背景
我是TensorFlow的新Python开发人员。
系统规格:
-
-
GeForce 940MX 4GB
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Ubuntu 18
我在Docker上运行TensorFlow(发现安装cuda的东西太复杂、太长,可能是我搞糟了)
基本上,我在GPU和CPU上运行一种HelloWorld代码,并检查它会有什么不同,让我惊讶的是,几乎没有什么不同!
version: '2.3'
services:
tensorflow:
# image: tensorflow/tensorflow:latest-gpu-py3
image: tensorflow/tensorflow:latest-py3
runtime: nvidia
volumes:
- ./:/notebooks/TensorTest1
ports:
- 8888:8888
image: tensorflow/tensorflow:latest-py3
我有大约5秒钟的时间。
root@e7dc71acfa59:/notebooks/TensorTest1# python3 hello1.py
2018-11-18 14:37:24.288321: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
TIME: 4.900559186935425
result: [3. 3. 3. ... 3. 3. 3.]
当我和你一起跑的时候
image: tensorflow/tensorflow:latest-gpu-py3
root@baf68fc71921:/notebooks/TensorTest1# python3 hello1.py
2018-11-18 14:39:39.811575: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-11-18 14:39:39.877483: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-11-18 14:39:39.878122: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce 940MX major: 5 minor: 0 memoryClockRate(GHz): 1.189
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 3.56GiB
2018-11-18 14:39:39.878148: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2018-11-18 14:44:17.101263: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-11-18 14:44:17.101303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2018-11-18 14:44:17.101313: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2018-11-18 14:44:17.101540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3259 MB memory) -> physical GPU (device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0, compute capability: 5.0)
TIME: 5.82940673828125
result: [3. 3. 3. ... 3. 3. 3.]
我的代码
import tensorflow as tf
import time
with tf.Session():
start_time = time.time()
input1 = tf.constant([1.0, 1.0, 1.0, 1.0] * 100 * 100 * 100)
input2 = tf.constant([2.0, 2.0, 2.0, 2.0] * 100 * 100 * 100)
output = tf.add(input1, input2)
result = output.eval()
duration = time.time() - start_time
print("TIME:", duration)
print("result: ", result)
我做错什么了吗?基于打印,它似乎正确地使用了GPU
Can I measure the execution time of individual operations with TensorFlow?
我得到了这个
