Linux by example – System configuration
The linux environment is now fully build, all tools and programs are created and we will do the final configuration of the system.
The linux environment is now fully build, all tools and programs are created and we will do the final configuration of the system.
In this video we setup rust jni to call a rust function from java using jni. Java native interface is a great way to enable quick calls to native code built in other languages like Rust. This approach can be used for other languages as well.
We are in our new Linux environment and create some more packages. util-linux – Linux utils e2fsprogs – file system programs sysklogd – logger framework sysvinit – handler to handle init run levels
In this video I talk about the different iterations I went through the last weeks in order to shut down a docker image in Kubernetes with workloads without missing any jobs.
We are in our new Linux environment and create some more packages. tar – create file packages vim – editor with a lot of functionality procps – check and manipulate processes texinfo – text manipulation used for documentation
Shutting down a java application gracefully is required to handle java scaling in kubernetes. Shutdown is handled via a hook in java and I’m doing an live coding example where I add code to wait until process is done.
We are in our new linux environment and create some more packages. Less – read files gzip – for compression iproute – to change network device configuration make – a build system libpipeline – to give piping functionallity man-db – the manual database.
We are in our new Linux environment and create some more packages. Check – To test C builds Gawk – To manipulate text diffutils – To check for changes findutils – To find files and directories GRUB – The boot manager
We look at how we can build rest API in python quickly. This quick guide is using FastAPI in order to create a quick API using python and SQL Alchemy. Rest API showing in OpenAPI and even redoc format is a really nice way to document your code.
We create a moving 3d image from pictures using a neural network. This new machine-learning algorithm uses mannequin videos to create pictures for the network to train on and create a depth map to rotate pictures in 3d.