commit | ab222114afb234bce6ebffb99b09e2b16d2ddcec | [log] [tgz] |
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author | Paweł Głomski <pawel.glomski@intel.com> | Wed Sep 29 17:41:36 2021 +0200 |
committer | GitHub <noreply@github.com> | Wed Sep 29 11:41:36 2021 -0400 |
tree | 6a8cab0c35449370ad20c1a08f65a37b55bb34b8 | |
parent | 80d72b534d4bd07d2db929c5c8dbb6181bdf62ea [diff] |
[BACKPORT][BUGFIX][FEATURE] Add oneDNN 1D and 3D deconvolution support and fix bias (#20292) * [v1.x][BUGFIX] Implement oneDNN deconvolution primitives to deconvolution 2D (#20107) * Use mkldnn deconvolution primitive in deconvolution * Apply clang-format * Refactor deconvolution version 1 * Refactor deconvolution version 2 and use permute_axes in IOLogicalSwapDesc * Refactor deconvolution version 3 * Enable Deconvolution2D test * Fix sanity * Fix windows builds * Fix deconvolution with bias test * [v1.x][FEATURE] Add MKLDNN Deconvolution 1D and 3D support (#20137) * Use MXNET_USE_ONEDNN * Fix test * Apply formatter * Add native support for 3D deconvolution * Remove outdated check * Replace math.prod with np.prod * Check convolution layout only when it has value * Remove outdated check * Change tests * Increase default workspace size to mach convolution * Fix deconv workspace size * Increase default deconv workspace size in python API * Disable 3D tests for GPU * Add deconv arguments checks * Remove next_impl calls until it is fixed * Share workspace * Fix documentation * Add test_deconv_dilation * Fix check * Fix include order
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scalable to many GPUs and machines.
MXNet is more than a deep learning project. It is a community on a mission of democratizing AI. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.
Licensed under an Apache-2.0 license.
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Follow MXNet Development on Github | See what's going on in the MXNet project. |
MXNet Confluence Wiki for Developers | MXNet developer wiki for information related to project development, maintained by contributors and developers. To request write access, send an email to send request to the dev list . |
dev@mxnet.apache.org mailing list | The “dev list”. Discussions about the development of MXNet. To subscribe, send an email to dev-subscribe@mxnet.apache.org . |
discuss.mxnet.io | Asking & answering MXNet usage questions. |
Apache Slack #mxnet Channel | Connect with MXNet and other Apache developers. To join the MXNet slack channel send request to the dev list . |
Follow MXNet on Social Media | Get updates about new features and events. |
Keep connected with the latest MXNet news and updates.
MXNet emerged from a collaboration by the authors of cxxnet, minerva, and purine2. The project reflects what we have learned from the past projects. MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency.
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015