Hello, would you please help explain this issue? Thanks in advance.
We found that convnet-benchmarks performance on cupy-2.0.0 is worse than that on cupy-1.0.0.1. We don't know whether it is problem of cupy or convnet-benchmarks scripts. We reported this issue in cupy/cupy#753, got no response yet.
---------------------details--------------------------
Test Environment:
P100
Test action:
1, install chainer
2, get convnet-benchmarks code:
git clone https://github.com/mitmul/convnet-benchmarks
3, test cases
3.1: case "pip install cupy==1.0.0.1"
(py2-chainer-gpu) [sys_dltest@mlt-gpu200 chainer]$ python train_imagenet.py
alexnet
('Chainer version:', '2.0.0b1')
('CuPy version:', '1.0.0.1')
('CUDA:', True)
('CUDA Version:', u'V8.0.61')
('cuDNN:', True)
('cuDNN Version:', 5110)
('Input data shape:', (128, 3, 224, 224))
('Average Forward: ', 16.15312328338623, ' ms')
('Average Backward: ', 35.27830085754395, ' ms')
('Average Total: ', 51.431424140930176, ' ms')
3.2: case "pip install cupy==2.0.0"
(py2-chainer-gpu) [sys_dltest@mlt-gpu200 chainer]$ python train_imagenet.py
alexnet
('Chainer version:', '2.0.0b1')
('CuPy version:', '2.0.0')
('CUDA:', True)
('cuDNN:', True)
('cuDNN Version:', 5110)
('Input data shape:', (128, 3, 224, 224))
('Average Forward: ', 35.381299591064455, ' ms')
('Average Backward: ', 63.26389694213867, ' ms')
('Average Total: ', 98.64519653320312, ' ms')
3.3: case "pip install cupy==2.0.0rc1"
(py2-chainer-gpu) [sys_dltest@mlt-gpu200 chainer]$ python train_imagenet.py
alexnet
('Chainer version:', '2.0.0b1')
('CuPy version:', '2.0.0rc1')
('CUDA:', True)
('cuDNN:', True)
('cuDNN Version:', 5110)
('Input data shape:', (128, 3, 224, 224))
('Average Forward: ', 35.5438117980957, ' ms')
('Average Backward: ', 63.336796569824216, ' ms')
('Average Total: ', 98.88060836791992, ' ms')
Notice: when run "case cupy==2.0.0*", you need to comment following lines in train_imagenet.py.
#if chainer.cuda.available:
cuda_v = cupy.cuda.compiler._get_nvcc_version().split()[-1].decode('utf-8')
print('CUDA Version:', cuda_v)
Hello, would you please help explain this issue? Thanks in advance.
We found that convnet-benchmarks performance on cupy-2.0.0 is worse than that on cupy-1.0.0.1. We don't know whether it is problem of cupy or convnet-benchmarks scripts. We reported this issue in cupy/cupy#753, got no response yet.
---------------------details--------------------------
Test Environment:
P100
Test action:
1, install chainer
2, get convnet-benchmarks code:
git clone https://github.com/mitmul/convnet-benchmarks
3, test cases
3.1: case "pip install cupy==1.0.0.1"
(py2-chainer-gpu) [sys_dltest@mlt-gpu200 chainer]$ python train_imagenet.py
alexnet
('Chainer version:', '2.0.0b1')
('CuPy version:', '1.0.0.1')
('CUDA:', True)
('CUDA Version:', u'V8.0.61')
('cuDNN:', True)
('cuDNN Version:', 5110)
('Input data shape:', (128, 3, 224, 224))
('Average Forward: ', 16.15312328338623, ' ms')
('Average Backward: ', 35.27830085754395, ' ms')
('Average Total: ', 51.431424140930176, ' ms')
3.2: case "pip install cupy==2.0.0"
(py2-chainer-gpu) [sys_dltest@mlt-gpu200 chainer]$ python train_imagenet.py
alexnet
('Chainer version:', '2.0.0b1')
('CuPy version:', '2.0.0')
('CUDA:', True)
('cuDNN:', True)
('cuDNN Version:', 5110)
('Input data shape:', (128, 3, 224, 224))
('Average Forward: ', 35.381299591064455, ' ms')
('Average Backward: ', 63.26389694213867, ' ms')
('Average Total: ', 98.64519653320312, ' ms')
3.3: case "pip install cupy==2.0.0rc1"
(py2-chainer-gpu) [sys_dltest@mlt-gpu200 chainer]$ python train_imagenet.py
alexnet
('Chainer version:', '2.0.0b1')
('CuPy version:', '2.0.0rc1')
('CUDA:', True)
('cuDNN:', True)
('cuDNN Version:', 5110)
('Input data shape:', (128, 3, 224, 224))
('Average Forward: ', 35.5438117980957, ' ms')
('Average Backward: ', 63.336796569824216, ' ms')
('Average Total: ', 98.88060836791992, ' ms')
Notice: when run "case cupy==2.0.0*", you need to comment following lines in train_imagenet.py.
#if chainer.cuda.available:
cuda_v = cupy.cuda.compiler._get_nvcc_version().split()[-1].decode('utf-8')
print('CUDA Version:', cuda_v)