Training and evaluation of Reinforcement Learning (RL) web agents have gained increasing attention, yet a scalable and efficient environment that couples realistic and robust browser-side interaction with controllable server-side state at scale is still missing. Existing environments tend to have one or more of the following issues: they overwhelm policy models with excessive and noisy context; they perform actions non-deterministically without waiting for the UI or network to stabilize; or they cannot scale isolated client-server containers effectively for parallel RL rollouts. We propose WEBSERV, an environment that includes 1) a compact, site-agnostic browser environment that balances context and action complexity, and 2) a scalable RL environment via efficient launching and resetting web-servers to enable scalable RL training and evaluation. We evaluate WEBSERV on the shopping CMS and Gitlab tasks in WebArena, achieving state-of-the-art single-prompt success rates while cutting launch latency by ~5x and storage need by ~240x, with a comparable memory footprint, enabling 200+ concurrent containers on a single host.

The emergence of Software-Defined Vehicles (SDVs) signifies a shift from a distributed network of electronic control units (ECUs) to a centralized computing architecture within the vehicle's electrical and electronic systems. This transition addresses the growing complexity and demand for enhanced functionality in traditional E/E architectures, with containerization and virtualization streamlining software development and updates within the SDV framework. While widely used in cloud computing, their performance and suitability for intelligent vehicles have yet to be thoroughly evaluated. In this work, we conduct a comprehensive performance evaluation of containerization and virtualization on embedded and high-performance AMD64 and ARM64 systems, focusing on CPU, memory, network, and disk metrics. In addition, we assess their impact on real-world automotive applications using the Autoware framework and further integrate a microservice-based architecture to evaluate its start-up time and resource consumption. Our extensive experiments reveal a slight 0-5% performance decline in CPU, memory, and network usage for both containerization and virtualization compared to bare-metal setups, with more significant reductions in disk operations-5-15% for containerized environments and up to 35% for virtualized setups. Despite these declines, experiments with actual vehicle applications demonstrate minimal impact on the Autoware framework, and in some cases, a microservice architecture integration improves start-up time by up to 18%.

We introduce Cognitive Kernel, an open-source agent system towards the goal of generalist autopilots. Unlike copilot systems, which primarily rely on users to provide essential state information (e.g., task descriptions) and assist users by answering questions or auto-completing contents, autopilot systems must complete tasks from start to finish independently, which requires the system to acquire the state information from the environments actively. To achieve this, an autopilot system should be capable of understanding user intents, actively gathering necessary information from various real-world sources, and making wise decisions. Cognitive Kernel adopts a model-centric design. In our implementation, the central policy model (a fine-tuned LLM) initiates interactions with the environment using a combination of atomic actions, such as opening files, clicking buttons, saving intermediate results to memory, or calling the LLM itself. This differs from the widely used environment-centric design, where a task-specific environment with predefined actions is fixed, and the policy model is limited to selecting the correct action from a given set of options. Our design facilitates seamless information flow across various sources and provides greater flexibility. We evaluate our system in three use cases: real-time information management, private information management, and long-term memory management. The results demonstrate that Cognitive Kernel achieves better or comparable performance to other closed-source systems in these scenarios. Cognitive Kernel is fully dockerized, ensuring everyone can deploy it privately and securely. We open-source the system and the backbone model to encourage further research on LLM-driven autopilot systems.

Dailymotion is the leading video discovery destination & technology that learns about your tastes over time, constantly surfacing the best, most relevant content on the web. Our mission is to provide the best video user experience for consumers on the market, connecting publishers and advertisers to engaged viewers who turn to Dailymotion for their daily fix of the most compelling music, entertainment, news and sports content around. Through partnerships with the world's leading publishers and content creators, France Télévisions, Le Parisien, CBS, Bein Sports, CNN, GQ, Universal Music Group, VICE and more, Dailymotion commands 3 billion monthly pageviews across its mobile app, desktop and connected TV experiences. Dailymotion is owned by Vivendi, one of the largest mass-media corporations in the world. We build the best place for people to enjoy the videos that matter.

Machine learning used to be an academic pursuit. If you wanted to work on it, you probably needed to be part of a lab or have a PhD. In early 2021, there was a shift. RiversHaveWings followed up with the VQGAN CLIP notebook. These notebooks turned text descriptions into images by guiding a GAN with CLIP.

Responsible for replacing and updating pods as and when needed without any downtime. Services: Services are responsible for routing and load-balancing traffic from external and internal sources to pods. Whenever a pod is deployed or replaced, its IP address changes. Hence a stable address provider is needed. A service provides stable IP addressing and DNS name to pods. We will define deployment and service for each of our applications.

Kubernetes: This open-source system allows you to automate the deployment, scaling, and management of containerized applications. It can be particularly useful for managing machine learning workflows, as it allows you to easily scale up or down as needed. Docker: It is a tool designed to make it easier to create, deploy, and run applications by using containers. Containers allow you to package an application with all of the parts it needs, such as libraries and other dependencies, and ship it all out as one package. This makes it easier to run the application on any other machine because everything it needs is contained in the package.

Deep learning models are wonderful, and we always want to use the newest cutting edge solutions to get the best results. But once in a while you stumble upon a relevant whitepaper that looks relevant to the task on hands, even though it's made a few years ago. And few years is an ethernity for the deep learning projects: old versions of frameworks, CUDA, python, etc -- nothing of that is easy to just install and laucnh on the modern systems. Usual answer for that would be Anaconda, but it doesn't provide enough isolation when it comes to the GPU accelerated models. My way of dealing with this problem would be of no surprise to the most: containerisation, in other words -- Docker.

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.

Thanks to the DALL·E 2, we finally have a very nice graphic representation of the feelings of a Docker container inside a macOS environment, I will try with this article to make this poor container safe to the coast. Docker engine, on macOS and Windows, needs a Linux Kernel; there aren't any exceptions here, you do not see it, but it is there to do all the dirty jobs (HN: https://news.ycombinator.com/item?id Instead, Docker CLI and docker-compose are native binaries for all operating systems. Two things are worth mentioning here regarding Microsoft; the first one is that Windows (and this sometimes can lead to some confusion) natively support Docker to run Windows containers. This implementation has been possible thanks to the joint effort of Microsoft and Docker in 2016 to create a container engine implementing the Docker specification on Windows; kudos to you, MS.