on a separate but equally exciting note, how about we start to talk about a 1.0 release in the future and what everyone would want that to look like? I'll start a separate thread. :)
On Mon, Oct 2, 2017 at 9:07 PM, Dominic Divakaruni < dominic.divakar...@gmail.com> wrote: > Seb is talking about support for Cuda 9 and cuDNN 7. Pull requests below. > @ptrendx and Dick Carter are working through some performance issues but > should be done in a week (hopefully). > > Jun, Bhavin, > Tensor RT runtime is a different subject. Nvidia is helping build a > converter for MXNet models. Not sure on the ETA. Tensor RT helps accelerate > vision models on the V100, TX2, P4/40 etc... > > > - Enabling persistent batch norm with cuDNN 7: > https://github.com/apache/incubator-mxnet/pull/7876 > <https://github.com/apache/incubator-mxnet/pull/7876> > - Making mixed precision work with all optimizers:https://github.com/ > apache/incubator-mxnet/pull/7654 > <https://github.com/apache/incubator-mxnet/pull/7654> > - Faster IO pipeline needed for Volta:https://github.com/ > apache/incubator-mxnet/pull/7152 > <https://github.com/apache/incubator-mxnet/pull/7152>; > - Expose Tell in RecordIO reader:https://github.com/ > dmlc/dmlc-core/pull/301 > > > On Mon, Oct 2, 2017 at 8:44 PM, Bhavin Thaker <bhavintha...@gmail.com> > wrote: > >> Hi Seb: please use a different email thread for new topics of discussion. >> >> Hi Jun: I think Seb may be referring to Volta V100 support in MXNet and >> NOT >> P4/P40 inference accelerators. >> >> Corrections/clarifications welcome. >> >> Bhavin Thaker. >> >> On Mon, Oct 2, 2017 at 8:22 PM Jun Wu <wujun....@gmail.com> wrote: >> >> > Thanks for your attention, Seb. We are inclined to be cautious on what >> can >> > claim for this project. TensorRT has already supported converting >> > TensorFlow and Caffe models to its compatible format for fast inference, >> > but not MXNet. In this sense, it may not be fair to claim MXNet as the >> > first one supporting Nvidia Volta. >> > >> > What we are working on is more experimental and research oriented. We >> want >> > to get the first-hand materials in our own hands by building a INT-8 >> > inference prototype and have a thorough understanding on its strength >> and >> > limitation, rather than handing it off completely to TensorRT, which is >> > transparent to us. Considering that the project is experimental, it's >> still >> > too early to make a conclusion here as there are plenty of known/unknown >> > issues and unfinished work. >> > >> > On the other hand, we are glad to hear that Nvidia is working on >> supporting >> > model conversion from MXNet to TensorRT (Dom please correct me if I'm >> > mistaken). It would be super beneficial to MXNet on INT-8 if they could >> > open-source their work as we would be able to maintain and add new >> features >> > on our side. >> > >> > >> > On Mon, Oct 2, 2017 at 8:04 PM, Dominic Divakaruni < >> > dominic.divakar...@gmail.com> wrote: >> > >> > > 👏 >> > > >> > > On Mon, Oct 2, 2017 at 8:02 PM Seb Kiureghian <sebou...@gmail.com> >> > wrote: >> > > >> > > > It would be awesome if MXNet were the first DL framework to support >> > > Nvidia >> > > > Volta. What do you all think about cutting a v0.12 release once that >> > > > integration is ready? >> > > > >> > > > On Wed, Sep 27, 2017 at 10:38 PM, Jun Wu <wujun....@gmail.com> >> wrote: >> > > > >> > > > > I had been working on the sparse tensor project with Haibin. >> After it >> > > was >> > > > > wrapped up for the first stage, I started my work on the >> quantization >> > > > > project (INT-8 inference). The benefits of using quantized models >> for >> > > > > inference include much higher inference throughput than FP32 model >> > with >> > > > > acceptable accuracy loss and compact models saved on small >> devices. >> > The >> > > > > work currently aims at quantizing ConvNets, and we will consider >> > > > expanding >> > > > > it to RNN networks after getting good results for images. >> Meanwhile, >> > > it's >> > > > > expected to support quantization on CPU, GPU, and mobile devices. >> > > > > >> > > > >> > > -- >> > > >> > > >> > > Dominic Divakaruni >> > > 206.475.9200 Cell >> > > >> > >> > > > > -- > > > Dominic Divakaruni > 206.475.9200 <(206)%20475-9200> Cell > -- Dominic Divakaruni 206.475.9200 Cell
