Having a centralized filesystem shared to the clients in the lab makes organizing data, doing backups, and sharing data considerably easier.
A shared filesystem is a great way to add versatility and functionality to a homelab. Having a centralized filesystem shared to the clients in the lab makes organizing data, doing backups, and sharing data considerably easier. This is especially useful for web applications load-balanced across multiple servers and for persistent volumes used by Kubernetes, as it allows pods to be spun up with persistent data on any number of nodes.
Whether your homelab is made up of ordinary computers, surplus enterprise servers, or Raspberry Pis or other single-board computers (SBCs), a shared filesystem is a useful asset, and a network filesystem (NFS) server is a great way to create one.
I have written before about setting up a "private cloud at home," a homelab made up of Raspberry Pis or other SBCs and maybe some other consumer hardware or a desktop PC. An NFS server is an ideal way of sharing data between these components. Since most SBCs' operating systems (OSes) run off an SD card, there are some challenges. SD cards suffer from increased failures, especially when used as the OS disk for a computer, and they are not made to be constantly read from and written to. What you really need is a real hard drive: they are generally cheaper per gigabyte than SD cards, especially for larger disks, and they are less likely to sustain failures. Raspberry Pi 4's now come with USB 3.0 ports, and USB 3.0 hard drives are ubiquitous and affordable. It's a perfect match. For this project, I will use a 2TB USB 3.0 external hard drive plugged into a Raspberry Pi 4 running an NFS server.
I am running Fedora Server on a Raspberry Pi, but this project can be done with other distributions as well. To run an NFS server on Fedora, you need the nfs-utils package, and luckily it is already installed (at least in Fedora 31). You also need the rpcbind package if you are planning to run NFSv3 services, but it is not strictly required for NFSv4.
Neural networks, as their name implies, are computer algorithms modeled after networks of neurons in the human brain. Learn more about neural networks from Algorithmia.
Recurrent neural networks, also known as RNNs, are a class of neural networks that allow previous outputs to be used as inputs while having hidden states.
The purpose of this project is to build and evaluate Recurrent Neural Networks(RNNs) for sentence-level classification tasks. Let's understand about recurrent neural networks for multilabel text classification tasks.
Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
Convolutional Neural Network: How is it different from the other networks? What’s so unique about CNNs and what does convolution really do? This is a math-free introduction to the wonders of CNNs.