[FEATURE] Add g5 instance to CI (#20876)

* Add g5 instance to jenkinsfiles where both p3 and g4 are mentioned

* Remove reference to non-existent restricted-mxnetlinux-gpu-g5

* Enable unittest job on g5

* Fix Jenkinsfile_unix_gpu syntax

* Include A10G arch 86 in build for g5

* Update is_TF32_enabled() for SM arch > 80

* Remove gpu arch 86 from centos builds on cuda 10

* Fix test_convolution_{grouping,dilated_impulse_response}, test_np_linalg_qr

* Fix test_convolution_grouping on A100

* Fix test_rnn_unroll_variant_length

* Fix test_convolution_dilated_impulse_response

* Skip test_np_standard_binary_funcs test of 0-dim array broadcast

* Temporarily add '-s' to pytest cpu tests

* Revert "Temporarily add '-s' to pytest cpu tests"

This reverts commit 4a9056a26f8c210497e3b5ed2318e30c8c2dbc5e.

* Improve test_rnn_layers_fp{16,32} invocation

* Pin MarkupSafe==2.0.1 to avoid soft_unicode import failure

* Run test_rnn_layers_fp32 only when cuDNN is present

* Fix potential out-of-bounds write in count_sketch.cu

* Revert "Pin MarkupSafe==2.0.1 to avoid soft_unicode import failure"

This reverts commit ae17b1f2af787427740c66a05ee1fb733ea56dd3.
10 files changed
tree: f36094c15fc04af07a68f9bb0ed26f2d5ecef468
  1. .github/
  2. 3rdparty/
  3. benchmark/
  4. cd/
  5. ci/
  6. cmake/
  7. config/
  8. contrib/
  9. cpp-package/
  10. docker/
  11. docs/
  12. example/
  13. include/
  14. licenses/
  15. plugin/
  16. python/
  17. src/
  18. tests/
  19. tools/
  20. .asf.yaml
  21. .clang-format
  22. .clang-tidy
  23. .cmakelintrc
  24. .codecov.yml
  25. .git-blame-ignore-revs
  26. .gitattributes
  27. .gitignore
  28. .gitmodules
  29. .licenserc.yaml
  30. .mxnet_root
  31. CMakeLists.txt
  34. conftest.py
  37. DNNL_README.md
  38. doap.rdf
  40. NEWS.md
  41. NOTICE
  42. prospector.yaml
  43. pytest.ini
  44. rat-excludes
  45. README.md
  46. readthedocs.yml
  47. SECURITY.md
  48. snap.python


Apache MXNet (incubating) for Deep Learning

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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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  • NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.
  • Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
  • Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO.
  • Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
  • Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
  • Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia.
  • Cloud-friendly and directly compatible with AWS and Azure.


What's New

Ecosystem News

Stay Connected

Follow MXNet Development on GithubSee 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 listThe “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 MediaGet updates about new features and events.

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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