{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/79d5dfd3cba4401fb2fd5967a8438391\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/79d5dfd3cba4401fb2fd5967a8438391-8f638c6e4730dd71.gif","duration":981.752,"title":"Run On GPU Package Setup Guide","description":"This Loom explains how to run the Run on GPU package using an Anaconda environment and a GitHub-based configuration. It covers creating and activating a conda environment, installing the run on GPU Python package, and updating a run on GPU text file with setup and build commands including NVIDIA compilation commands for CUDA code. The speaker notes that the first run may require signing into Google Colab and can time out if not signed in, but subsequent runs should be smooth and faster, producing results like metric addition from CUDA execution. It also advises not to touch Google Colab while the automated job is running and rerunning if a timeout error occurs."}