kalyc opened a new issue #13211: train_cifar10 example not working with MXNet v1.3 URL: https://github.com/apache/incubator-mxnet/issues/13211 Note: Providing complete information in the most concise form is the best way to get help. This issue template serves as the checklist for essential information to most of the technical issues and bug reports. For non-technical issues and feature requests, feel free to present the information in what you believe is the best form. For Q & A and discussion, please start a discussion thread at https://discuss.mxnet.io ## Description train_cifar10 example not working as described in the readme Following error message is thrown: ``` Traceback (most recent call last): File "train_mnist.py", line 96, in <module> fit.fit(args, sym, get_mnist_iter) File "/home/ubuntu/incubator-mxnet/example/image-classification/common/fit.py", line 220, in fit lr, lr_scheduler = _get_lr_scheduler(args, kv) File "/home/ubuntu/incubator-mxnet/example/image-classification/common/fit.py", line 53, in _get_lr_scheduler base_lr=args.lr)) TypeError: __init__() got an unexpected keyword argument 'base_lr' ``` ## Environment info (Required) ``` ----------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/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.3.0 Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet Commit Hash : b3be92f4a48bce62a5a8424271871c2f81c8f7f1 ----------System Info---------- Platform : Linux-4.4.0-1070-aws-x86_64-with-debian-stretch-sid system : Linux node : ip-172-31-13-114 release : 4.4.0-1070-aws version : #80-Ubuntu SMP Thu Oct 4 13:56:07 UTC 2018 ----------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): 32 On-line CPU(s) list: 0-31 Thread(s) per core: 2 Core(s) per socket: 16 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: 2701.871 CPU max MHz: 3000.0000 CPU min MHz: 1200.0000 BogoMIPS: 4600.06 Hypervisor vendor: Xen Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 46080K NUMA node0 CPU(s): 0-31 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single kaiser fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0018 sec, LOAD: 0.5779 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0474 sec, LOAD: 0.1173 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.3806 sec, LOAD: 0.1539 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0098 sec, LOAD: 0.2449 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0095 sec, LOAD: 0.3935 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0009 sec, LOAD: 0.0534 sec. ``` Package used (Python/R/Scala/Julia): Python For Scala user, please provide: 1. Java version: (`java -version`) 2. Maven version: (`mvn -version`) 3. Scala runtime if applicable: (`scala -version`) For R user, please provide R `sessionInfo()`: ## Build info (Required if built from source) Compiler (gcc/clang/mingw/visual studio): MXNet commit hash: 89f3091f06b3ab82aab27f6a48d6e2b6223b2a09 Build config: (Paste the content of config.mk, or the build command.) ## Error Message: (Paste the complete error message, including stack trace.) ## Minimum reproducible example ## Steps to reproduce (Paste the commands you ran that produced the error.) 1. Run `python train_cifar10.py --network resnet --num-layers 110 -- batch-size 128 --gpus 0,1` ## What have you tried to solve it? 1. Installed v1.3 from source on Base DLAMI 2. Used DLAMI environment - `mxnet_p36` to test this example Error thrown in both the cases @mxnet-label-bot add[Example, Python]
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