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From Anirudh Acharya <anirudhk...@gmail.com>
Subject Re: [VOTE] Release Apache MXNet (incubating) version 1.2.0.RC0
Date Mon, 23 Apr 2018 21:30:39 GMT
@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 <thomas.delteil1@gmail.com>
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 <dzzhen@amazon.com>:
>
> > @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 <anirudhkrec@gmail.com
> > <mailto:anirudhkrec@gmail.com>> 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 <
> > thomas.delteil1@gmail.com<mailto:thomas.delteil1@gmail.com>>
> > 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 <wujun.nju@gmail.com<mailto:wu
> > jun.nju@gmail.com>>:
> >
> > +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 <indhubharathi@gmail.com<mailto:
> > indhubharathi@gmail.com>> 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 <anirudh2290@gmail.com<mailto:
> > anirudh2290@gmail.com>> 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
> >
> >
> >
> >
> >
>

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