pixels) to prevent overfitting (i.e. doing notably better on the training set than on the validation set) and thus increase the general applicability of the model. In other words, block a percentage of the material to force it to not become to overdependent on repeating patterns that lead it astray.
These tests were setup to isolate dropout rate as much as possible. Also, while this test was using ResNet50, results may differ using a different model. Okay, enough jibber-jabber, let’s jump right to the conclusions, shall we?
When getting started exploring machine learning, you will likely come across the free lessons at Fast.ai. These lessons require a few gigabytes worth of programs and algorithms as well as access to a powerful GPU from Nvidia (e.g. GTX 1060). The first lesson even walks you through setting up a cloud server for just that purpose, but what if your PC already has a powerful Nividia graphics card? What if you use Windows?
No problem. This quick guide walks you through the process of setting up a local environment for machine learning, starting with the Fast.ai tutorial series. It’s designed for Windows PCs with an Nvidia graphics card. Alright, let’s get started with a few quick downloads.