Setting up a GPU development environment typically requires 2-3 hours of configuration time and deep CUDA expertise. Here's how Daytona's infrastructure enables instant GPU workspace deployment with zero configuration overhead.
We’ve previously showcased how Codeanywhere has leveraged Daytona’s infrastructure to integrate AI coding capabilities. Today, we’re expanding that: Codeanywhere has now enabled GPU-powered workspaces, utilizing Daytona’s GPU support to provide developers with access to high-performance GPU development environments instantly.
Why Cloud GPU Workspaces?
Today, GPUs (Graphics Processing Units) have become essential for working on machine learning, model training, data analysis and visualization, or scientific simulations. However, the challenge is not just getting access to high-powered GPUs but also dealing with the complexity of setting them up. Installing CUDA drivers and the CUDA toolkit can be a tedious and frustrating process.
GPU workspaces solve this problem by providing access to a GPU-equipped workspace with just a click. Moreover, these workspaces come fully set up with all the CUDA drivers and tools you need right out of the box, saving time and eliminating the hassle of configuration.
Under the hood: Nvidia T4 GPUs
In the Codeanywhere case study, NVIDIA T4 GPUs power their workspaces, delivering the performance needed for demanding tasks. The Codeanywhere team chose the T4 as their starting point because these GPUs are both versatile and efficient, making them an ideal choice for developers working across a wide range of applications.
Here’s what makes the NVIDIA T4 a standout option:
High-Performance Computing: The NVIDIA T4 handles demanding tasks like training and inference with ease, delivering the speed and computational power needed for intensive workflows.
Energy Efficiency: Despite its robust performance, the T4 is engineered for energy efficiency, providing advanced computing capabilities without unnecessary overhead costs.
Versatile Workloads: From data processing to simulations, the T4 is built to support a diverse set of use cases, making it a flexible tool for developers.
Of course, with Daytona’s infrastructure, any GPU can be deployed under the hood, from NVIDIA T4 and L4 to H100. As we understand it, Codeanywhere plans to expand their offering to include the full portfolio of GPUs, further enhancing their workspace capabilities in the future.
Focus on your work, Not on GPU Infrastructure
For many developers, setting up and managing GPU environments is a time-consuming and frustrating process, requiring the installation of drivers, managing dependencies, and ensuring system compatibility. These tasks pull your focus away from the work that truly matters.
GPU-enabled workspaces remove these roadblocks entirely. Beyond individual convenience, this streamlined approach enhances productivity, by offering cost-effective access to high-powered GPUs, seamless collaboration through shared workspaces, and the ability to scale resources dynamically as workloads grow.
Code with GPU Power
Experience lightning-fast AI coding on Codeanywhere's GPU-enabled workspace, powered by Daytona's NVIDIA infrastructure.
Get Started with GPU Workspaces
Daytona enabled Codeanywhere to achieve one of its core objectives: simplifying complex development tasks and making them accessible to everyone. With GPU workspaces now as easy to spin up as any other environment, Codeanywhere extends its solution to a broader audience, which now include AI Engineers, Researchers and Data Scientists.
To test out GPUs for yourself just go to Codeanywhere.com, Create a Workspace and select GPU class. That's it!
