Я пытался протестировать производительность нескольких графических процессоров версии cifar10_estimator на 2 или 3 1080Ti, но не получил ускорения.
Я нашел некоторую полезную информацию об оборудовании здесь , но все еще не понимает, как ее решить.
Моя среда:
- ВЕРСИЯ Ubuntu = 16.04.5 LTS (Xenial Xerus)
- Python3
- CUDA_VERSION = 9.0.176
- tenenflow-gpu = 1.11.0
Информация о графическом процессоре:
nvidia-smi topo -m
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity
GPU0 X PIX PHB PHB SYS SYS SYS SYS 0-7
GPU1 PIX X PHB PHB SYS SYS SYS SYS 0-7
GPU2 PHB PHB X PIX SYS SYS SYS SYS 0-7
GPU3 PHB PHB PIX X SYS SYS SYS SYS 0-7
GPU4 SYS SYS SYS SYS X PIX PHB PHB 8-15
GPU5 SYS SYS SYS SYS PIX X PHB PHB 8-15
GPU6 SYS SYS SYS SYS PHB PHB X PIX 8-15
GPU7 SYS SYS SYS SYS PHB PHB PIX X 8-15
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe switches (without traversing the PCIe Host Bridge)
PIX = Connection traversing a single PCIe switch
NV# = Connection traversing a bonded set of # NVLinks
1 GPU bach_size = 128
INFO:tensorflow:loss = 2.2576141, step = 200 (3.729 sec)
INFO:tensorflow:learning_rate = 0.1, loss = 2.2576141 (3.729 sec)
INFO:tensorflow:Average examples/sec: 2821.06 (2858.65), step = 200
INFO:tensorflow:Average examples/sec: 2847.23 (3496.06), step = 210
INFO:tensorflow:Average examples/sec: 2857.91 (3102.29), step = 220
INFO:tensorflow:Average examples/sec: 2867.04 (3083.62), step = 230
INFO:tensorflow:Average examples/sec: 2889.21 (3514.15), step = 240
INFO:tensorflow:Average examples/sec: 2913.15 (3636.28), step = 250
INFO:tensorflow:Average examples/sec: 2915.99 (2988.94), step = 260
INFO:tensorflow:Average examples/sec: 2901.94 (2578.95), step = 270
INFO:tensorflow:Average examples/sec: 2888.87 (2575.46), step = 280
INFO:tensorflow:Average examples/sec: 2892.13 (2986.66), step = 290
INFO:tensorflow:global_step/sec: 24.25
2 gpu bach_size = 256
INFO:tensorflow:loss = 2.4630964, step = 200 (5.971 sec)
INFO:tensorflow:learning_rate = 0.1, loss = 2.4630964 (5.971 sec)
INFO:tensorflow:Average examples/sec: 3255.68 (4296.71), step = 200
INFO:tensorflow:Average examples/sec: 3297.51 (4437.93), step = 210
INFO:tensorflow:Average examples/sec: 3332.15 (4275.33), step = 220
INFO:tensorflow:Average examples/sec: 3363.86 (4254.65), step = 230
INFO:tensorflow:Average examples/sec: 3395.09 (4316.94), step = 240
INFO:tensorflow:Average examples/sec: 3418.44 (4094.23), step = 250
INFO:tensorflow:Average examples/sec: 3447.17 (4364.24), step = 260
INFO:tensorflow:Average examples/sec: 3474.56 (4379.02), step = 270
INFO:tensorflow:Average examples/sec: 3492.73 (4067.13), step = 280
INFO:tensorflow:Average examples/sec: 3514.19 (4244.23), step = 290
INFO:tensorflow:global_step/sec: 16.6026
3 gpu bach_size = 384
INFO:tensorflow:loss = 2.0980535, step = 200 (9.329 sec)
INFO:tensorflow:learning_rate = 0.1, loss = 2.0980535 (9.329 sec)
INFO:tensorflow:Average examples/sec: 3214.65 (4165.7), step = 200
INFO:tensorflow:Average examples/sec: 3272.85 (5130.99), step = 210
INFO:tensorflow:Average examples/sec: 3324.15 (4955.13), step = 220
INFO:tensorflow:Average examples/sec: 3376.65 (5174.76), step = 230
INFO:tensorflow:Average examples/sec: 3425.48 (5132.15), step = 240
INFO:tensorflow:Average examples/sec: 3468.29 (4954.35), step = 250
INFO:tensorflow:Average examples/sec: 3509.91 (5014.23), step = 260
INFO:tensorflow:Average examples/sec: 3544.29 (4755.56), step = 270
INFO:tensorflow:Average examples/sec: 3579.69 (4901.39), step = 280
INFO:tensorflow:Average examples/sec: 3617.84 (5156.66), step = 290
INFO:tensorflow:global_step/sec: 13.1009
