Piwigo is open-source software for sharing image galleries easily. Installing it on Dreamhost (I’m using a VPS but any type of hosting works) was super easy, but by default any emails sent would have Mail System Delivery Failure due to email security problems.
Luckily, the solution was to create an email account in Dreamhost and configure SMTP in Piwigo. Here are the steps in a new installation:
Castopod is podcast hosting software that allows you to easily serve your podcast(s) from your own server rather than through a secondary service like anchor.fm, blubrry, and libsyn. While I personally use anchor.fm for some podcasts which I wanted to easily monetize, for a new podcast, Story Suggest, I had no desire for it to get monetized by me or anyone else. Furthermore, I also wanted to dip my toe into integration with the fediverse. Don’t worry if you don’t know what that is, it’s not critical to any of this.
Either way, while the installation documentation for castopod is relatively clear, I felt like there were a few quick notes/tips I could add for those installing on a shared host like Dreamhost. While I maintain a VPS at Dreamhost, the functionality and installation is the same. Below I’ve highlighted some clarifying points to help others quickly get setup with Castopod.
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:
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.
Continue reading
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.
Continue reading
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?
Continue reading
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.
Selectize.js is a javascript library that allows you to offer more complex HTML select boxes, such as combining a select and an input box, commonly known as an autocomplete combobox. Most importantly, they have a built-in stylesheet for Bootstrap 3. However, I discovered a problem when you are trying to add another form field dynamically, specifically using jQuery’s .clone() function.
However, selectize() does not clone well… it breaks horribly. The key you must .destroy() the selectize() prior to cloning. Of course, another problem occurs then: The select element, upon selectize.destroy(), will reset the value to the last option. Solution? Store the value, destroy(), then set the value.
// When add button is clicked $('#add').on('click',function(){ $('.combobox').each(function(){ // do this for every select with the 'combobox' class if ($(this)[0].selectize) { // requires [0] to select the proper object var value = $(this).val(); // store the current value of the select/input $(this)[0].selectize.destroy(); // destroys selectize() $(this).val(value); // set back the value of the select/input } }); $('#monsters .form-group:first') .clone() // copy .insertAfter('#monsters .form-group:last'); // where selectizeme(); // reinitialize selectize on all .combobox }); |