Before jumping to “Singularity” let's start first with what is a container?
A container is defined as a software unit that contains the code of an app and all its dependencies. The goal is to run quickly and reliably your app when shared from a computing environment to another. You can see it as a lightweight alternative to full machine virtualization.
You can create your own container from an image, i.e., your app's blueprint (code, dependencies, how to run it).
Singularity is a container runtime designed specifically for High-Performance Computing HPC, but not only it can also run on NON-supercomputer…
Three steps to install cuDNN …
$ sudo apt-get update
$ sudo apt-get upgrade
For this one check the following page
Head to the NVIDIA developer website to download CUDA. You can access the downloads via this direct link:
“openfortivpn” is a client for PPP (Point-to-Point Protocol) + SSL (Secure Socket Layer) VPN (Virtual Private Network) tunnel services.
It spawns a PPPD (Point-to-Point Protocol Daemon) process and operates the communication between the gateway and this process. and It is compatible with Fortinet VPNs.
Install openfortivpn using the following commands in your terminal:
sudo apt update
sudo apt install openfortivpn
Use the config file:
/etc/openfortivpn/config and add to it the following:
# config file for openfortivpn
host = your.fortivpnserver.com # change this according to your need
port = 10443…
Nowadays, most of the Machine Learning application requires to run on a NVIDIA GPU to speed up the training process and the inference. In this tutorial, we will see how you can use Docker for your machine learning application and still access your GPU(s) to make your life easier when you want to share your work and/or deploy it into other machines.
Why using Docker?
You probably know already that there are a lot of prerequisites before being able to install TensorFlow or PyTorch and start building your machine learning app. …
A digital image is a function with discrete and bounded support. Support is multidimensional (Two or three dimensional) containing a set of discrete-valued pixels. These last can be scalar (Example: grayscale images) or vector (Example: multi-component or color imaging).
The range of possible values varies depending on the type of image considered, a digital image is associated with a rectangular tiling of space. Each element of the tiling called a pixel (Figure 1) is designated by its full coordinates.
Sampling is the process of spatial discretization of an image, it consists of associating for each pixel a unique value, the…