> Benchmarking cutting-edge graph-processing algorithms running on 128-core clusters against a single-threaded 2014 Macbook Pro. The laptop consistently wins, sometimes by an order of magnitude.
LOL, this hits close to home. My company had a modeling specific VM set up to run our predictive modeling pipelines. Typical pipeline is about 50,000 to 5 million rows of training data. At best, using an expensive VM, we managed to get 2x training speed from lightgbm on the VM vs my personal work laptop. We tried GPU boxes, hyper threaded machines, you name it. At the end of the day, we decided to let our data scientists just run models locally.
LOL, this hits close to home. My company had a modeling specific VM set up to run our predictive modeling pipelines. Typical pipeline is about 50,000 to 5 million rows of training data. At best, using an expensive VM, we managed to get 2x training speed from lightgbm on the VM vs my personal work laptop. We tried GPU boxes, hyper threaded machines, you name it. At the end of the day, we decided to let our data scientists just run models locally.