This is the third part of the tutorial to install and configure SLURM on Azure (part I, part II). With this post, we are going to complete the process and we show an example of the execution of one task.
This is the second post of the SLURM configuration and installation guide on Azure (part I is here). In this part, we are going to configure the NFS system, and finally, in the third post, we are going to set up the SLURM environment.
I got some free time to share this project, the deployment of a workload manager to ease the management of my research group’s cluster of GPUs.
As these days I’m very busy, I want to publish just one quick post about something that could be useful in some contexts, that is the use of vector instructions, in particular, the Advanced Vector Extensions (AVX) instruction set from Intel.
The project that I want to introduce in this post is a minigame developed with Unity, a powerful game engine, and Vuforia, a library for creating Augmented Reality (AR) apps. This was the final task of the subject “Augmented and Virtual Reality”.
In this game, the idea is throwing stones to hit the skeletons that raise from their graves. It is a very straightforward game, but developing it was enjoyable. The following video shows a short gameplay.
About Unity, it seemed a helpful tool that allows you to develop your ideas quickly, obtaining a “multiplatform” app that you can run and test. Considering the number of available assets thanks to both the store and the community, it could be the best option to evaluate if a game concept could work or not.
Regarding Vuforia, it was integrated with Unity through its plugin, and it worked very well, providing a fast response even with marker occlusion.
I don’t know if the situation is the same with the updates that both tools have received since this project was developed, around 2014, but I strongly recommend using this combination (Unity + Vuforia) for creating your AR apps, even more, considering the attention that AR is receiving nowadays.
This is the second post about my Master’s thesis and I’m going to talk about the classifier that we used for this project. Regarding this task, we want to classify the main action that is happening in a video among the available categories in the dataset UCF-101. According to this, I want to introduce the Neural Network model that we used for this task.
In this post, I’m going to show you the project that I developed for my Master’s Thesis while I was collaborating with my current research group. The code could be found on my GitHub and comprises the process of extracting features from the dataset UCF-101 using DenseTrajectories, as well as training and evaluating the model.
As a part of my career review, I would like to introduce the project that I presented as Bachelor’s Final Project. The main idea was to design and implement a library to generate interactive urban environments running in the browser using WebGL through the library three.js.