I updated to Gluon-cv 0.4.0 and received this error doing the Instance Segmentation tutorial using MASK-RCNN MXNetError: [12:14:12] src/ndarray/ndarray.cc:752: Check failed: !IsMKLDNNData() We can’t generate TBlob for MKLDNN data. Please use Reorder2Default() to generate a new NDArray. Using mxnet version 1.4.0
Which MXNet version are you using?
Just tried it on another machine with mxnet 1.4.0 and gluon-cv 0.30 and received same the only thing different about this version of 1.4.0 is that it was build with intel mkl 2019 release 3 all my previous builds have been with mkl 2018 release 4 . I did a rebuild with mkl 2018 and it made no difference still receive the same error.
When you installed MXNet did you use MKL version? By default, MXNet comes without MKL support and you need to install a version which has mkl suffix. For example, for CUDA 9.0 and MKL supported version of MXNet you need to type:
pip install mxnet-cu90mkl
Similar for CUDA 9.1, 9.2, 10.0 - replace the number in the command.
Try to reinstall mxnet and let me know if it helped.
I have always built from source since I started using MXNet version 0.7 never used pip install, using Intel’s MKL, jemalloc and nccl. Never had any issues until now, and that is the only one I have a problem with finished the rest of gluoncv tutorial without any issues and no problems with gluonnlp. This was the first time I used mkl 2019 but went back and did a build with 2017 release 4 and the problem persisted. I start with the same settings you list for your builds then add the ones for nccl and use_cpp_package . I had gone through that tutorial before without any issues not sure why my builds are having trouble now and only with that one.
Did a pip install and it worked fine. Uninstalled it and reinstalled mine will trouble shoot on my own sorry for the confusion in hindsight I should have noted that I build from source just never occurred to me since I have never used a pip install for mxnet.
No problem at all. Feel free to post your findings - it might benefit other people as well!