jschmitz28 opened a new issue #14939: MXNet silently produces bad results (all zeroes) when allocating NDArray larger than 2^32 in size via random_normal URL: https://github.com/apache/incubator-mxnet/issues/14939 ## Description MXNet silently produces bad results (all zeroes) when allocating NDArray larger than 2^32 in size via random_normal(). ## Environment info (Required) Base deep learning AMI on AWS: ami-01ac4e28da63bac3c [ec2-user@ip-10-2-68-132 ~]$ source activate mxnet_p36 (mxnet_p36) [ec2-user@ip-10-2-68-132 ~]$ python diagnose.py ----------Python Info---------- Version : 3.6.5 Compiler : GCC 7.2.0 Build : ('default', 'Apr 29 2018 16:14:56') Arch : ('64bit', '') ------------Pip Info----------- Version : 10.0.1 Directory : /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.4.0 Directory : /home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet Commit Hash : a03d59ed867ba334d78d61246a1090cd1868f5da ----------System Info---------- Platform : Linux-4.14.104-78.84.amzn1.x86_64-x86_64-with-glibc2.9 system : Linux node : ip-10-2-68-132 release : 4.14.104-78.84.amzn1.x86_64 version : #1 SMP Mon Mar 4 19:19:37 UTC 2019 ----------Hardware Info---------- machine : x86_64 processor : x86_64 Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 8 On-line CPU(s) list: 0-7 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 NUMA node(s): 1 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz Stepping: 1 CPU MHz: 2702.223 BogoMIPS: 4600.12 Hypervisor vendor: Xen Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 46080K NUMA node0 CPU(s): 0-7 ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0023 sec, LOAD: 0.8111 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0007 sec, LOAD: 0.0224 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0006 sec, LOAD: 0.3334 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0007 sec, LOAD: 0.1205 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0020 sec, LOAD: 0.0719 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0007 sec, LOAD: 0.0281 sec. ## Error Message: None (although I would prefer if there were an error compared to bad results) ## Minimum reproducible example source activate mxnet_p36 && python -c 'import mxnet; print(mxnet.nd.random_normal(shape=(42949672,50)))' [[0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]] <NDArray 42949672x50 @cpu(0)> ## Steps to reproduce 1. Launch p3.2xlarge with base deep learning AMI 2. source activate mxnet_p36 && python -c 'import mxnet; print(mxnet.nd.random_normal(shape=(42949672,50)))'
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