Robot Vision Lab

RVL Cloud
3 April 2017: Configuring AC for RVL Cloud


Hardware Specifications

RVL Blog

Bash Resources


    The main reason for why we created the RVL Cloud is that practically every research project these days has some sort of a big-data and deep-learning component to it. A cloud based execution of these projects makes it much easier to customize the resources needed for solving such problems. Additionally, some of our research projects now require deliverables in the form of virtual machines that must run on a commercial cloud platform like the Amazon Web Services.

    These factors have heavily influenced how computing is done in RVL at Purdue. Several of our research projects involve large datasets of satellite imagery. These datasets can be terabytes in size and are typically processed by GPU enabled VMs. Other examples include processing of video for tracking humans and objects, detection of hazardous materials in airport security systems, retrieval of information from large textual datasets and software libraries, automatic bug localization in large software systems, etc.

    RVL Cloud runs on the OpenStack framework. This is the home page for our RVL Cloud where we hope to share our experience and knowledge in setting up a powerful open-source cloud computing platform in a university setting.

    We are frequently asked as to why we created our own cloud platform. Why not just rent time on, say, Amazon Web Services? To answer, an in-house cloud platform allows the students to experiment with the cloud architecture itself and thus develop deeper insights into the workings of a cloud. Additionally, the inter-node communication in an in-house cloud can be much faster than in a large-scale commercial operation.