Tensor Compilers: Comparing PlaidML, Tensor Comprehensions, and TVM

AGPL would be a restriction if you need to deploy this model on top of PlaidML in production. It is still very useful during the training time after which the neural network can be offloaded into production framework such as tensorflow.

Thanks, I updated the wording to call out that we developed it. We dual license the software so there's a commercial option similar to MySQL, just get in touch. The open source project is mostly a way to support research/education.

Thanks, I've shared this with our team to have a look. There was some subtlety to timing the right thing and it's possible we missed something.

Thank you so much for pointing this out. We'll get updated numbers out soon. How did you benchmark plaid, out of curiosity? The error which I correct here (https://github.com/brianretford/nnvm-rocm/blob/master/mxnet_...) was caused by a desire to roughly approximate how keras does things, and plaidbench w/ keras is the easiest way for us to evaluate things, though it definitely adds in a lot of overhead. My script roughly matches the numbers I get out of your script, though I will say that I think the TVM time_evaluator should be calling Sync on the inside of its loop, to be fair (which I patched it to do to compare against your methodology). It doesn't make a huge difference, but it does exist.

If I just pull the overall kernel runtime from our logs, I get ~525 inferences/sec.