This case study was originally posted at the AWS Solutions site.

Overview

Torc Robotics (Torc) wanted to facilitate remote development for its distributed workforce. The company develops autonomous vehicle software and technology that’s aimed at commercializing autonomous semitrucks by 2027. To support these efforts, Torc needed a secure, robust virtual desktop infrastructure (VDI) solution for engineers to run large GPU- and CPU-based workloads.

Torc, which was already using Amazon Web Services (AWS) for many of its workloads, built a VDI solution using Amazon DCV, which delivers high-performance remote desktop and application streaming. Now, Torc engineers have secure, highly available access to the compute resources that they need in minutes, and the company can continue working toward its goal of making highways safer using autonomous driving technology.

Opportunity | Using Amazon DCV to create the VDI Ranch for Torc

Torc—founded in 2005 and an independent subsidiary of Daimler Truck since 2019—is focused on delivering an autonomous trucking software product for hub-to-hub transportation, with the vision to provide fleet customers with the safest, most reliable, and cost-effective solution on the market. “Safety is a top priority at Torc,” says Jason Fox, senior engineering manager at Torc. “The trucking industry is facing driver shortages and inefficiencies, and there are many crashes on public roads that involve trucks. There is an opportunity to improve road safety and efficiency in freight transportation and Torc’s role in this is developing autonomously driving semitrucks.” In 2024, Torc completed validation of its first driver-out product release on production-intent hardware and software. The company is now testing on public roads from its autonomous hub in the Dallas–Fort Worth area.

Torc’s engineers and developers work from many locations, and the company sought to support remote development in a governed, standardized environment where it could secure its intellectual property. Torc also wanted to provide flexible access to GPU resources for the machine learning research and training that supports its autonomous driving software. At the same time, Torc did not want to create a centralized environment that would have high maintenance overhead or single points of failure. “We’re cloud engineers, so we think that things should be horizontally scaled, resilient, automated, and repeatable hundreds of times; not centrally managed or where a single developer’s issues will affect other people,” says Fox.

Torc tested various VDI solutions. As a customer of AWS since 2020, it looked to see what AWS had to offer. “We lean on AWS heavily for managed services whenever we can so that we can think more about writing code and making the trucks work,” says Fox. “The services that AWS offers made sense for this project as well.” Torc worked with the AWS team to test Amazon DCV. The solution worked well for the company, and Torc ultimately used it as the main component of its in-house VDI solution, the VDI Ranch.

Solution | Spinning up GPUs in under 5 minutes using Amazon DCV

The main principle behind the VDI Ranch is the ability to spin up and down instances as needed. “We strongly feel that in cloud computing environments, servers should be cattle, not pets,” says Fox. “We should have easily reproducible servers in the cloud, and when there’s a problem with a server, you delete it and spin up another. You don’t feed and care for it like a pet.” In fact, one of the options in the VDI Ranch is a “Replace Instance” button. If a server has an issue, the developer can simply replace the instance with a new one, keeping their data and settings intact.

The VDI Ranch provides a self-service, end-user compute environment for nearly 300 developers and engineers who can get access to the compute resources they need in under 5 minutes—rather than submitting a ticket and waiting several days to have resources allocated. This greatly accelerates developer productivity.

With the VDI Ranch, Torc can provide developers with flexible access to GPU and other high-powered computing resources using Amazon Elastic Compute Cloud (Amazon EC2), which provides secure and resizable compute capacity for virtually any workload. “Using AWS and Amazon DCV is a much easier way for us to provide GPU horsepower to developers when they need it,” says Fox. “We cannot provide laptops or even desktops with the kind of GPU power that we get from Amazon EC2 instances, and it’s flexible, so we can tear the instance down when we don’t need it anymore.” The VDI Ranch now powers every major area of Torc’s software development.

Torc implemented automated governance and security controls within the VDI Ranch, including integrating the VDI Ranch with Torc’s third-party identity and access management solution. Torc also implemented observability dashboards in Datadog to track networking and compute instance performance. These dashboards are used by the cloud engineers supporting the VDI Ranch, which has helped Torc more easily troubleshoot technical issues among its remote workforce, improving performance and latency.

The VDI Ranch also makes it possible for the Torc cloud engineering team to standardize the hardware that Torc employees use—which improves security and troubleshooting—while still giving engineers a development environment that uses their preferred operating system. For contractors, Torc uses Amazon WorkSpaces, which provides fully managed virtual desktops. “Using Amazon WorkSpaces, we get the benefits of managed VDI, including segmentation between employee and contractor workloads, and don’t have to manage Windows images,” says Fox.

Outcome | Improving remote development using AWS

As the company works toward releasing its autonomous trucks, Torc will continue improving the user experience of the VDI Ranch for its developers. It has recently deployed a system that intelligently shuts down instances that aren’t being used and has built a VDI-specific compute optimizer into FinOps dashboards to help users rightsize their compute resources. These measures will lead to better optimization and lower costs.