Commit Graph

2355 Commits

Author SHA1 Message Date
Glenn Jocher 6a05cf56c2 updates 2019-11-30 18:45:43 -08:00
Glenn Jocher 8be4b41b3d updates 2019-11-30 18:19:17 -08:00
Glenn Jocher 34155887bc updates 2019-11-30 17:48:21 -08:00
Glenn Jocher 6992c68e33 updates 2019-11-30 17:47:49 -08:00
Glenn Jocher 0f6954fa04 updates 2019-11-30 17:47:33 -08:00
Glenn Jocher a699c901d3 updates 2019-11-30 17:38:29 -08:00
Glenn Jocher f2ec1cb9ea updates 2019-11-30 17:19:44 -08:00
Glenn Jocher 4e0067cdc9 updates 2019-11-30 17:14:53 -08:00
Glenn Jocher e28a425384 updates 2019-11-30 17:13:21 -08:00
Glenn Jocher 3cdbf246c9 updates 2019-11-30 17:03:47 -08:00
Glenn Jocher 1ff01f0973 updates 2019-11-30 16:58:56 -08:00
Glenn Jocher ff41a15a2b updates 2019-11-30 15:34:57 -08:00
Glenn Jocher 8a13bf0f3f updates 2019-11-30 15:33:10 -08:00
Glenn Jocher 23ca2f2e7e updates 2019-11-30 15:32:39 -08:00
Glenn Jocher 9f0273a459 updates 2019-11-30 14:43:23 -08:00
Glenn Jocher 937d8fa53e updates 2019-11-30 14:17:32 -08:00
Glenn Jocher 8d4790349b updates 2019-11-30 14:16:01 -08:00
Glenn Jocher 5a1bc71406 updates 2019-11-30 13:20:22 -08:00
Glenn Jocher 8afc18e028 updates 2019-11-30 13:00:20 -08:00
Glenn Jocher f365946c2f updates 2019-11-30 12:43:41 -08:00
Glenn Jocher e613bbc88c updates 2019-11-29 19:10:01 -08:00
Glenn Jocher 77012f8f97 updates 2019-11-29 18:20:57 -08:00
Glenn Jocher 51d666a81a updates 2019-11-28 09:05:13 -10:00
Glenn Jocher 6258061a81 updates 2019-11-27 23:36:02 -10:00
Glenn Jocher bccff3bfc1 updates 2019-11-27 23:31:25 -10:00
Glenn Jocher 340e0371f8 updates 2019-11-27 22:36:01 -10:00
Glenn Jocher 9e9a6a1425 updates 2019-11-27 15:50:29 -10:00
Glenn Jocher 82b62c9855 updates 2019-11-27 15:50:00 -10:00
Glenn Jocher 4b251406e2 updates 2019-11-27 15:04:05 -10:00
Glenn Jocher 91fca0e17d updates 2019-11-27 15:03:05 -10:00
Glenn Jocher 9319ae8ff9 updates 2019-11-27 15:00:41 -10:00
Glenn Jocher 413afab11c updates 2019-11-27 14:59:46 -10:00
Glenn Jocher 9c1d7d5248 updates 2019-11-27 14:52:33 -10:00
Glenn Jocher ea19c33a87 updates 2019-11-27 14:35:18 -10:00
Glenn Jocher 3dec99b16c updates 2019-11-26 16:03:45 -10:00
Glenn Jocher 0417b3a527 updates 2019-11-26 13:53:05 -10:00
Glenn Jocher 78a2de52b5 updates 2019-11-26 13:23:47 -10:00
Glenn Jocher b04392e298 updates 2019-11-26 12:59:13 -10:00
Glenn Jocher 40ae87cb46 updates 2019-11-26 12:36:21 -10:00
Glenn Jocher 0fe40cb687 updates 2019-11-26 12:34:47 -10:00
Glenn Jocher 92f742618c updates 2019-11-26 10:26:14 -10:00
Glenn Jocher b269ed7b29 updates 2019-11-25 18:42:48 -10:00
Glenn Jocher 3c57ff7b1b updates 2019-11-25 17:24:05 -10:00
Glenn Jocher 90cfb91858 updates 2019-11-25 17:13:10 -10:00
Glenn Jocher 75e8ec323f updates 2019-11-25 11:45:28 -10:00
Glenn Jocher 0245ff9133 updates 2019-11-25 08:26:41 -10:00
Francisco Reveriano 26e3a28bee Update train.py for distributive programming (#655)
When attempting to running this function in a multi-GPU environment I kept on getting a runtime issue. I was able to solve this problem by passing this keyword. I first found the solution here: 
https://github.com/pytorch/pytorch/issues/22436
and in the pytorch tutorial

'RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by (1) passing the keyword argument find_unused_parameters=True to torch.nn.parallel.DistributedDataParallel; (2) making sure all forward function outputs participate in calculating loss. If you already have done the above two steps, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's forward function. Please include the loss function and the structure of the return value of forward of your module when reporting this issue (e.g. list, dict, iterable). '
2019-11-24 22:21:36 -10:00
Glenn Jocher a0ef217842 updates 2019-11-24 20:10:39 -10:00
Glenn Jocher 9b55bbf9e2 updates 2019-11-24 20:08:24 -10:00
Glenn Jocher 7773651e8e updates 2019-11-24 18:38:30 -10:00