Hi Lumin, Quoting Lumin (2018-07-12 14:35:24) > My core concern is: > > Even if upstream releases their pretrained model under GPL license, > the freedom to modify, research, reproduce the neural networks, > especially "very deep" neural networks is de facto controled by > PROPRIETARIES. > > Justification to the concern: > > 1. CUDA/cuDNN is used by nearly all deep-learning researchers and > service providers. > > 2. Deep neural networks is extremely hard to train on CPU due to the > time cost. By leveraging cuDNN and powerful graphic cards, the > training process can be boosted up to more than 100x times > faster. That means, for example if a neural network can be > trained by GPU in 1 day, then the same thing would take a few > months on CPU. (Google's TPU and FPGA are not the point here) > > 3. A meaningful "modification" to neural network often refers to > "fine-tune", which is a similar process to "training". A > meaningful "reproduce" of neural network often refers to > "training starting from random initialization". > > 4. Due to 1. 2. and 3. , the software freedom is not complete. In a > pure freesoftware environment, that work cannot be reproduced, > modified, or even researched. Although CPU indeed can finish the > same work in several months or several years, but that's way too > much reluctant. > > In this way, the pretrained neural network is not totally "free" > even if it is licenced under GPL-*. None of the clauses in GPL is > violated, but the software freedom is limited. Perhaps I am missing something, but if _possible_ just 100x slower to use CPUs instead of GPUs, then I fail to recognize how it cannot be reproduced, modified, and researched 100x slower. Quite interesting question you raise! - Jonas -- * Jonas Smedegaard - idealist & Internet-arkitekt * Tlf.: +45 40843136 Website: http://dr.jones.dk/ [x] quote me freely [ ] ask before reusing [ ] keep private
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