fullfanta opened a new issue #14790: training speed at 1.4.0 is slower than 1.3.1 with large number of classes. URL: https://github.com/apache/incubator-mxnet/issues/14790 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 (Brief description of the problem in no more than 2 sentences.) Compared 1.3.1 and 1.4.0, training speed of 1.4.0 is slower than 1.3.1 with large number of classes, for example 80000. ## Environment info (Required) ----------Python Info---------- Version : 3.5.2 Compiler : GCC 5.4.0 20160609 Build : ('default', 'Nov 12 2018 13:43:14') Arch : ('64bit', 'ELF') ------------Pip Info----------- Version : 10.0.1 Directory : /hanmail/.local/lib/python3.5/site-packages/pip ----------MXNet Info----------- Version : 1.4.0 Directory : /usr/local/lib/python3.5/dist-packages/mxnet Commit Hash : a03d59ed867ba334d78d61246a1090cd1868f5da ----------System Info---------- Platform : Linux-4.4.0-78-generic-x86_64-with-Ubuntu-16.04-xenial system : Linux node : * release : 4.4.0-78-generic version : #99-Ubuntu SMP Thu Apr 27 15:29:09 UTC 2017 ----------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): 48 On-line CPU(s) list: 0-47 Thread(s) per core: 2 Core(s) per socket: 12 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz Stepping: 1 CPU MHz: 2195.156 BogoMIPS: 4392.89 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 30720K NUMA node0 CPU(s): 0-11,24-35 NUMA node1 CPU(s): 12-23,36-47 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb intel_pt tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdseed adx smap xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm arat pln pts Package used (Python/R/Scala/Julia): python3.5 ## Build info (Required if built from source) I installed through pip3. pip3 install mxnet-cu92==1.3.1 pip3 install mxnet-cu92==1.4.0 ## Minimum reproducible example I used imagenet classification benchmark. (https://github.com/apache/incubator-mxnet/blob/master/example/image-classification/train_imagenet.py) Default network is resnet with 50 layers. Number of classes is 80000. command is simple as ``` python3 train_imagenet.py --benchmark=1 --gpus=0,1,2,3,4,5,6,7 --batch-size=1024 --num-classes=80000 ``` following result is from 1.4.0 ``` INFO:root:start with arguments Namespace(batch_size=1024, benchmark=1, brightness=0, contrast=0, data_nthreads=4, data_train=None, data_train_idx='', data_val=None, data_val_idx='', disp_batches=20, dtype='float32', fill_value=127, gc_threshold=0.5, gc_type='none', gpus='0,1,2,3,4,5,6,7', image_shape='3,224,224', initializer='default', kv_store='device', load_epoch=None, loss='', lr=0.1, lr_factor=0.1, lr_step_epochs='30,60', macrobatch_size=0, max_crop_size=-1, max_random_area=1, max_random_aspect_ratio=0, max_random_h=0, max_random_l=0, max_random_rotate_angle=0, max_random_s=0, max_random_scale=1, max_random_shear_ratio=0, min_crop_size=-1, min_random_area=1, min_random_aspect_ratio=None, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network='resnet', num_classes=80000, num_epochs=80, num_examples=1281167, num_layers=50, optimizer='sgd', pad_size=0, pca_noise=0, profile_server_suffix='', profile_worker_suffix='', random_crop=0, random_mirror=0, random_resized_crop=0, rgb_mean='123.68,116.779,103.939', rgb_std='1,1,1', saturation=0, save_period=1, test_io=0, top_k=0, warmup_epochs=5, warmup_strategy='linear', wd=0.0001) [11:11:31] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable) INFO:root:Epoch[0] Batch [0-20] Speed: 751.94 samples/sec accuracy=0.002046 INFO:root:Epoch[0] Batch [20-40] Speed: 747.25 samples/sec accuracy=0.466602 INFO:root:Epoch[0] Batch [40-60] Speed: 749.05 samples/sec accuracy=0.982373 INFO:root:Epoch[0] Batch [60-80] Speed: 752.68 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [80-100] Speed: 746.27 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [100-120] Speed: 747.09 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [120-140] Speed: 745.22 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [140-160] Speed: 751.97 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [160-180] Speed: 741.86 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [180-200] Speed: 746.97 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [200-220] Speed: 750.02 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [220-240] Speed: 747.36 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [240-260] Speed: 745.85 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [260-280] Speed: 749.36 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [280-300] Speed: 751.17 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [300-320] Speed: 747.06 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [320-340] Speed: 752.16 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [340-360] Speed: 752.29 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [360-380] Speed: 751.47 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [380-400] Speed: 751.15 samples/sec accuracy=1.000000 ``` following result is from 1.3.1 ``` INFO:root:start with arguments Namespace(batch_size=1024, benchmark=1, brightness=0, contrast=0, data_nthreads=4, data_train=None, data_train_idx='', data_val=None, data_val_idx='', disp_batches=20, dtype='float32', fill_value=127, gc_threshold=0.5, gc_type='none', gpus='0,1,2,3,4,5,6,7', image_shape='3,224,224', initializer='default', kv_store='device', load_epoch=None, loss='', lr=0.1, lr_factor=0.1, lr_step_epochs='30,60', macrobatch_size=0, max_crop_size=-1, max_random_area=1, max_random_aspect_ratio=0, max_random_h=0, max_random_l=0, max_random_rotate_angle=0, max_random_s=0, max_random_scale=1, max_random_shear_ratio=0, min_crop_size=-1, min_random_area=1, min_random_aspect_ratio=None, min_random_scale=1, model_prefix=None, mom=0.9, monitor=0, network='resnet', num_classes=80000, num_epochs=80, num_examples=1281167, num_layers=50, optimizer='sgd', pad_size=0, pca_noise=0, profile_server_suffix='', profile_worker_suffix='', random_crop=0, random_mirror=0, random_resized_crop=0, rgb_mean='123.68,116.779,103.939', rgb_std='1,1,1', saturation=0, save_period=1, test_io=0, top_k=0, warmup_epochs=5, warmup_strategy='linear', wd=0.0001) [11:23:20] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable) INFO:root:Epoch[0] Batch [20] Speed: 1018.92 samples/sec accuracy=0.001442 INFO:root:Epoch[0] Batch [40] Speed: 1019.44 samples/sec accuracy=0.464893 INFO:root:Epoch[0] Batch [60] Speed: 1020.81 samples/sec accuracy=0.997559 INFO:root:Epoch[0] Batch [80] Speed: 1021.99 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [100] Speed: 1021.33 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [120] Speed: 1020.30 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [140] Speed: 1023.01 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [160] Speed: 1025.59 samples/sec accuracy=1.000000 INFO:root:Epoch[0] Batch [180] Speed: 1023.41 samples/sec accuracy=1.000000 ```
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