Hi, Some additional data points:
* In order to train the most widely used convolutional neural network, I use 4 * GTX 1080Ti cards on an 8-card machine. The network occupies around 40 GiB of video memory during training. * GTX 1080 is the lowest standard for research or production. More common choices for rich groups are the Nvidia Titan X cards or Tesla cards. * The state-of-the-art natural language representation, BERT, takes 2 weeks to train on TPU at a cost about $500. https://github.com/google-research/bert CPU cannot do that in finite time. For the reproducibility problem: In the definition of "Free Model", I mentioned that the model *should be reproducible* with a fixed random seed. This is also a good practice for ML/DL engineers and researchers. On 2019-05-21 12:10, [email protected] wrote: > Hi > > Le 21 mai 2019 13:45, Mo Zhou <[email protected]> a écrit : > >> It's always good if we can do these things purely with our archive. >> >> However sometimes it's just not easy to enforce: datasets used by DL >> >> are generally large, (several hundred MB ~ several TB or even >> larger). > > And even with the data, the training might need an awfully powerful > box *and* weeks of computation *and* some of the algorithms aren't > deterministic, so reproducibility is a problem, not only for Debian > but for the scientific community at large. > > jpuydt
