znxlwm / pytorch-apex-experiment

Simple experiment of Apex (A PyTorch Extension)

pytorch-apex-experiment

Simple experiment of Apex: PyTorch Extension with Tools to Realize the Power of Tensor Cores

Usage

1.Install Apex package

Apex: A PyTorch Extension

2.Train

python CIFAR.py --GPU gpu_name --mode 'FP16' --batch_size 128 --iteration 100

3.plot (optional)

python make_plot.py --GPU 'gpu_name1' 'gpu_name2' 'gpu_name3' --method 'FP32' 'FP16' 'amp' --batch 128 256 512 1024 2048

Folder structure

The following shows basic folder structure.

├── cifar
├── CIFAR.py  # training code
├── utils.py
├── make_plot.py
└── results
    └── gpu_name  # results to be saved here

Experiment settings

• Network: vgg16
• Dataset: CIFAR10
• Method: FP32 (float32), FP16 (float16; half tensor), AMP (Automatic Mixed Precision)
• GPU: GTX 1080 Ti, GTX TITAN X, Tesla V100
• Batch size: 128, 256, 512, 1024, 2048
• All random seeds are fixed
• Result: The mean and std of 5 times (each 100 iterations)
• Ubuntu 16.04
• Python 3
• Cuda 9.0
• PyTorch 0.4.1
• torchvision 0.2.1

Resutls

GPU - Method	Metric	Batch size
		128	256	512	1024	2048
1080 Ti - FP32	Accuracy (%)	40.92 ± 2.08	50.74 ± 3.64	61.32 ± 2.43	64.79 ± 1.56	63.44 ± 1.76
	Time (sec)	5.16 ± 0.73	9.12 ± 1.20	16.75 ± 2.05	32.23 ± 3.23	63.42 ± 4.89
	Memory (Mb)	1557.00 ± 0.00	2053.00 ± 0.00	2999.00 ± 0.00	4995.00 ± 0.00	8763.00 ± 0.00
1080 Ti - FP16	Accuracy (%)	43.35 ± 2.04	51.00 ± 3.75	57.70 ± 1.58	63.79 ± 3.95	62.64 ± 1.91
	Time (sec)	5.42 ± 0.71	9.11 ± 1.14	16.54 ± 1.78	31.49 ± 3.01	61.79 ± 5.15
	Memory (Mb)	1405.00 ± 0.00	1745.00 ± 0.00	2661.00 ± 0.00	4013.00 ± 0.00	6931.00 ± 0.00
1080 Ti - AMP	Accuracy (%)	41.11 ± 1.19	47.59 ± 1.79	60.37 ± 2.48	63.31 ± 1.92	63.41 ± 3.75
	Time (sec)	6.32 ± 0.70	10.70 ± 1.11	18.95 ± 1.80	36.15 ± 3.01	72.64 ± 5.11
	Memory (Mb)	1941.00 ± 317.97	1907.00 ± 179.63	2371.00 ± 0.00	4073.00 ± 0.00	7087.00 ± 0.00
TITAN X - FP32	Accuracy (%)	42.90 ± 2.42	45.78 ± 1.22	60.88 ± 1.78	64.22 ± 2.62	63.79 ± 1.62
	Time (sec)	5.86 ± 0.80	9.59 ± 1.29	18.19 ± 1.84	35.62 ± 4.07	66.56 ± 4.62
	Memory (Mb)	1445.00 ± 0.00	1879.00 ± 0.00	2683.00 ± 0.00	4439.00 ± 0.00	7695.00 ± 0.00
TITAN X - FP16	Accuracy (%)	39.13 ± 3.56	49.87 ± 2.42	59.77 ± 1.77	65.57 ± 2.82	64.08 ± 1.80
	Time (sec)	5.66 ± 0.97	9.72 ± 1.23	17.14 ± 1.82	33.23 ± 3.50	65.86 ± 4.94
	Memory (Mb)	1361.00 ± 0.00	1807.00 ± 0.00	2233.00 ± 0.00	3171.00 ± 0.00	5535.00 ± 0.00
TITAN X - AMP	Accuracy (%)	42.57 ± 1.25	49.59 ± 2.14	59.76 ± 1.60	63.76 ± 4.24	65.14 ± 2.93
	Time (sec)	7.55 ± 1.03	11.82 ± 1.07	20.96 ± 1.83	38.82 ± 3.17	76.54 ± 6.60
	Memory (Mb)	1729.00 ± 219.51	1999.00 ± 146.97	2327.00 ± 0.00	3453.00 ± 0.00	5917.00 ± 0.00
V100 - FP32	Accuracy (%)	42.56 ± 1.37	49.50 ± 1.81	60.91 ± 0.88	65.26 ± 1.76	63.93 ± 3.69
	Time (sec)	3.93 ± 0.54	6.90 ± 0.82	12.97 ± 1.27	25.11 ± 1.83	49.43 ± 3.46
	Memory (Mb)	1834.00 ± 0.00	2214.00 ± 0.00	2983.60 ± 116.80	4674.00 ± 304.00	8534.80 ± 826.40
V100 - FP16	Accuracy (%)	43.37 ± 2.13	51.78 ± 2.48	58.46 ± 1.81	64.72 ± 2.37	63.21 ± 1.60
	Time (sec)	3.28 ± 0.52	5.95 ± 1.03	10.50 ± 1.27	19.65 ± 1.95	37.32 ± 3.73
	Memory (Mb)	1777.20 ± 25.60	2040.00 ± 0.00	2464.00 ± 0.00	3394.00 ± 0.00	4748.00 ± 0.00
V100 - AMP	Accracy (%)	42.39 ± 2.35	51.33 ± 1.84	61.41 ± 2.10	65.05 ± 3.29	61.67 ± 3.13
	Time (sec)	4.27 ± 0.54	7.18 ± 0.90	13.31 ± 1.26	23.99 ± 2.29	45.68 ± 3.77
	Memory (Mb)	2174.80 ± 211.74	2274.00 ± 172.15	2775.20 ± 77.60	3790.80 ± 154.40	5424.00 ± 0.00

Visualization

Time	Memory
Time with std	Memory with std