Tensorflow + Monero in Docker Impact?

Discussion in 'Docker and Containers' started by Biren78, May 10, 2017.

  1. Biren78

    Biren78 Active Member

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    I know that Monero and Tensorflow use different CPU parts and Monero doesn't run full CPU.

    Will mining Monero in Docker while training models in Tensorflow hurt training performance? A lot?
     
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  2. Patrick

    Patrick Administrator
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    Yes it will. Let me run something overnight so it is big enough to show a difference. Running a job on the machine in question now and will queue this after.
     
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  3. Patrick

    Patrick Administrator
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    Here is LeNet style MINST training eating CPU on a D-1540:
    upload_2017-5-10_22-22-39.png
     
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  4. Patrick

    Patrick Administrator
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    LeNet style MINST training using Theano, with and without Monero mining in another container:
    Code:
    Train on 48000 samples, validate on 12000 samples
    Epoch 1/20
    48000/48000 [==============================] - 220s - loss: 0.1813 - acc: 0.9454 - val_loss: 0.0604 - val_acc: 0.9814
    Epoch 2/20
    48000/48000 [==============================] - 225s - loss: 0.0499 - acc: 0.9842 - val_loss: 0.0490 - val_acc: 0.9847
    Epoch 3/20
    48000/48000 [==============================] - 229s - loss: 0.0339 - acc: 0.9893 - val_loss: 0.0476 - val_acc: 0.9847
    Epoch 4/20
    48000/48000 [==============================] - 221s - loss: 0.0226 - acc: 0.9929 - val_loss: 0.0415 - val_acc: 0.9862
    Epoch 5/20
    48000/48000 [==============================] - 220s - loss: 0.0173 - acc: 0.9945 - val_loss: 0.0339 - val_acc: 0.9897
    Epoch 6/20
    48000/48000 [==============================] - 229s - loss: 0.0136 - acc: 0.9955 - val_loss: 0.0347 - val_acc: 0.9906
    Epoch 7/20
    48000/48000 [==============================] - 226s - loss: 0.0105 - acc: 0.9966 - val_loss: 0.0331 - val_acc: 0.9894
    Epoch 8/20
    48000/48000 [==============================] - 242s - loss: 0.0095 - acc: 0.9969 - val_loss: 0.0473 - val_acc: 0.9868
    Epoch 9/20
    48000/48000 [==============================] - 234s - loss: 0.0062 - acc: 0.9979 - val_loss: 0.0389 - val_acc: 0.9896
    Epoch 10/20
    48000/48000 [==============================] - 226s - loss: 0.0078 - acc: 0.9973 - val_loss: 0.0420 - val_acc: 0.9886
    Epoch 11/20
    48000/48000 [==============================] - 227s - loss: 0.0086 - acc: 0.9970 - val_loss: 0.0481 - val_acc: 0.9868
    Epoch 12/20
    48000/48000 [==============================] - 143s - loss: 0.0062 - acc: 0.9979 - val_loss: 0.0523 - val_acc: 0.9867
    Epoch 13/20
    48000/48000 [==============================] - 144s - loss: 0.0043 - acc: 0.9985 - val_loss: 0.0415 - val_acc: 0.9897
    Epoch 14/20
    48000/48000 [==============================] - 143s - loss: 0.0050 - acc: 0.9984 - val_loss: 0.0480 - val_acc: 0.9885
    Epoch 15/20
    48000/48000 [==============================] - 142s - loss: 0.0059 - acc: 0.9980 - val_loss: 0.0479 - val_acc: 0.9899
    You can see the impact.
     
    #4
    DWSimmons likes this.

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