Thanks Ziyi, May I know which platform did you notice the performance regression, CPU or GPU? ~20% regression would be a large gap.
Thanks, -Ciyong -----Original Message----- From: Patrick Mu <zm2...@columbia.edu> Sent: Monday, July 13, 2020 4:13 PM To: d...@mxnet.apache.org Subject: Re: RE: [VOTE] Release Apache MXNet (incubating) version 1.7.0.rc0 Hi Ciyong, I have reverted the commit, and I am able to train Yolov3 with no problem. However I also noticed there is a ~20% regression in 1.7 comparing with 1.6 in inference Yolov3 with Module API, so we are going to discuss tomorrow if that would be an issue for 1.7. Thanks, Ziyi On 2020/07/13 02:19:28, "Chen, Ciyong" <ciyong.c...@intel.com> wrote: > Hi Ziyi, Xingjian, > > Thanks for reporting the issues from GluonCV/AutoGluon perspective. > I just did a quick try by reverting the > https://github.com/apache/incubator-mxnet/pull/18358, then the behavior is > same as 1.6.0 with the cases in the gist > (https://gist.github.com/sxjscience/944066c82e566f1b89b01fa226678890). > > Considering there's many end-users using Gluon based API/models, and > introducing a new patch to fix this issue could be risky, so I agree that > reverting this PR (#18358) might be the best option for the 1.7.0 release. > But I'm considering is there any other test cases to cover this feature, > which could be helpful to track this kind of code changes in future, or can > you help to verify if this revert do resolve the broken issue at your side? > > > Thus, the real issue is: Should we supporting pickling a Gluon Block? If > > not, should we support combining multiprocessing.pool with the Gluon Block? > Seems it's more like a new feature for MXNet Gluon Block, probably we can > make it available in the next patch/minor release? > > Thanks, > -Ciyong > > -----Original Message----- > From: Xingjian SHI <xsh...@connect.ust.hk> > Sent: Saturday, July 11, 2020 4:27 AM > To: dev@mxnet.incubator.apache.org; d...@mxnet.apache.org > Subject: Re: [VOTE] Release Apache MXNet (incubating) version 1.7.0.rc0 > > Thanks Ziyi, > > I've discovered the same issue when I'm trying to use AutoGluon with 1.7.0rc0 > and would like to share my finding: > > Basically, I don't think Gluon Block is designed to be pickleble. But > pickling do work for some cases in the old version: > > I've included two cases in the gist > (https://gist.github.com/sxjscience/944066c82e566f1b89b01fa226678890). > > - Case1: we construct a gluon block, hybridize it and feed one NDArray to > help initialize the block. After that, it will no longer be pickleble. > - Case2: we just construct a gluon block and it will be pickleble in 1.6.0, > but won't be pickleble in 1.7.0. > > Thus, the real issue is: Should we supporting pickling a Gluon Block? If not, > should we support combining multiprocessing.pool with the Gluon Block? For > reference, PyTorch supports pickling the nn.Module as shown in: > https://gist.github.com/sxjscience/90b812a66d445e759c55eedc3ef93668 and also > in the doc > (https://pytorch.org/tutorials/beginner/saving_loading_models.html). > > Best, > Xingjian > > > On 7/10/20, 11:31 AM, "Patrick Mu" <zm2...@columbia.edu> wrote: > > Hi Ciyong, > > I just discovered an issue with the 1.7, which causes the Yolo training > with latest Gluon CV Yolo to fail. > > The PR that causes the failure is > https://github.com/apache/incubator-mxnet/pull/18358, which modifies basic > blocks of Gluon to fix a memory leak issue. > > Talked with Leonard, the author of the PR, and he said he found the root > cause, but patching that PR would modifies those Gluon basic blocks further, > which might be risky towards existing models and various customer models. > > So my 2-cents is reverting this PR in 1.7, and try patching the PR in 1.x > and 2.0, meaning that the 1.7 won't have memory usage optimized by that > feature. > > I'd like to hear what you think about this issue. > > Thanks, > Ziyi > > > On 2020/07/10 06:18:02, "Chen, Ciyong" <ciyong.c...@intel.com> wrote: > > Hi Community, > > > > I would like to call for action to test/validate/vote for the release > candidate (1.7.0.rc0) > > As there's not any voting result during the scheduled time window, I > would like to extend the time windows to July 13, 23:59:59 PST. > > Please prepare your time and provide feedback if you've tried with the > pre-release code bases, thanks! > > > > Best regards, > > Ciyong > > > > -----Original Message----- > > From: Chen, Ciyong <ciyong.c...@intel.com> > > Sent: Monday, July 6, 2020 10:48 PM > > To: d...@mxnet.apache.org > > Cc: Bob Paulin <b...@apache.org>; Henri Yandell <bay...@apache.org>; > Jason Dai <jason...@apache.org>; Markus Weimer <wei...@apache.org>; Michael > Wall <mjw...@apache.org> > > Subject: RE: [VOTE] Release Apache MXNet (incubating) version 1.7.0.rc0 > > > > For the language bindings and windows platform, may I have your support > to help verify these features? Thanks! > > > > @lanking520 to help verify the Scala/Java @gigasquid to help verify the > Clojure > > @hetong007 to help verify the R > > @yajiedesign to help verify the windows platform > > > > Best regards, > > Ciyong Chen > > > > -----Original Message----- > > From: Chen, Ciyong <ciyong.c...@intel.com> > > Sent: Monday, July 6, 2020 10:39 PM > > To: d...@mxnet.apache.org > > Cc: Bob Paulin <b...@apache.org>; Henri Yandell <bay...@apache.org>; > Jason Dai <jason...@apache.org>; Markus Weimer <wei...@apache.org>; Michael > Wall <mjw...@apache.org> > > Subject: [VOTE] Release Apache MXNet (incubating) version 1.7.0.rc0 > > > > Dear MXNet community, > > > > This is the vote to release Apache MXNet (incubating) version 1.7.0. > Voting will start July 6, 23:59:59 PST and close on July 9, 23:59:59 PST. > > > > Link to release notes: > > https://cwiki.apache.org/confluence/display/MXNET/1.7.0+Release+notes > > > > Link to release candidate: > > https://github.com/apache/incubator-mxnet/releases/tag/1.7.0.rc0 > > > > Link to source and signatures on apache dist server: > > > https://dist.apache.org/repos/dist/dev/incubator/mxnet/1.7.0.rc0<https://dist.apache.org/repos/dist/dev/incubator/mxnet/1.7.0.rc0/> > > > > Please remember to TEST first before voting accordingly: > > +1 = approve > > +0 = no opinion > > -1 = disapprove (provide reason) > > > > Additional notes: > > > > * There was an issue and discussion[1] regarding on a few numpy > operators failed due to numpy 1.19.0 released on Jun 20, 2020, which exists > in all branches (works with numpy <= 1.18.5). As numpy operator is still an > experimental feature in 1.7.0 release and mainly targeting in MXNet 2.0 > release, so I decided to not block the voting and instead let the Community > decide whether this is a blocker for the release. > > > > [1] https://github.com/apache/incubator-mxnet/issues/18600 > > > > Best regards, > > Ciyong Chen > > > > > >
