@ThomasDelteil Sorry for the late reply. Yes, it should indeed work when compiled with mkldnn and cuda.
On Sat, Apr 21, 2018 at 5:15 PM, Thomas DELTEIL <[email protected]> wrote: > @Anirudh, thanks for looking into it! However I do not understand what you > mean by 'set as CPU and not GPU'? MXNet compiled with mkldnn and cuda is > supposed to be able to work with both context no? There are other tutorials > that are running successfully on both CPU and GPU context. > > @Da to reproduce: > > Download the source of 1.2.0.rc0 and extract it, cd into it. > > make docs > make clean > make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 > USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1 USE_MKLDNN=1 > export PYTHONPATH=$(pwd)/python > cd tests/nightly > python test_tutorial.py --tutorial onnx/super_resolution > > you can also start a jupyter notebook server and try to run > docs/_build/html/tutorials/onnx/super_resolution.ipynb > > > > 2018-04-21 15:08 GMT-07:00 Zheng, Da <[email protected]>: > > > @ThomasDelteil could you show me how to reproduce the problem? I'll take > > it a look as well. > > > > Best, > > Da > > > > Sent from my iPhone > > > > On Apr 21, 2018, at 1:12 PM, Anirudh Acharya <[email protected] > > <mailto:[email protected]>> wrote: > > > > @ThomasDelteil that might be due to the fact that in the example, the > > context is being set as CPU and not GPU. > > But I will still take a look as soon as possible. > > > > > > Regards > > Anirudh > > > > On Sat, Apr 21, 2018 at 11:10 AM, Thomas DELTEIL < > > [email protected]<mailto:[email protected]>> > > wrote: > > > > *-0* > > > > compiled from source on GPU CUDA/CUDNN, tutorials run fine. > > > > However: > > Compiled from source and adding USE_MKLDNN=1, the onnx/super_resolution > > tutorial is crashing on this line: > > > > ``` > > from collections import namedtuple > > Batch = namedtuple('Batch', ['data']) > > > > # forward on the provided data batch > > mod.forward(Batch([mx.nd.array(test_image)])) > > ``` > > > > Stack trace returned 8 entries: > > [bt] (0) > > /home/ubuntu/apache-mxnet-src-1.2.0.rc0-incubating/python/ > > mxnet/../../lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b) > > [0x7feef615721b] > > [bt] (1) > > /home/ubuntu/apache-mxnet-src-1.2.0.rc0-incubating/python/ > > mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~ > > LogMessageFatal()+0x28) > > [0x7feef6158258] > > [bt] (2) > > /home/ubuntu/apache-mxnet-src-1.2.0.rc0-incubating/python/ > > mxnet/../../lib/libmxnet.so(mxnet::engine::ThreadedEngine: > > :ExecuteOprBlock(mxnet::RunContext, > > mxnet::engine::OprBlock*)+0xfa9) [0x7feef8b1ad49] > > [bt] (3) > > /home/ubuntu/apache-mxnet-src-1.2.0.rc0-incubating/python/ > > mxnet/../../lib/libmxnet.so(std::_Function_handler<void > > (std::shared_ptr<dmlc::ManualEvent>), > > mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine:: > > OprBlock*, > > bool)::{lambda()#1}::operator()() > > const::{lambda(std::shared_ptr<dmlc::ManualEvent>)#1}>::_ > > M_invoke(std::_Any_data > > const&, std::shared_ptr<dmlc::ManualEvent>&&)+0xe2) [0x7feef8b30d82] > > [bt] (4) > > /home/ubuntu/apache-mxnet-src-1.2.0.rc0-incubating/python/ > > mxnet/../../lib/libmxnet.so(std::thread::_Impl<std::_Bind_ > > simple<std::function<void > > (std::shared_ptr<dmlc::ManualEvent>)> (std::shared_ptr<dmlc:: > > ManualEvent>)> > > ::_M_run()+0x4a) [0x7feef8b2af1a] > > [bt] (5) /home/ubuntu/anaconda3/bin/../lib/libstdc++.so.6(+0xafc5c) > > [0x7fef7cc79c5c] > > [bt] (6) /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba) [0x7fef7dec36ba] > > [bt] (7) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7fef7dbf941d] > > > > Depending on how experimental we consider MKLDNN, that could be a *-1 > *for > > me. > > > > 2018-04-21 9:01 GMT-07:00 Jun Wu <[email protected]<mailto:wu > > [email protected]>>: > > > > +1 > > > > Compiled from source. Ran the model quantization example. Both quantized > > model generation and inference can run successfully. > > > > On Fri, Apr 20, 2018 at 5:14 PM, Indhu <[email protected]<mailto: > > [email protected]>> wrote: > > > > +1 > > > > Compiled from source on P3 instance. Tested the SSD example and some > > Gluon > > examples. > > > > On Wed, Apr 18, 2018, 7:40 PM Anirudh <[email protected]<mailto: > > [email protected]>> wrote: > > > > Hi everyone, > > > > This is a vote to release Apache MXNet (incubating) version 1.2.0. > > Voting > > will start now (Wednesday, April 18th) and end at 7:40 PM PDT, > > Saturday, > > April 21st. > > > > Link to the release notes: > > > > > > https://cwiki.apache.org/confluence/display/MXNET/ > > Apache+MXNet+%28incubating%29+1.2.0+Release+Notes > > > > Link to the release candidate 1.2.0.rc0: > > https://github.com/apache/incubator-mxnet/releases/tag/1.2.0.rc0 > > > > View this page, click on "Build from Source", and use the source code > > obtained from the 1.2.0.rc0 tag: > > https://mxnet.incubator.apache.org/install/index.html > > > > (Note: The README.md points to the 1.2.0 tag and does not work at the > > moment.) > > > > Please remember to TEST first before voting accordingly: > > +1 = approve > > +0 = no opinion > > -1 = disapprove (provide reason) > > > > Thanks, > > > > Anirudh > > > > > > > > > > >
