How Computer Vision Researchers Use Grid For Their Infrastructure Requirements

Clutterbot, a New Zealand startup developing autonomous home-cleaning robots, uses computer vision and artificial intelligence to navigate homes, identify objects such as toys on floors, and sort them into containers through a mobile app. Senior Machine Learning Engineer Syed Riaz and his team faced costly delays from having to build and maintain their own machine-learning infrastructure while developing the robot’s object detection, tracking, and multitask-learning capabilities. To reduce this burden, the company adopted Grid after discovering it through PyTorch Lightning, using Grid Sessions to prototype and train models on demand with automatically allocated GPUs and preinstalled development tools. The platform’s ability to pause and resume sessions helped Clutterbot avoid unnecessary computing costs, while reducing infrastructure work, CUDA and driver setup, and other operational overhead so the team could concentrate on research, product development, and business priorities.

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