日本电子维修技术 显卡关于 1080ti 跑 tensorflow

最近开始学深度学习了，都知道有cuda跑得快，不过不知道现在的机器是什么水平，来请教下大神看看有无必要换显卡。
因为刚开始学，代码就是最简单的官网指南的第一个例子 https://www.tensorflow.org/tutorials/

import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)复制代码

我这 1950x win10系统，每次训练16秒多点，默频16核32线程，占用率全核50%（不知道为啥没占用满）

2080出了1080也跌了，如果换 1080ti 的话会有质变吗




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你是要学还是要投入生产？
学的话，16秒和8秒有区别吗？

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此帖终结？

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CPU和GPU的速度差距巨大，能买GPU就买

学就是不停错的过程，别浪费时间在等待上面

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既然是CUDA，2080Ti岂不是更多

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非常不专业的相关人员一枚表示：
1.大部分类型的神经网络中相当多占用时间的操作都是可以由GPU执行的，相对CPU有十倍以上的速度收益。
2.刚开始学的时候无所谓，但是当你决定训练一个网络的时候，GPU会大幅度加快训练速度。很多情况下训练相当耗时，上个月用自己的数据集训练的FasterRCNN+Resnet50单张1080TI跑了2个星期还没有完全收敛。.
3.显卡在这方面的应用不但要看本身的性能，显存容量也很重要，复杂的网络参数量巨大，可能会导致现存塞不下……

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都这样了还不去请外援嘛∑(ﾟДﾟ)
还是说天朝没有外援可以请…？

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见6楼

作为一开始最简单的例子当然耗时少了……

最近来的实习生对一个2000组的时间序列数据集，建一次模就跑15-20分钟，机器是13还是15寸的外星人，好像没有开cuda

学完了我是要用的，不想把时间花在等待上

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所以单 1080ti 跑一个主楼的代码，每个循环要多少时间呢？
可否麻烦兄台帮忙跑一下？ 可以肯定速度比 16秒快，您要觉得 5 个循环太多浪费时间，跑2个也行

如果跑下来每个循环在4秒以下我就考虑搞一张 1080ti，如果是8秒10秒的，那就算了
不管您有没有时间跑，先谢过


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2080ti的 cuda 单元没比 1080ti 多多少，价格嘛……

cpu换gpu，我花4k买个二手，效率有可能提高10倍，上2080ti价格翻倍，效率提升能变成20不……估计也就12、13嘛

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对于开发的话一块960我觉得和980ti，1080ti都没啥区别吧。。。
除非是新的CUDA/tensor API需要新卡支持。
而且开发的话，弄些小case跑通程序就可以了吧 ？
其实真正的程序员到家还搞程序的太少了。。已经累了一天了，到家就玩吧。



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情况有点特殊

不过兄台言之有理，小公司申请的工作站已经批了，赶上RMB贬值，我可以把原来的P5000升级到 titan v了，后面就在公司搞

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训练一批样本数据需要2个礼拜？你觉得这种效率能投入商业应用？

不想办法从算法上提升效率，就知道堆硬件？

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训练一批样本数据需要2个礼拜？你觉得这种效率能投入商业应用？

不想办法从算法上提升效率，就知道堆硬件？

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幸好能不能投入不是你说了算。我堆硬件我乐意，吃你家大米了？您用算盘去算吧，拉黑不送

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必然有质变，但你玩这种16秒变成16毫秒的游戏就有点浪费钱了，找个CPU需要训练一个礼拜的例子来换显卡上才有意义

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起步阶段管中窥豹看看。后面耗时的项目马上就来啦。谢谢提醒啦

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你这个模型太小了，我给你找了个常见的CNN模型的benchmark

https://www.hpc-technologies.co.jp/tensorflow-benchmark-1

别听上面瞎扯淡
这玩意每个batch就算提升0.1毫秒最后都是节省下难以想象的时间，可以迭代更多的模型就是胜利

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/home/ryu/anaconda3/envs/tensorflow/bin/python /home/ryu/PycharmProjects/remote_test/intro.py
/home/ryu/anaconda3/envs/tensorflow/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/home/ryu/anaconda3/envs/tensorflow/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
Epoch 1/5
2018-08-22 20:13:30.186830: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-08-22 20:13:30.542511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: TITAN V major: 7 minor: 0 memoryClockRate(GHz): 1.455
pciBusID: 0000:03:00.0
totalMemory: 11.76GiB freeMemory: 10.65GiB
2018-08-22 20:13:30.542538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-08-22 20:13:30.812873: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-08-22 20:13:30.812907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-08-22 20:13:30.812914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-08-22 20:13:30.813163: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10287 MB memory) -> physical GPU (device: 0, name: TITAN V, pci bus id: 0000:03:00.0, compute capability: 7.0)
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57440/60000 [===========================>..] - ETA: 0s - loss: 0.2233 - acc: 0.9329
57760/60000 [===========================>..] - ETA: 0s - loss: 0.2226 - acc: 0.9332
58112/60000 [============================>.] - ETA: 0s - loss: 0.2220 - acc: 0.9334
58432/60000 [============================>.] - ETA: 0s - loss: 0.2214 - acc: 0.9335
58752/60000 [============================>.] - ETA: 0s - loss: 0.2209 - acc: 0.9337
59104/60000 [============================>.] - ETA: 0s - loss: 0.2203 - acc: 0.9339
59456/60000 [============================>.] - ETA: 0s - loss: 0.2196 - acc: 0.9341
59808/60000 [============================>.] - ETA: 0s - loss: 0.2187 - acc: 0.9344
60000/60000 [==============================] - 10s 173us/step - loss: 0.2185 - acc: 0.9345
Epoch 2/5
   32/60000 [..............................] - ETA: 17s - loss: 0.0435 - acc: 1.0000
  352/60000 [..............................] - ETA: 10s - loss: 0.1117 - acc: 0.9716
  672/60000 [..............................] - ETA: 9s - loss: 0.0872 - acc: 0.9777
1024/60000 [..............................] - ETA: 9s - loss: 0.0932 - acc: 0.9746
1376/60000 [..............................] - ETA: 9s - loss: 0.0997 - acc: 0.9702
1696/60000 [..............................] - ETA: 9s - loss: 0.1034 - acc: 0.9682
2016/60000 [>.............................] - ETA: 9s - loss: 0.0992 - acc: 0.9673
2336/60000 [>.............................] - ETA: 9s - loss: 0.1031 - acc: 0.9683
2656/60000 [>.............................] - ETA: 9s - loss: 0.1087 - acc: 0.9676
3008/60000 [>.............................] - ETA: 9s - loss: 0.1041 - acc: 0.9697
3328/60000 [>.............................] - ETA: 9s - loss: 0.1042 - acc: 0.9694
3680/60000 [>.............................] - ETA: 8s - loss: 0.1054 - acc: 0.9679
4032/60000 [=>............................] - ETA: 8s - loss: 0.1046 - acc: 0.9678
4352/60000 [=>............................] - ETA: 8s - loss: 0.1022 - acc: 0.9692
4704/60000 [=>............................] - ETA: 8s - loss: 0.1030 - acc: 0.9692
5056/60000 [=>............................] - ETA: 8s - loss: 0.1027 - acc: 0.9691
5408/60000 [=>............................] - ETA: 8s - loss: 0.1017 - acc: 0.9695
5760/60000 [=>............................] - ETA: 8s - loss: 0.1017 - acc: 0.9694
6112/60000 [==>...........................] - ETA: 8s - loss: 0.1000 - acc: 0.9694
6464/60000 [==>...........................] - ETA: 8s - loss: 0.0979 - acc: 0.9697
6816/60000 [==>...........................] - ETA: 8s - loss: 0.0964 - acc: 0.9695
7136/60000 [==>...........................] - ETA: 8s - loss: 0.0962 - acc: 0.9693
7456/60000 [==>...........................] - ETA: 8s - loss: 0.0947 - acc: 0.9700
7808/60000 [==>...........................] - ETA: 8s - loss: 0.0956 - acc: 0.9695
8128/60000 [===>..........................] - ETA: 8s - loss: 0.0958 - acc: 0.9697
8480/60000 [===>..........................] - ETA: 8s - loss: 0.0953 - acc: 0.9699
8800/60000 [===>..........................] - ETA: 8s - loss: 0.0974 - acc: 0.9694
9120/60000 [===>..........................] - ETA: 7s - loss: 0.0986 - acc: 0.9691
9472/60000 [===>..........................] - ETA: 7s - loss: 0.0987 - acc: 0.9692
9824/60000 [===>..........................] - ETA: 7s - loss: 0.0984 - acc: 0.9691
10144/60000 [====>.........................] - ETA: 7s - loss: 0.0991 - acc: 0.9690
10464/60000 [====>.........................] - ETA: 7s - loss: 0.1005 - acc: 0.9686
10816/60000 [====>.........................] - ETA: 7s - loss: 0.1004 - acc: 0.9683
11168/60000 [====>.........................] - ETA: 7s - loss: 0.0996 - acc: 0.9686
11520/60000 [====>.........................] - ETA: 7s - loss: 0.1005 - acc: 0.9683
11872/60000 [====>.........................] - ETA: 7s - loss: 0.1002 - acc: 0.9686
12192/60000 [=====>........................] - ETA: 7s - loss: 0.1016 - acc: 0.9683
12544/60000 [=====>........................] - ETA: 7s - loss: 0.1013 - acc: 0.9683
12896/60000 [=====>........................] - ETA: 7s - loss: 0.1011 - acc: 0.9683
13216/60000 [=====>........................] - ETA: 7s - loss: 0.1022 - acc: 0.9681
13568/60000 [=====>........................] - ETA: 7s - loss: 0.1025 - acc: 0.9681
13888/60000 [=====>........................] - ETA: 7s - loss: 0.1027 - acc: 0.9683
14208/60000 [======>.......................] - ETA: 7s - loss: 0.1027 - acc: 0.9683
14528/60000 [======>.......................] - ETA: 7s - loss: 0.1031 - acc: 0.9681
14880/60000 [======>.......................] - ETA: 7s - loss: 0.1044 - acc: 0.9675
15200/60000 [======>.......................] - ETA: 7s - loss: 0.1035 - acc: 0.9679
15520/60000 [======>.......................] - ETA: 6s - loss: 0.1030 - acc: 0.9679
15840/60000 [======>.......................] - ETA: 6s - loss: 0.1026 - acc: 0.9681
16192/60000 [=======>......................] - ETA: 6s - loss: 0.1019 - acc: 0.9683
16544/60000 [=======>......................] - ETA: 6s - loss: 0.1017 - acc: 0.9684
16896/60000 [=======>......................] - ETA: 6s - loss: 0.1015 - acc: 0.9686
17248/60000 [=======>......................] - ETA: 6s - loss: 0.1014 - acc: 0.9689
17568/60000 [=======>......................] - ETA: 6s - loss: 0.1012 - acc: 0.9690
17920/60000 [=======>......................] - ETA: 6s - loss: 0.1012 - acc: 0.9689
18272/60000 [========>.....................] - ETA: 6s - loss: 0.1015 - acc: 0.9689
18624/60000 [========>.....................] - ETA: 6s - loss: 0.1015 - acc: 0.9688
18976/60000 [========>.....................] - ETA: 6s - loss: 0.1017 - acc: 0.9689
19296/60000 [========>.....................] - ETA: 6s - loss: 0.1016 - acc: 0.9690
19616/60000 [========>.....................] - ETA: 6s - loss: 0.1009 - acc: 0.9693
19936/60000 [========>.....................] - ETA: 6s - loss: 0.1011 - acc: 0.9691
20256/60000 [=========>....................] - ETA: 6s - loss: 0.1013 - acc: 0.9690
20608/60000 [=========>....................] - ETA: 6s - loss: 0.1013 - acc: 0.9688
20928/60000 [=========>....................] - ETA: 6s - loss: 0.1013 - acc: 0.9688
21248/60000 [=========>....................] - ETA: 6s - loss: 0.1014 - acc: 0.9688
21600/60000 [=========>....................] - ETA: 6s - loss: 0.1011 - acc: 0.9689
21920/60000 [=========>....................] - ETA: 5s - loss: 0.1009 - acc: 0.9691
22240/60000 [==========>...................] - ETA: 5s - loss: 0.1008 - acc: 0.9690
22560/60000 [==========>...................] - ETA: 5s - loss: 0.1010 - acc: 0.9690
22912/60000 [==========>...................] - ETA: 5s - loss: 0.1009 - acc: 0.9692
23264/60000 [==========>...................] - ETA: 5s - loss: 0.1006 - acc: 0.9693
23616/60000 [==========>...................] - ETA: 5s - loss: 0.1002 - acc: 0.9694
23968/60000 [==========>...................] - ETA: 5s - loss: 0.1008 - acc: 0.9691
24320/60000 [===========>..................] - ETA: 5s - loss: 0.1010 - acc: 0.9690
24672/60000 [===========>..................] - ETA: 5s - loss: 0.1008 - acc: 0.9691
25088/60000 [===========>..................] - ETA: 5s - loss: 0.1005 - acc: 0.9691
25472/60000 [===========>..................] - ETA: 5s - loss: 0.1009 - acc: 0.9689
25824/60000 [===========>..................] - ETA: 5s - loss: 0.1016 - acc: 0.9689
26144/60000 [============>.................] - ETA: 5s - loss: 0.1012 - acc: 0.9689
26464/60000 [============>.................] - ETA: 5s - loss: 0.1018 - acc: 0.9689
26784/60000 [============>.................] - ETA: 5s - loss: 0.1018 - acc: 0.9688
27104/60000 [============>.................] - ETA: 5s - loss: 0.1017 - acc: 0.9689
27424/60000 [============>.................] - ETA: 5s - loss: 0.1014 - acc: 0.9690
27744/60000 [============>.................] - ETA: 5s - loss: 0.1015 - acc: 0.9689
28064/60000 [=============>................] - ETA: 4s - loss: 0.1018 - acc: 0.9690
28384/60000 [=============>................] - ETA: 4s - loss: 0.1020 - acc: 0.9688
28736/60000 [=============>................] - ETA: 4s - loss: 0.1017 - acc: 0.9689
29056/60000 [=============>................] - ETA: 4s - loss: 0.1017 - acc: 0.9689
29376/60000 [=============>................] - ETA: 4s - loss: 0.1018 - acc: 0.9689
29696/60000 [=============>................] - ETA: 4s - loss: 0.1022 - acc: 0.9688
30016/60000 [==============>...............] - ETA: 4s - loss: 0.1020 - acc: 0.9689
30368/60000 [==============>...............] - ETA: 4s - loss: 0.1022 - acc: 0.9689
30688/60000 [==============>...............] - ETA: 4s - loss: 0.1023 - acc: 0.9688
31040/60000 [==============>...............] - ETA: 4s - loss: 0.1022 - acc: 0.9689
31392/60000 [==============>...............] - ETA: 4s - loss: 0.1019 - acc: 0.9690
31712/60000 [==============>...............] - ETA: 4s - loss: 0.1017 - acc: 0.9691
32064/60000 [===============>..............] - ETA: 4s - loss: 0.1019 - acc: 0.9691
32416/60000 [===============>..............] - ETA: 4s - loss: 0.1015 - acc: 0.9692
32736/60000 [===============>..............] - ETA: 4s - loss: 0.1017 - acc: 0.9692
33056/60000 [===============>..............] - ETA: 4s - loss: 0.1017 - acc: 0.9692
33376/60000 [===============>..............] - ETA: 4s - loss: 0.1016 - acc: 0.9693
33728/60000 [===============>..............] - ETA: 4s - loss: 0.1015 - acc: 0.9692
34080/60000 [================>.............] - ETA: 4s - loss: 0.1010 - acc: 0.9694
34432/60000 [================>.............] - ETA: 3s - loss: 0.1009 - acc: 0.9694
34752/60000 [================>.............] - ETA: 3s - loss: 0.1007 - acc: 0.9696
35104/60000 [================>.............] - ETA: 3s - loss: 0.1007 - acc: 0.9696
35424/60000 [================>.............] - ETA: 3s - loss: 0.1007 - acc: 0.9696
35776/60000 [================>.............] - ETA: 3s - loss: 0.1004 - acc: 0.9696
36128/60000 [=================>............] - ETA: 3s - loss: 0.1008 - acc: 0.9697
36480/60000 [=================>............] - ETA: 3s - loss: 0.1006 - acc: 0.9698
36800/60000 [=================>............] - ETA: 3s - loss: 0.1004 - acc: 0.9698
37152/60000 [=================>............] - ETA: 3s - loss: 0.1005 - acc: 0.9697
37472/60000 [=================>............] - ETA: 3s - loss: 0.1006 - acc: 0.9697
37792/60000 [=================>............] - ETA: 3s - loss: 0.1001 - acc: 0.9699
38112/60000 [==================>...........] - ETA: 3s - loss: 0.0998 - acc: 0.9700
38432/60000 [==================>...........] - ETA: 3s - loss: 0.0999 - acc: 0.9700
38752/60000 [==================>...........] - ETA: 3s - loss: 0.0995 - acc: 0.9701
39072/60000 [==================>...........] - ETA: 3s - loss: 0.0995 - acc: 0.9700
39392/60000 [==================>...........] - ETA: 3s - loss: 0.0992 - acc: 0.9701
39712/60000 [==================>...........] - ETA: 3s - loss: 0.0990 - acc: 0.9701
40032/60000 [===================>..........] - ETA: 3s - loss: 0.0988 - acc: 0.9702
40352/60000 [===================>..........] - ETA: 3s - loss: 0.0988 - acc: 0.9702
40672/60000 [===================>..........] - ETA: 3s - loss: 0.0989 - acc: 0.9701
41024/60000 [===================>..........] - ETA: 2s - loss: 0.0987 - acc: 0.9701
41344/60000 [===================>..........] - ETA: 2s - loss: 0.0988 - acc: 0.9700
41664/60000 [===================>..........] - ETA: 2s - loss: 0.0987 - acc: 0.9700
42016/60000 [====================>.........] - ETA: 2s - loss: 0.0986 - acc: 0.9701
42368/60000 [====================>.........] - ETA: 2s - loss: 0.0984 - acc: 0.9701
42688/60000 [====================>.........] - ETA: 2s - loss: 0.0983 - acc: 0.9701
43040/60000 [====================>.........] - ETA: 2s - loss: 0.0982 - acc: 0.9701
43360/60000 [====================>.........] - ETA: 2s - loss: 0.0980 - acc: 0.9702
43680/60000 [====================>.........] - ETA: 2s - loss: 0.0981 - acc: 0.9702
44000/60000 [=====================>........] - ETA: 2s - loss: 0.0984 - acc: 0.9701
44352/60000 [=====================>........] - ETA: 2s - loss: 0.0986 - acc: 0.9701
44704/60000 [=====================>........] - ETA: 2s - loss: 0.0986 - acc: 0.9701
45024/60000 [=====================>........] - ETA: 2s - loss: 0.0984 - acc: 0.9702
45344/60000 [=====================>........] - ETA: 2s - loss: 0.0985 - acc: 0.9701
45664/60000 [=====================>........] - ETA: 2s - loss: 0.0987 - acc: 0.9701
46016/60000 [======================>.......] - ETA: 2s - loss: 0.0989 - acc: 0.9700
46336/60000 [======================>.......] - ETA: 2s - loss: 0.0985 - acc: 0.9701
46656/60000 [======================>.......] - ETA: 2s - loss: 0.0982 - acc: 0.9703
47008/60000 [======================>.......] - ETA: 2s - loss: 0.0979 - acc: 0.9703
47328/60000 [======================>.......] - ETA: 1s - loss: 0.0981 - acc: 0.9704
47648/60000 [======================>.......] - ETA: 1s - loss: 0.0979 - acc: 0.9704
48000/60000 [=======================>......] - ETA: 1s - loss: 0.0977 - acc: 0.9704
48352/60000 [=======================>......] - ETA: 1s - loss: 0.0976 - acc: 0.9705
48672/60000 [=======================>......] - ETA: 1s - loss: 0.0974 - acc: 0.9705
49024/60000 [=======================>......] - ETA: 1s - loss: 0.0971 - acc: 0.9706
49344/60000 [=======================>......] - ETA: 1s - loss: 0.0972 - acc: 0.9706
49664/60000 [=======================>......] - ETA: 1s - loss: 0.0970 - acc: 0.9706
50016/60000 [========================>.....] - ETA: 1s - loss: 0.0970 - acc: 0.9706
50368/60000 [========================>.....] - ETA: 1s - loss: 0.0969 - acc: 0.9707
50720/60000 [========================>.....] - ETA: 1s - loss: 0.0972 - acc: 0.9706
51072/60000 [========================>.....] - ETA: 1s - loss: 0.0973 - acc: 0.9705
51392/60000 [========================>.....] - ETA: 1s - loss: 0.0977 - acc: 0.9704
51744/60000 [========================>.....] - ETA: 1s - loss: 0.0979 - acc: 0.9704
52096/60000 [=========================>....] - ETA: 1s - loss: 0.0978 - acc: 0.9704
52448/60000 [=========================>....] - ETA: 1s - loss: 0.0981 - acc: 0.9703
52768/60000 [=========================>....] - ETA: 1s - loss: 0.0980 - acc: 0.9703
53088/60000 [=========================>....] - ETA: 1s - loss: 0.0978 - acc: 0.9703
53408/60000 [=========================>....] - ETA: 1s - loss: 0.0976 - acc: 0.9704
53728/60000 [=========================>....] - ETA: 0s - loss: 0.0979 - acc: 0.9704
54080/60000 [==========================>...] - ETA: 0s - loss: 0.0979 - acc: 0.9704
54432/60000 [==========================>...] - ETA: 0s - loss: 0.0979 - acc: 0.9704
54752/60000 [==========================>...] - ETA: 0s - loss: 0.0978 - acc: 0.9704
55104/60000 [==========================>...] - ETA: 0s - loss: 0.0976 - acc: 0.9704
55424/60000 [==========================>...] - ETA: 0s - loss: 0.0974 - acc: 0.9704
55744/60000 [==========================>...] - ETA: 0s - loss: 0.0972 - acc: 0.9704
56064/60000 [===========================>..] - ETA: 0s - loss: 0.0970 - acc: 0.9705
56384/60000 [===========================>..] - ETA: 0s - loss: 0.0969 - acc: 0.9705
56704/60000 [===========================>..] - ETA: 0s - loss: 0.0967 - acc: 0.9705
57024/60000 [===========================>..] - ETA: 0s - loss: 0.0967 - acc: 0.9705
57344/60000 [===========================>..] - ETA: 0s - loss: 0.0969 - acc: 0.9705
57696/60000 [===========================>..] - ETA: 0s - loss: 0.0972 - acc: 0.9704
58048/60000 [============================>.] - ETA: 0s - loss: 0.0973 - acc: 0.9704
58400/60000 [============================>.] - ETA: 0s - loss: 0.0976 - acc: 0.9703
58752/60000 [============================>.] - ETA: 0s - loss: 0.0975 - acc: 0.9703
59136/60000 [============================>.] - ETA: 0s - loss: 0.0973 - acc: 0.9704
59488/60000 [============================>.] - ETA: 0s - loss: 0.0973 - acc: 0.9704
59840/60000 [============================>.] - ETA: 0s - loss: 0.0971 - acc: 0.9705
60000/60000 [==============================] - 9s 156us/step - loss: 0.0970 - acc: 0.9705
Epoch 3/5
   32/60000 [..............................] - ETA: 16s - loss: 0.0054 - acc: 1.0000
  384/60000 [..............................] - ETA: 9s - loss: 0.0460 - acc: 0.9896
  736/60000 [..............................] - ETA: 9s - loss: 0.0584 - acc: 0.9864
1056/60000 [..............................] - ETA: 9s - loss: 0.0662 - acc: 0.9839
1408/60000 [..............................] - ETA: 9s - loss: 0.0674 - acc: 0.9822
1760/60000 [..............................] - ETA: 9s - loss: 0.0679 - acc: 0.9824
2112/60000 [>.............................] - ETA: 8s - loss: 0.0615 - acc: 0.9839
2464/60000 [>.............................] - ETA: 8s - loss: 0.0613 - acc: 0.9838
2816/60000 [>.............................] - ETA: 8s - loss: 0.0592 - acc: 0.9840
3136/60000 [>.............................] - ETA: 8s - loss: 0.0564 - acc: 0.9850
3456/60000 [>.............................] - ETA: 8s - loss: 0.0551 - acc: 0.9852
3776/60000 [>.............................] - ETA: 8s - loss: 0.0529 - acc: 0.9854
4128/60000 [=>............................] - ETA: 8s - loss: 0.0529 - acc: 0.9847
4448/60000 [=>............................] - ETA: 8s - loss: 0.0541 - acc: 0.9836
4800/60000 [=>............................] - ETA: 8s - loss: 0.0572 - acc: 0.9821
5120/60000 [=>............................] - ETA: 8s - loss: 0.0583 - acc: 0.9818
5472/60000 [=>............................] - ETA: 8s - loss: 0.0577 - acc: 0.9819
5824/60000 [=>............................] - ETA: 8s - loss: 0.0567 - acc: 0.9821
6176/60000 [==>...........................] - ETA: 8s - loss: 0.0591 - acc: 0.9809
6528/60000 [==>...........................] - ETA: 8s - loss: 0.0580 - acc: 0.9812
6880/60000 [==>...........................] - ETA: 8s - loss: 0.0595 - acc: 0.9808
7232/60000 [==>...........................] - ETA: 8s - loss: 0.0609 - acc: 0.9809
7584/60000 [==>...........................] - ETA: 8s - loss: 0.0598 - acc: 0.9813
7936/60000 [==>...........................] - ETA: 8s - loss: 0.0595 - acc: 0.9812
8256/60000 [===>..........................] - ETA: 8s - loss: 0.0588 - acc: 0.9812
8608/60000 [===>..........................] - ETA: 7s - loss: 0.0597 - acc: 0.9809
8960/60000 [===>..........................] - ETA: 7s - loss: 0.0588 - acc: 0.9814
9312/60000 [===>..........................] - ETA: 7s - loss: 0.0580 - acc: 0.9817
9664/60000 [===>..........................] - ETA: 7s - loss: 0.0574 - acc: 0.9820
10048/60000 [====>.........................] - ETA: 7s - loss: 0.0577 - acc: 0.9818
10432/60000 [====>.........................] - ETA: 7s - loss: 0.0575 - acc: 0.9818
10784/60000 [====>.........................] - ETA: 7s - loss: 0.0582 - acc: 0.9821
11136/60000 [====>.........................] - ETA: 7s - loss: 0.0584 - acc: 0.9818
11488/60000 [====>.........................] - ETA: 7s - loss: 0.0585 - acc: 0.9817
11840/60000 [====>.........................] - ETA: 7s - loss: 0.0585 - acc: 0.9817
12192/60000 [=====>........................] - ETA: 7s - loss: 0.0592 - acc: 0.9815
12512/60000 [=====>........................] - ETA: 7s - loss: 0.0594 - acc: 0.9815
12864/60000 [=====>........................] - ETA: 7s - loss: 0.0600 - acc: 0.9812
13216/60000 [=====>........................] - ETA: 7s - loss: 0.0603 - acc: 0.9812
13568/60000 [=====>........................] - ETA: 7s - loss: 0.0609 - acc: 0.9810
13952/60000 [=====>........................] - ETA: 7s - loss: 0.0615 - acc: 0.9811
14368/60000 [======>.......................] - ETA: 6s - loss: 0.0622 - acc: 0.9807
14784/60000 [======>.......................] - ETA: 6s - loss: 0.0630 - acc: 0.9805
15168/60000 [======>.......................] - ETA: 6s - loss: 0.0634 - acc: 0.9804
15520/60000 [======>.......................] - ETA: 6s - loss: 0.0647 - acc: 0.9801
15872/60000 [======>.......................] - ETA: 6s - loss: 0.0642 - acc: 0.9803
16224/60000 [=======>......................] - ETA: 6s - loss: 0.0643 - acc: 0.9802
16608/60000 [=======>......................] - ETA: 6s - loss: 0.0644 - acc: 0.9802
16960/60000 [=======>......................] - ETA: 6s - loss: 0.0642 - acc: 0.9804
17312/60000 [=======>......................] - ETA: 6s - loss: 0.0642 - acc: 0.9804
17664/60000 [=======>......................] - ETA: 6s - loss: 0.0640 - acc: 0.9804
18016/60000 [========>.....................] - ETA: 6s - loss: 0.0633 - acc: 0.9805
18368/60000 [========>.....................] - ETA: 6s - loss: 0.0649 - acc: 0.9803
18720/60000 [========>.....................] - ETA: 6s - loss: 0.0646 - acc: 0.9805
19072/60000 [========>.....................] - ETA: 6s - loss: 0.0655 - acc: 0.9801
19424/60000 [========>.....................] - ETA: 6s - loss: 0.0654 - acc: 0.9800
19776/60000 [========>.....................] - ETA: 6s - loss: 0.0657 - acc: 0.9800
20128/60000 [=========>....................] - ETA: 5s - loss: 0.0656 - acc: 0.9801
20480/60000 [=========>....................] - ETA: 5s - loss: 0.0659 - acc: 0.9800
20800/60000 [=========>....................] - ETA: 5s - loss: 0.0663 - acc: 0.9799
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21440/60000 [=========>....................] - ETA: 5s - loss: 0.0659 - acc: 0.9800
21792/60000 [=========>....................] - ETA: 5s - loss: 0.0661 - acc: 0.9799
22144/60000 [==========>...................] - ETA: 5s - loss: 0.0656 - acc: 0.9799
22464/60000 [==========>...................] - ETA: 5s - loss: 0.0655 - acc: 0.9801
22784/60000 [==========>...................] - ETA: 5s - loss: 0.0649 - acc: 0.9803
23104/60000 [==========>...................] - ETA: 5s - loss: 0.0649 - acc: 0.9803
23456/60000 [==========>...................] - ETA: 5s - loss: 0.0649 - acc: 0.9803
23776/60000 [==========>...................] - ETA: 5s - loss: 0.0651 - acc: 0.9802
24128/60000 [===========>..................] - ETA: 5s - loss: 0.0649 - acc: 0.9803
24480/60000 [===========>..................] - ETA: 5s - loss: 0.0646 - acc: 0.9803
24800/60000 [===========>..................] - ETA: 5s - loss: 0.0643 - acc: 0.9804
25120/60000 [===========>..................] - ETA: 5s - loss: 0.0641 - acc: 0.9805
25504/60000 [===========>..................] - ETA: 5s - loss: 0.0642 - acc: 0.9805
25888/60000 [===========>..................] - ETA: 5s - loss: 0.0641 - acc: 0.9805
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26528/60000 [============>.................] - ETA: 5s - loss: 0.0643 - acc: 0.9804
26848/60000 [============>.................] - ETA: 5s - loss: 0.0648 - acc: 0.9804
27168/60000 [============>.................] - ETA: 4s - loss: 0.0645 - acc: 0.9805
27488/60000 [============>.................] - ETA: 4s - loss: 0.0644 - acc: 0.9805
27808/60000 [============>.................] - ETA: 4s - loss: 0.0649 - acc: 0.9803
28160/60000 [=============>................] - ETA: 4s - loss: 0.0649 - acc: 0.9803
28512/60000 [=============>................] - ETA: 4s - loss: 0.0648 - acc: 0.9803
28864/60000 [=============>................] - ETA: 4s - loss: 0.0651 - acc: 0.9802
29216/60000 [=============>................] - ETA: 4s - loss: 0.0654 - acc: 0.9800
29568/60000 [=============>................] - ETA: 4s - loss: 0.0657 - acc: 0.9799
29920/60000 [=============>................] - ETA: 4s - loss: 0.0662 - acc: 0.9799
30272/60000 [==============>...............] - ETA: 4s - loss: 0.0660 - acc: 0.9799
30624/60000 [==============>...............] - ETA: 4s - loss: 0.0659 - acc: 0.9800
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53760/60000 [=========================>....] - ETA: 0s - loss: 0.0659 - acc: 0.9795
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55168/60000 [==========================>...] - ETA: 0s - loss: 0.0665 - acc: 0.9794
55520/60000 [==========================>...] - ETA: 0s - loss: 0.0664 - acc: 0.9794
55840/60000 [==========================>...] - ETA: 0s - loss: 0.0664 - acc: 0.9794
56160/60000 [===========================>..] - ETA: 0s - loss: 0.0664 - acc: 0.9794
56512/60000 [===========================>..] - ETA: 0s - loss: 0.0665 - acc: 0.9794
56864/60000 [===========================>..] - ETA: 0s - loss: 0.0667 - acc: 0.9793
57216/60000 [===========================>..] - ETA: 0s - loss: 0.0667 - acc: 0.9793
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58240/60000 [============================>.] - ETA: 0s - loss: 0.0668 - acc: 0.9793
58592/60000 [============================>.] - ETA: 0s - loss: 0.0668 - acc: 0.9792
58944/60000 [============================>.] - ETA: 0s - loss: 0.0669 - acc: 0.9792
59264/60000 [============================>.] - ETA: 0s - loss: 0.0668 - acc: 0.9792
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59936/60000 [============================>.] - ETA: 0s - loss: 0.0668 - acc: 0.9792
60000/60000 [==============================] - 9s 153us/step - loss: 0.0668 - acc: 0.9792
Epoch 4/5
   32/60000 [..............................] - ETA: 15s - loss: 0.0170 - acc: 1.0000
  384/60000 [..............................] - ETA: 9s - loss: 0.0621 - acc: 0.9766
  736/60000 [..............................] - ETA: 9s - loss: 0.0503 - acc: 0.9823
1088/60000 [..............................] - ETA: 9s - loss: 0.0457 - acc: 0.9825
1440/60000 [..............................] - ETA: 8s - loss: 0.0407 - acc: 0.9847
1760/60000 [..............................] - ETA: 8s - loss: 0.0393 - acc: 0.9858
2080/60000 [>.............................] - ETA: 8s - loss: 0.0390 - acc: 0.9870
2432/60000 [>.............................] - ETA: 8s - loss: 0.0474 - acc: 0.9864
2784/60000 [>.............................] - ETA: 8s - loss: 0.0464 - acc: 0.9867
3200/60000 [>.............................] - ETA: 8s - loss: 0.0454 - acc: 0.9869
3552/60000 [>.............................] - ETA: 8s - loss: 0.0455 - acc: 0.9873
3872/60000 [>.............................] - ETA: 8s - loss: 0.0454 - acc: 0.9873
4224/60000 [=>............................] - ETA: 8s - loss: 0.0457 - acc: 0.9872
4576/60000 [=>............................] - ETA: 8s - loss: 0.0469 - acc: 0.9862
4896/60000 [=>............................] - ETA: 8s - loss: 0.0466 - acc: 0.9861
5248/60000 [=>............................] - ETA: 8s - loss: 0.0465 - acc: 0.9859
5600/60000 [=>............................] - ETA: 8s - loss: 0.0467 - acc: 0.9859
5952/60000 [=>............................] - ETA: 8s - loss: 0.0469 - acc: 0.9857
6272/60000 [==>...........................] - ETA: 8s - loss: 0.0465 - acc: 0.9861
6560/60000 [==>...........................] - ETA: 8s - loss: 0.0464 - acc: 0.9864
6912/60000 [==>...........................] - ETA: 8s - loss: 0.0456 - acc: 0.9867
7232/60000 [==>...........................] - ETA: 8s - loss: 0.0457 - acc: 0.9866
7584/60000 [==>...........................] - ETA: 8s - loss: 0.0446 - acc: 0.9871
7936/60000 [==>...........................] - ETA: 8s - loss: 0.0447 - acc: 0.9869
8288/60000 [===>..........................] - ETA: 7s - loss: 0.0446 - acc: 0.9871
8672/60000 [===>..........................] - ETA: 7s - loss: 0.0438 - acc: 0.9873
9056/60000 [===>..........................] - ETA: 7s - loss: 0.0439 - acc: 0.9869
9440/60000 [===>..........................] - ETA: 7s - loss: 0.0437 - acc: 0.9871
9792/60000 [===>..........................] - ETA: 7s - loss: 0.0445 - acc: 0.9867
10176/60000 [====>.........................] - ETA: 7s - loss: 0.0440 - acc: 0.9869
10560/60000 [====>.........................] - ETA: 7s - loss: 0.0443 - acc: 0.9864
10944/60000 [====>.........................] - ETA: 7s - loss: 0.0435 - acc: 0.9868
11264/60000 [====>.........................] - ETA: 7s - loss: 0.0441 - acc: 0.9864
11616/60000 [====>.........................] - ETA: 7s - loss: 0.0441 - acc: 0.9864
12000/60000 [=====>........................] - ETA: 7s - loss: 0.0447 - acc: 0.9862
12352/60000 [=====>........................] - ETA: 7s - loss: 0.0449 - acc: 0.9860
12704/60000 [=====>........................] - ETA: 7s - loss: 0.0458 - acc: 0.9858
13024/60000 [=====>........................] - ETA: 7s - loss: 0.0467 - acc: 0.9855
13408/60000 [=====>........................] - ETA: 6s - loss: 0.0469 - acc: 0.9855
13760/60000 [=====>........................] - ETA: 6s - loss: 0.0471 - acc: 0.9855
14080/60000 [======>.......................] - ETA: 6s - loss: 0.0471 - acc: 0.9857
14400/60000 [======>.......................] - ETA: 6s - loss: 0.0473 - acc: 0.9853
14720/60000 [======>.......................] - ETA: 6s - loss: 0.0476 - acc: 0.9853
15072/60000 [======>.......................] - ETA: 6s - loss: 0.0477 - acc: 0.9854
15424/60000 [======>.......................] - ETA: 6s - loss: 0.0474 - acc: 0.9853
15744/60000 [======>.......................] - ETA: 6s - loss: 0.0471 - acc: 0.9854
16064/60000 [=======>......................] - ETA: 6s - loss: 0.0472 - acc: 0.9852
16416/60000 [=======>......................] - ETA: 6s - loss: 0.0471 - acc: 0.9853
16768/60000 [=======>......................] - ETA: 6s - loss: 0.0477 - acc: 0.9851
17120/60000 [=======>......................] - ETA: 6s - loss: 0.0476 - acc: 0.9851
17440/60000 [=======>......................] - ETA: 6s - loss: 0.0474 - acc: 0.9851
17792/60000 [=======>......................] - ETA: 6s - loss: 0.0473 - acc: 0.9851
18144/60000 [========>.....................] - ETA: 6s - loss: 0.0475 - acc: 0.9851
18496/60000 [========>.....................] - ETA: 6s - loss: 0.0478 - acc: 0.9850
18848/60000 [========>.....................] - ETA: 6s - loss: 0.0481 - acc: 0.9851
19200/60000 [========>.....................] - ETA: 6s - loss: 0.0483 - acc: 0.9849
19552/60000 [========>.....................] - ETA: 6s - loss: 0.0482 - acc: 0.9850
19872/60000 [========>.....................] - ETA: 6s - loss: 0.0486 - acc: 0.9848
20192/60000 [=========>....................] - ETA: 6s - loss: 0.0486 - acc: 0.9847
20512/60000 [=========>....................] - ETA: 5s - loss: 0.0484 - acc: 0.9847
20864/60000 [=========>....................] - ETA: 5s - loss: 0.0486 - acc: 0.9847
21184/60000 [=========>....................] - ETA: 5s - loss: 0.0488 - acc: 0.9847
21504/60000 [=========>....................] - ETA: 5s - loss: 0.0489 - acc: 0.9846
21856/60000 [=========>....................] - ETA: 5s - loss: 0.0484 - acc: 0.9849
22176/60000 [==========>...................] - ETA: 5s - loss: 0.0487 - acc: 0.9848
22496/60000 [==========>...................] - ETA: 5s - loss: 0.0489 - acc: 0.9847
22848/60000 [==========>...................] - ETA: 5s - loss: 0.0485 - acc: 0.9848
23232/60000 [==========>...................] - ETA: 5s - loss: 0.0489 - acc: 0.9845
23584/60000 [==========>...................] - ETA: 5s - loss: 0.0490 - acc: 0.9845
23904/60000 [==========>...................] - ETA: 5s - loss: 0.0492 - acc: 0.9845
24224/60000 [===========>..................] - ETA: 5s - loss: 0.0491 - acc: 0.9846
24576/60000 [===========>..................] - ETA: 5s - loss: 0.0493 - acc: 0.9845
24928/60000 [===========>..................] - ETA: 5s - loss: 0.0496 - acc: 0.9845
25248/60000 [===========>..................] - ETA: 5s - loss: 0.0498 - acc: 0.9845
25632/60000 [===========>..................] - ETA: 5s - loss: 0.0497 - acc: 0.9845
26048/60000 [============>.................] - ETA: 5s - loss: 0.0501 - acc: 0.9843
26432/60000 [============>.................] - ETA: 5s - loss: 0.0502 - acc: 0.9841
26816/60000 [============>.................] - ETA: 5s - loss: 0.0503 - acc: 0.9841
27200/60000 [============>.................] - ETA: 4s - loss: 0.0502 - acc: 0.9842
27584/60000 [============>.................] - ETA: 4s - loss: 0.0506 - acc: 0.9840
27968/60000 [============>.................] - ETA: 4s - loss: 0.0508 - acc: 0.9839
28352/60000 [=============>................] - ETA: 4s - loss: 0.0510 - acc: 0.9838
28800/60000 [=============>................] - ETA: 4s - loss: 0.0510 - acc: 0.9838
29216/60000 [=============>................] - ETA: 4s - loss: 0.0510 - acc: 0.9837
29632/60000 [=============>................] - ETA: 4s - loss: 0.0507 - acc: 0.9839
30016/60000 [==============>...............] - ETA: 4s - loss: 0.0507 - acc: 0.9839
30400/60000 [==============>...............] - ETA: 4s - loss: 0.0507 - acc: 0.9840
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31168/60000 [==============>...............] - ETA: 4s - loss: 0.0505 - acc: 0.9841
31552/60000 [==============>...............] - ETA: 4s - loss: 0.0505 - acc: 0.9840
31936/60000 [==============>...............] - ETA: 4s - loss: 0.0504 - acc: 0.9842
32320/60000 [===============>..............] - ETA: 4s - loss: 0.0503 - acc: 0.9841
32704/60000 [===============>..............] - ETA: 4s - loss: 0.0504 - acc: 0.9840
33088/60000 [===============>..............] - ETA: 3s - loss: 0.0506 - acc: 0.9839
33472/60000 [===============>..............] - ETA: 3s - loss: 0.0508 - acc: 0.9837
33856/60000 [===============>..............] - ETA: 3s - loss: 0.0507 - acc: 0.9838
34240/60000 [================>.............] - ETA: 3s - loss: 0.0506 - acc: 0.9838
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40000/60000 [===================>..........] - ETA: 2s - loss: 0.0510 - acc: 0.9837
40352/60000 [===================>..........] - ETA: 2s - loss: 0.0509 - acc: 0.9837
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60000/60000 [==============================] - 9s 142us/step - loss: 0.0526 - acc: 0.9832
Epoch 5/5
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10000/10000 [==============================] - 1s 63us/step

Process finished with exit code 0


评论

鼠标滚轮滚得我手指疼
真的有必要搞这么长吗喂



评论
这个模型太小数据太简单并行度不够看不出来什么。

无论如何你都要用gpu来运算，这行业就没人用cpu算的，你要习惯gpu运算方式是，think in gpu。

现在封装的太厚了，你看不出来什么，等你以后要去调grid，调tile，按warp来分配线程的时候，你的传统cpu思维就想不明白了。

评论

楼主按照Win的煞笔计算方式，楼主在开着超线程的时候看到占用率全核50%，实际上对CPU来说已经数据接近满载的高负荷了，于是

至于1950x vs 1080Ti？
我来猜一个10+可能接近20的加速比
（当然不是跑楼主的这个玩具程序，弄个稍微大点的训练集跑ResNet18这种

其实我猜Quadro RTX应该挺好的楼主不来一发吗？
老黄要是良心不是太坏那2080Ti应该也挺好的



评论

唔，为什么一个很普通的酷哒编程被树导师一说立刻就变成了似乎很高端很腻害的东西

果然这就是次元级的差距？

评论

对我来说很高端的，我现在都没掌握，编程一塌糊涂。不过我们主要是玩模型的，编程是副业，硕士生和本科生比我强的多了。

评论

终于等到各路大牛来了

先用 keras 解决问题，后面慢慢看更深的东西。线程这块目前还是0，还有很多要学的

评论

原来是超线程和os的锅，怪不得

Quadro RTX 个人搞不起啊  公司新电脑批了，由于汇率问题转RMB的时候多了一笔，可以考虑上好一点的卡

RTX 由于是成套的工作站估计一时上不了，看看 titan v能不能上。目前dell 官网上只有 P5000选项，回头要问问销售

评论
别用keras，纯tf写会比keras快50%....

评论

很有用的信息，粗略算了下这个数据集 1080ti 应该比 1950x 快10倍左右
后面看看收个 80ti吧，手头 vega 不想换系统搞，换硬件比较方便

评论

有所耳闻，起步阶段还望理解搞imagenet没1080Ti以上的显卡不行的。而且最好要多张。之前买显卡不太花钱，因为可以挖矿回本。现在挖矿不行了，显卡成本陡增。

评论
每次16s？ 你是纯cpu每次都要16 ？
正好我也是初学，搭了下环境。 我是一直玩c++，qt的。 我跑了下你的测试。时间越来越少啊。对了我就是一块960而已。  
Epoch 1/5
60000/60000 [==============================] - 8s 126us/step - loss: 0.2211 - acc: 0.9339
Epoch 2/5
60000/60000 [==============================] - 6s 107us/step - loss: 0.0989 - acc: 0.9702
Epoch 3/5
60000/60000 [==============================] - 6s 105us/step - loss: 0.0699 - acc: 0.9776
Epoch 4/5
60000/60000 [==============================] - 6s 107us/step - loss: 0.0547 - acc: 0.9824
Epoch 5/5
60000/60000 [==============================] - 6s 107us/step - loss: 0.0432 - acc: 0.9860
10000/10000 [==============================] - 0s 37us/step

评论
不知道你做DL的目的是什么，我个人的经验告诉我：

1. cuda+cudnn一般可以比CPU快数十倍

2. 认真学的话投入linux的怀抱吧

3. Keras 高封装函数不利于学习

4. Linux 下 Tensorflow 默认情况下会占用整个gpu memory，如果这个gpu还负责你的显示输出，allow_growth了解一下

5. PyTorch 相比于 Tensorflow会对初学者更友好一些，更帮助你理解网络的构成，但，Tensorboard真香


评论
不知道你做DL的目的是什么，我个人的经验告诉我：

1. cuda+cudnn一般可以比CPU快数十倍

2. 认真学的话投入linux的怀抱吧

3. Keras 高封装函数不利于学习

4. Linux 下 Tensorflow 默认情况下会占用整个gpu memory，如果这个gpu还负责你的显示输出，allow_growth了解一下

5. PyTorch 相比于 Tensorflow会对初学者更友好一些，更帮助你理解网络的构成，但，Tensorboard真香


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我每次都是16s，最后一次到16以内（不是预测，就是说训练）

查了下 960 cuda单元是1024，这样3k5单元的 1080ti 是什么水平心里大概有数了（当然频率也要考虑）


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windows下面效率这么差吗？TF1.9+CUDA9.2+CUDNN7.2.1，1080ti还不如960。。。


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这有点惨吧……持续关注下…… 如果是这个量级，那用现在的 1950x 在初学阶段也勉强够用

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这个model太小了吧。以矩阵乘法（FC层）为例，之前测试过，基本上2560*2560 x 2560*2560的矩阵规模才能跑满GTX1080的理论flops（接近8Tflops）；Conv的复杂度很高，很容易跑满GPU。建议拿一个cnn模型作为benchmark和网上别人用GPU的结果对比。

还有，不要低估GPU的性能，跑深度学习，GPU甚至会比CPU快到接近1个数量级；所以，跑深度学习任务的时候，除了一些特殊场景，基本上都不会考虑用CPU的。。。
ps：以前测试在GTX 750Ti上训练的速度都能秒掉双路E5-2630v3 （每个8C16T）。。。
ps2：注意使用框架的姿势，例如：tf上不要用placeholder+feed_dict的方式，数据从python内存空间传输到tf内存空间上的开销非常大。是否正确使用框架非常影响最终的速度。



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11493376/11490434 [==============================] - 40s 3us/step
Epoch 1/5
60000/60000 [==============================] - 6s 107us/step - loss: 0.2196 - acc: 0.9349
Epoch 2/5
60000/60000 [==============================] - 4s 72us/step - loss: 0.0978 - acc: 0.9705
Epoch 3/5
60000/60000 [==============================] - 4s 72us/step - loss: 0.0681 - acc: 0.9788
Epoch 4/5
60000/60000 [==============================] - 4s 72us/step - loss: 0.0527 - acc: 0.9831
Epoch 5/5
60000/60000 [==============================] - 4s 72us/step - loss: 0.0429 - acc: 0.9861

1080ti
但是计算时候看显卡占用只有40%多


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多谢数据

跑不满应该是数据量少模型简单的缘故，我就是用来参考下看下上显卡的性价比如何

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先感谢哥们的分享
要说model 小，这不是刚开始学直接用的官方指南的第一个例子吗。我总不能上一个 1080ti 要跑几天的模型吧（别说几天，几分钟都不会有人帮我跑的）

顺带您有数据吗……


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可以试试lenet-5在mnist数据集上跑，也是教程系列，小的cnn模型，训练非常快

数据可以参考别人的测试结果：https://github.com/jcjohnson/cnn-benchmarks

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谢谢提供另一个bench test

这个要折腾下了，放狗找了一圈代码也都有些年头或者用了些不熟悉的库，或者没有对整个mnist数据集进行训练。要点时间转换下

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各种经典model的实现参考：https://github.com/tensorflow/models
lenet5+mnist见：https://github.com/tensorflow/mo ... st/convolutional.py

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显存极其重要。。

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不懂装懂

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跑了快一个小时还没出来，撑不住断了睡觉去了

这次cpu占用率是100%了，不再是50%

每次打印出来的时间稳定在 2000 ms 多一点

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不好意思白天一直在训练别的东西。。这会儿刚刚结束
环境:
Windows 10 1803
AMD Ryzen7 1700 + 32GB DDR4 2400
CUDA9 + CUDNN7
非公 GTX1080TI
G:\chh_tf_benchmark>python benchmark.py
Downloading data from https://storage.googleapis.com/t ... -datasets/mnist.npz
11493376/11490434 [==============================] - 2s 0us/step
Epoch 1/5
2018-08-23 02:17:05.223275: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-08-23 02:17:05.623475: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:09:00.0
totalMemory: 11.00GiB freeMemory: 9.10GiB
2018-08-23 02:17:05.638330: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1484] Adding visible gpu devices: 0
2018-08-23 02:17:06.560595: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-08-23 02:17:06.568198: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971]      0
2018-08-23 02:17:06.573614: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:984] 0:   N
2018-08-23 02:17:06.577594: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8793 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:09:00.0, compute capability: 6.1)
60000/60000 [==============================] - 10s 160us/step - loss: 0.2214 - acc: 0.9348
Epoch 2/5
60000/60000 [==============================] - 7s 121us/step - loss: 0.0972 - acc: 0.9697
Epoch 3/5
60000/60000 [==============================] - 7s 123us/step - loss: 0.0685 - acc: 0.9780
Epoch 4/5
60000/60000 [==============================] - 8s 126us/step - loss: 0.0536 - acc: 0.9825
Epoch 5/5
60000/60000 [==============================] - 8s 127us/step - loss: 0.0454 - acc: 0.9852
10000/10000 [==============================] - 0s 46us/step

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观察每个step的速度……通常会稳定
提高性能一方面能加快训练速度，一方面能更快发现模型不收敛之类的问题


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1. 消费级显卡显存太小，只能业余玩玩，亲测EC2 8GB的实例，跑fcn-8s显存不够，只能分阶段训练
2. 新显卡显存带宽升级，可以用更少的core得到更高的性能，因为大部分时间core在等待显存传数据

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记得2080ti有半精度支持来着，这个对训练速度应该有帮助

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谢谢分享

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你们这种也就是找个tensorflow现成的工具死算，有为了针对实际应用写过优化的算法？

还有很多像楼主这种连实际应用场景都没碰过，只能网上找些样本数据跑分，跑完了就大言不惭都说学会了。

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这个确定么……NV一直没有直接说支持FP16


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上面960的数据就是用windows 啊。。 我设置有什么问题吗？  怎么你们的都比我慢？ 。。。

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听起来挺有道理，那就等等评测再看

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唉，system领域其实早就有不少paper解决显存问题了（不断的h2d d2h），然而现在这些框架还是没有给应用上啊……

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gv100、gp100核心才支持fp16，turing架构（包括tensor core）支持的是int8、int4。
并且，说实话，实测在训练过程中砍精度的效果并不太好，一些模型收敛都会有问题。。。

模型压缩目前更多地应用在inference阶段

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1080ti_sli,只调用了一个卡。资源管理器看gpu0占有率10%。gpu1没有调用。我又看了一下，表达的有问题。3d占了10%。显存双卡均占用9.6g，compute_0占用接近40%。compute_1占用0%。


Python 3.5.5 |Anaconda, Inc.| (default, Apr  7 2018, 04:52:34) [MSC v.1900 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.

IPython 6.1.0 -- An enhanced Interactive Python.

runfile('C:/Users/chuan/.spyder-py3/gctest1.py', wdir='C:/Users/chuan/.spyder-py3')
Downloading data from https://storage.googleapis.com/t ... -datasets/mnist.npz
11493376/11490434 [==============================] - 1s 0us/step
Epoch 1/5
60000/60000 [==============================] - 10s 171us/step - loss: 0.2180 - acc: 0.9347
Epoch 2/5
60000/60000 [==============================] - 6s 107us/step - loss: 0.0974 - acc: 0.9697
Epoch 3/5
60000/60000 [==============================] - 6s 107us/step - loss: 0.0683 - acc: 0.9787
Epoch 4/5
60000/60000 [==============================] - 6s 105us/step - loss: 0.0536 - acc: 0.9831
Epoch 5/5
60000/60000 [==============================] - 6s 106us/step - loss: 0.0428 - acc: 0.9856
10000/10000 [==============================] - 0s 42us/step


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但是为啥比上面的960还低呢？是因为没有全力计算所以效率反而下降了吗？
这是第一次算出来的结果。
如果计算过程保存在显存里，继续进行多次计算。
那么时间会缩短。
960也不差啊。小规模运算时并不比1080ti差。

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了解了，谢谢回复

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不一定，要考虑执行的任务是否完全一样，比如：batch size是否一样、tf版本是否一样、模型的实现是否一样、执行tf的方式是否一样（比如，用feed_dict和dataset方式灌数据执行时间的差距就很大）

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ps:tf第一轮比后面轮次慢其实并不是因为后面轮次的数据保存在显存里，tf每次session run的流程都是要执行 数据h2d、compute、结果d2h 的。用nvprof profile一下就会发现第一轮session run比后续的轮次多了很多额外的操作（比如很多额外的h2d d2h），导致第一轮很慢

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谢谢分享

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在执行的程序和环境完全一样的情况下，1080ti的性能一般都是>=960，即使利用率不满（不满是因为没用满gpu的所有的核，但是用上的算力仍然>=960）

ps：除了fp16的计算，maxwell是用fp32模拟fp16，fp16性能等于fp32性能；pascal是硬件支持fp16，只是老黄把除了gp100以外的核心的fp16的核砍得太多，导致fp16性能远远低于fp32。。。（1080ti是gp102）

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树导，我翻到了你一年前的文章

https://www.chiphell.com/thread-1787038-1-1.html

当时还没人感兴趣

今天来看，是不是可以认为扶她的 tensor core 下放到 rtx 上了？

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这个tensor core比volta的更加凶残，只是规模没volta那么大而已。进阶版本。

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rtx主要是在应用环境下使用模型，而volta主要是在生产环境下训练模型，训练模型一般采用较高精度训练，然后经历模型压缩枝剪等操作，最终会产生一个在应用环境下的模型，这个模型对比当初刚训练完后的模型中feature更少，精度更低，以便在真正应用环境下快速运行（应用环境更讲究实时性能，而且通常没有生产环境那么高的计算能力）。

rtx中的tensor core就是为应用环境而特别强化的版本，拥有400T int4性能，推理性能爆表。

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再问问树导更凶残这个说法有出处吗，我去学习一下，多谢多谢

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rtx 8000发布会说的。看69楼。 电路 电子 维修 我现在把定影部分拆出来了。想换下滚，因为卡纸。但是我发现灯管挡住了。拆不了。不会拆。论坛里的高手拆解过吗？ 评论 认真看，认真瞧。果然有收 电路 电子 维修 求创维42c08RD电路图 评论 电视的图纸很少见 评论 电视的图纸很少见 评论 创维的图纸你要说 版号，不然无能为力 评论 板号5800-p42ALM-0050 168P-P42CLM-01
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