MyActualUserName99
MyActualUserName99 OP t1_j669vl1 wrote
Reply to comment by [deleted] in [D] ImageNet2012 Advice by MyActualUserName99
Where at? Everything I can find is like 3$ . Cheapest I can find is google colab $10 for 7.5 hours, but you’re limited by ram and your node will drop at any given time
MyActualUserName99 OP t1_j668o4r wrote
Reply to comment by arg_max in [D] ImageNet2012 Advice by MyActualUserName99
I’ll definitely check it out!
MyActualUserName99 OP t1_j668iiq wrote
Reply to comment by [deleted] in [D] ImageNet2012 Advice by MyActualUserName99
Yes, they had 40GB A100 GPUs in 2015
MyActualUserName99 t1_irapufa wrote
Reply to comment by Knurpel in Deeplearning and multi-gpu or not by ronaldxd2
If you’re using Tensorflow, adding multiple GPUs is extremely easy. Just have to call some functions and make a strategy:
MyActualUserName99 t1_jaes8gh wrote
Reply to [Discussion] Open Source beats Google's AutoML for Time series by fedegarzar
My biggest concerns with this assessment is the lake of dataset diversity. Sure, you can get one method to outperform another on one or two datasets, but to be able to do so across many datasets, all of various sizes, is much much harder.
From what I can tell, the open source StatsForecast was able to outperform BigQuery for an extremely small dataset (Citibike Trips) and one large dataset (Liquor Sales). Granted the much larger dataset, to me, is much more impressive to outperform upon than the smaller. But to make such a definitive conclusion that Open Source is better than commercial would require testing across a plethora of datasets, all of different sizes, domains, etc.