Autonomous vehicles have been rapidly developed as demand that provides safety and efficiency in transportation systems. As autonomous vehicles are designed based on open-source operating and computing systems, there are numerous resources aimed at building an operating platform composed of Ubuntu, Autoware, and Robot Operating System (ROS). However, no explicit guidelines exist to help scholars perform trouble-shooting due to incompatibility between the Autoware platform and Ubuntu operating systems installed in autonomous driving systems-equipped vehicles (i.e., Chrysler Pacifica). The paper presents an overview of integrating the Autoware platform into the autonomous vehicle's interface based on lessons learned from trouble-shooting processes for resolving incompatible issues. The trouble-shooting processes are presented based on resolving the incompatibility and integration issues of Ubuntu 20.04, Autoware.AI, and ROS Noetic software installed in an autonomous driving systems-equipped vehicle. Specifically, the paper focused on common incompatibility issues and code-solving protocols involving Python compatibility, Compute Unified Device Architecture (CUDA) installation, Autoware installation, and simulation in Autoware.AI. The objective of the paper is to provide an explicit and detail-oriented presentation to showcase how to address incompatibility issues among an autonomous vehicle's operating interference. The lessons and experience presented in the paper will be useful for researchers who encountered similar issues and could follow up by performing trouble-shooting activities and implementing ADS-related projects in the Ubuntu, Autoware, and ROS operating systems.

We study automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game. We relax the standard assumption that the game model is correctly specified and consider that each player has a probabilistic conjecture about the model, which may be misspecified in the sense that the true model has probability 0. This formulation allows us to capture uncertainty about the infrastructure and the intents of the players. To learn effective game strategies online, we design a novel method where a player iteratively adapts its conjecture using Bayesian learning and updates its strategy through rollout. We prove that the conjectures converge to best fits, and we provide a bound on the performance improvement that rollout enables with a conjectured model. To characterize the steady state of the game, we propose a variant of the Berk-Nash equilibrium. We present our method through an advanced persistent threat use case. Simulation studies based on testbed measurements show that our method produces effective security strategies that adapt to a changing environment. We also find that our method enables faster convergence than current reinforcement learning techniques.

Reproducing published deep learning papers to validate their conclusions can be difficult due to sources of irreproducibility. We investigate the impact that implementation factors have on the results and how they affect reproducibility of deep learning studies. Three deep learning experiments were ran five times each on 13 different hardware environments and four different software environments. The analysis of the 780 combined results showed that there was a greater than 6% accuracy range on the same deterministic examples introduced from hardware or software environment variations alone. To account for these implementation factors, researchers should run their experiments multiple times in different hardware and software environments to verify their conclusions are not affected.

We study automated intrusion response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed stochastic game. To solve the game we follow an approach where attack and defense strategies co-evolve through reinforcement learning and self-play toward an equilibrium. Solutions proposed in previous work prove the feasibility of this approach for small infrastructures but do not scale to realistic scenarios due to the exponential growth in computational complexity with the infrastructure size. We address this problem by introducing a method that recursively decomposes the game into subgames which can be solved in parallel. Applying optimal stopping theory we show that the best response strategies in these subgames exhibit threshold structures, which allows us to compute them efficiently. To solve the decomposed game we introduce an algorithm called Decompositional Fictitious Self-Play (DFSP), which learns Nash equilibria through stochastic approximation. We evaluate the learned strategies in an emulation environment where real intrusions and response actions can be executed. The results show that the learned strategies approximate an equilibrium and that DFSP significantly outperforms a state-of-the-art algorithm for a realistic infrastructure configuration.

A host node is the physical server that your virtual machine is provisioned on. Double check that it has enough resources to allocate for your desired instance, that it is close to your location, and that it is reliable. Note: You may have multiple GPUs, but they must all be the same type. E.g. you cannot have both an NVIDIA A5000 and an NVIDIA A6000 on the same server, but you can have 2 NVIDIA A6000s The external port is where requests will enter; the internal port is where you set the requests to be forwarded to. Please remember to include an SSH port (a port forwarded to port 22) or an RDP port (a port forwarded to port 3389) so that you will be able to access your instance once created.

Everybody hates installing NVIDIA drivers, you have to manually download them, then install cuda, be sure to have the correct version of everything, and change them from time to time to be updated. From Ubuntu 20.02, the drivers will be automatically installed by the OS. That's great, but you lose control over them. Maybe you need a specific version, or your code only works with cuda 10. In that case, well things may get dirty.

When we talk about Computer vision products, most of them have required the configuration of multiple things including the configuration of GPU and Operating System for the implementation of different problems. This sometimes causes issues for customers and even for the development team. Keeping these things in mind, Nvidia released Jetson Nano, which has its own GPU, CPU, and SDKs, that help to overcome problems like multiple framework development, and multiple configurations. Jetson Nano is good in all perspectives, except memory, because it has limited memory of 2GB/4GB, which is shared between GPU and CPU. Due to this, training of custom Computer Vision models on Jetson Nano is not possible.

Lambda Stack provides a one line installation and managed upgrade path for: PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA Drivers. No more futzing with your Linux AI software, Lambda Stack is here. To install Lambda Stack on your desktop, run this command on a fresh Ubuntu installation (20.04, 18.04, or 16.04). For servers, see the server installation section below. Lambda Stack can run on your laptop, workstation, server, cluster, inside a container, on the cloud, and comes pre-installed on every Lambda GPU Cloud instance.

It is always convoluted and challenging to install a CUDA toolkit and library that needs to interact with your NVIDIA GPU on an Ubuntu machine. However, if done right, the CUDA toolkit harnessing your NVIDIA GPU can be a great tool that can harness the power of GPU to produce fast applications. The basic requirement for following instructions in this article is a computer with Ubuntu 20.04 installed with an NVIDIA GPU. In my case, it was NVIDIA GeForce GTX 1650 Ti. Further, at the time of writing this article, I installed the latest version of the CUDA toolkit which was CUDA Toolkit 11.3.

Ubuntu 20.04 LTS is the best Ubuntu yet. It's been over a month since it got released, and it has excellent reception among both desktop and server users. Many organisations are already starting using the latest Ubuntu. Others might be using previous versions of Ubuntu which are still supported under LTS or ESM - such as 19.10, 18.04 LTS, 16.04 LTS or even 14.04 LTS. If you use the previous version of Ubuntu you are probably wondering if you should migrate, when is the right time, and what factors should you take into account when planning a migration.