The purpose of the downloader function is not about downloading, but about getting the file in a state where it can be shared amongst the other functions within the Tape Index natural language processing flow. Since each step needs to reference the file being processed, it’s much faster and efficient to not have to download it every time it’s required. Here’s a refresher of the NLP flow…
Read MoreAs mentioned before, Modal is a powerful serverless computing platform. These means that code (functions) can be called on demand and only run for as long as the function requires. In many cases, this method of computing can be significantly cheaper and more scalable than other solutions. Since Tape Index needs to take advantage of GPU cores for certain aspects of its NLP flow, Modal is an ideal and cost-effective solution for running GPU enabled servers on demand…
Read MoreIf you’re familiar with serverless computing especially functions as a service (FaaS) like AWS Lambda or DigitalOcean Functions, you might be aware of the limitations that these services have. Limitations usually revolve around compute power, runtime environments and the additional packages you might be able to install to get a function running. For Tape Index and its natural language processing (NLP) flow, I quickly hit all those limitations from the start. In my searching for alternative solutions I came across Modal. Modal is similar to serverless functions, but fits more in line with what I would consider a serverless application…
Read MoreOver the past few months I’ve been building out Tape Index – a radically simple organizational system designed for content creators. Indexing literally everything and anything that someone uploads, regardless of file format, is core to keeping things simple. To accomplish this, Tape Index needs to support transcription and natural language processing (NLP) right from the start. I am also self-funding Tape Index, so whatever solution I use can’t break the bank, specifically my wallet…
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