ubuntu 18.04 P106-90 nvidia-docker tf 1.13.1 /models/tutorials/image/cifar10/ 跑个分

本文链接：https://blog.youkuaiyun.com/qadzhangc/article/details/100844603

name: P106-090 major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:06:00.0
totalMemory: 5.94GiB freeMemory: 5.89GiB
2019-09-14 21:47:52.023115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-09-14 21:47:52.023806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-09-14 21:47:52.023829: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-09-14 21:47:52.023838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-09-14 21:47:52.023916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5730 MB memory) -> physical GPU (device: 0, name: P106-090, pci bus id: 0000:06:00.0, compute capability: 6.1)
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py:809: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
2019-09-14 21:47:53.739612: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
2019-09-14 21:47:56.331259: step 0, loss = 4.68 (281.8 examples/sec; 0.454 sec/batch)
2019-09-14 21:47:56.662404: step 10, loss = 4.61 (3865.2 examples/sec; 0.033 sec/batch)
2019-09-14 21:47:56.884238: step 20, loss = 4.44 (5770.2 examples/sec; 0.022 sec/batch)
2019-09-14 21:47:57.113884: step 30, loss = 4.38 (5573.8 examples/sec; 0.023 sec/batch)
2019-09-14 21:47:57.349964: step 40, loss = 4.32 (5421.9 examples/sec; 0.024 sec/batch)
2019-09-14 21:47:57.575752: step 50, loss = 4.48 (5669.0 examples/sec; 0.023 sec/batch)
2019-09-14 21:47:57.799153: step 60, loss = 4.39 (5729.7 examples/sec; 0.022 sec/batch)
2019-09-14 21:47:58.026325: step 70, loss = 4.29 (5634.4 examples/sec; 0.023 sec/batch)
2019-09-14 21:47:58.256787: step 80, loss = 4.12 (5554.2 examples/sec; 0.023 sec/batch)
2019-09-14 21:47:58.481502: step 90, loss = 4.03 (5696.0 examples/sec; 0.022 sec/batch)
2019-09-14 21:47:58.852741: step 100, loss = 4.07 (3447.9 examples/sec; 0.037 sec/batch)
2019-09-14 21:47:59.084432: step 110, loss = 4.06 (5524.6 examples/sec; 0.023 sec/batch)
2019-09-14 21:47:59.318055: step 120, loss = 4.08 (5478.9 examples/sec; 0.023 sec/batch)
2019-09-14 21:47:59.547535: step 130, loss = 3.93 (5577.8 examples/sec; 0.023 sec/batch)
2019-09-14 21:47:59.794773: step 140, loss = 3.95 (5177.2 examples/sec; 0.025 sec/batch)
2019-09-14 21:48:00.030716: step 150, loss = 4.02 (5425.0 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:00.267078: step 160, loss = 3.89 (5415.5 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:00.493762: step 170, loss = 3.93 (5646.5 examples/sec; 0.023 sec/batch)
2019-09-14 21:48:00.730811: step 180, loss = 3.96 (5399.8 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:00.962910: step 190, loss = 3.85 (5514.9 examples/sec; 0.023 sec/batch)
2019-09-14 21:48:01.338612: step 200, loss = 4.13 (3406.9 examples/sec; 0.038 sec/batch)
2019-09-14 21:48:01.575155: step 210, loss = 3.72 (5411.3 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:01.801716: step 220, loss = 4.02 (5649.8 examples/sec; 0.023 sec/batch)
2019-09-14 21:48:02.028819: step 230, loss = 3.64 (5636.2 examples/sec; 0.023 sec/batch)
2019-09-14 21:48:02.266736: step 240, loss = 3.71 (5380.1 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:02.505762: step 250, loss = 3.70 (5355.1 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:02.733827: step 260, loss = 3.54 (5612.2 examples/sec; 0.023 sec/batch)
2019-09-14 21:48:02.966545: step 270, loss = 3.68 (5500.3 examples/sec; 0.023 sec/batch)
2019-09-14 21:48:03.204564: step 280, loss = 3.77 (5377.7 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:03.429354: step 290, loss = 3.84 (5694.1 examples/sec; 0.022 sec/batch)
2019-09-14 21:48:03.801209: step 300, loss = 3.67 (3442.2 examples/sec; 0.037 sec/batch)
2019-09-14 21:48:04.040016: step 310, loss = 3.39 (5360.0 examples/sec; 0.024 sec/batch)
2019-09-14 21:48:04.279548: step 320, loss = 3.82 (5343.7 examples/sec; 0.024 sec/batch)

这块散热就一个散热片一个USB小风扇吹着风。。。跑这个最高70W左右