如何确保 Keras 使用带有 tensorflow 后端的 GPU？

Question

I've created virtual notebook on Paperspace cloud infrastructure with Tensorflow GPU P5000 virtual instance on the backend. When i am starting to train my network, it woks 2x SLOWER than on my MacBook Pro with pure CPU runtime engine. How could i ensure that Keras NN is using GPU instead of CPU during training process?

Please find my code below:

from tensorflow.contrib.keras.api.keras.models import Sequential
from tensorflow.contrib.keras.api.keras.layers import Dense
from tensorflow.contrib.keras.api.keras.layers import Dropout
from tensorflow.contrib.keras.api.keras import utils as np_utils
import numpy as np
import pandas as pd

# Read data
pddata= pd.read_csv('data/data.csv', delimiter=';')

# Helper function (prepare & test data)
def split_to_train_test (data):
    trainLenght = len(data) - len(data)//10

    trainData = data.loc[:trainLenght].sample(frac=1).reset_index(drop=True)
    testData = data.loc[trainLenght+1:].sample(frac=1).reset_index(drop=True)

    trainLabels = trainData.loc[:,"Label"].as_matrix()
    testLabels = testData.loc[:,"Label"].as_matrix()

    trainData = trainData.loc[:,"Feature 0":].as_matrix()
    testData  = testData.loc[:,"Feature 0":].as_matrix()

    return (trainData, testData, trainLabels, testLabels)

# prepare train & test data
(X_train, X_test, y_train, y_test) = split_to_train_test (pddata)

# Convert labels to one-hot notation
Y_train = np_utils.to_categorical(y_train, 3)
Y_test  = np_utils.to_categorical(y_test, 3)

# Define model in Keras
def create_model(init):
    model = Sequential()
    model.add(Dense(101, input_shape=(101,), kernel_initializer=init, activation='tanh'))
    model.add(Dense(101, kernel_initializer=init, activation='tanh'))
    model.add(Dense(101, kernel_initializer=init, activation='tanh'))
    model.add(Dense(101, kernel_initializer=init, activation='tanh'))
    model.add(Dense(3, kernel_initializer=init, activation='softmax'))
    return model

# Train the model
uniform_model = create_model("glorot_normal")
uniform_model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
uniform_model.fit(X_train, Y_train, batch_size=1, epochs=300, verbose=1, validation_data=(X_test, Y_test)) 

Answer 1

You need to run your network with log_device_placement = True set in the TensorFlow session (the line before the last in the sample code below.) Interestingly enough, if you set that in a session, it will still apply when Keras does the fitting. So this code below (tested) does output the placement for each tensor. Please note, I've short-circuited the data reading because your data wan't available, so I'm just running the network with random data. The code this way is self-contained and runnable by anyone. Another note: if you run this from Jupyter Notebook, the output of the log_device_placement will go to the terminal where Jupyter Notebook was started , not the notebook cell's output.

from tensorflow.contrib.keras.api.keras.models import Sequential
from tensorflow.contrib.keras.api.keras.layers import Dense
from tensorflow.contrib.keras.api.keras.layers import Dropout
from tensorflow.contrib.keras.api.keras import utils as np_utils
import numpy as np
import pandas as pd
import tensorflow as tf

# Read data
#pddata=pd.read_csv('data/data.csv', delimiter=';')
pddata = "foobar"

# Helper function (prepare & test data)
def split_to_train_test (data):

    return (
        np.random.uniform( size = ( 100, 101 ) ),
        np.random.uniform( size = ( 100, 101 ) ),
        np.random.randint( 0, size = ( 100 ), high = 3 ),
        np.random.randint( 0, size = ( 100 ), high = 3 )
    )

    trainLenght = len(data) - len(data)//10

    trainData = data.loc[:trainLenght].sample(frac=1).reset_index(drop=True)
    testData = data.loc[trainLenght+1:].sample(frac=1).reset_index(drop=True)

    trainLabels = trainData.loc[:,"Label"].as_matrix()
    testLabels = testData.loc[:,"Label"].as_matrix()

    trainData = trainData.loc[:,"Feature 0":].as_matrix()
    testData  = testData.loc[:,"Feature 0":].as_matrix()

    return (trainData, testData, trainLabels, testLabels)

# prepare train & test data
(X_train, X_test, y_train, y_test) = split_to_train_test (pddata)

# Convert labels to one-hot notation
Y_train = np_utils.to_categorical(y_train, 3)
Y_test  = np_utils.to_categorical(y_test, 3)

# Define model in Keras
def create_model(init):
    model = Sequential()
    model.add(Dense(101, input_shape=(101,), kernel_initializer=init, activation='tanh'))
    model.add(Dense(101, kernel_initializer=init, activation='tanh'))
    model.add(Dense(101, kernel_initializer=init, activation='tanh'))
    model.add(Dense(101, kernel_initializer=init, activation='tanh'))
    model.add(Dense(3, kernel_initializer=init, activation='softmax'))
    return model

# Train the model
uniform_model = create_model("glorot_normal")
uniform_model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
with tf.Session( config = tf.ConfigProto( log_device_placement = True ) ):
    uniform_model.fit(X_train, Y_train, batch_size=1, epochs=300, verbose=1, validation_data=(X_test, Y_test)) 

Terminal output (partial, it was way too long):

...
VarIsInitializedOp_13: (VarIsInitializedOp): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-21 21:54:33.485870: I tensorflow/core/common_runtime/placer.cc:884]
VarIsInitializedOp_13: (VarIsInitializedOp)/job:localhost/replica:0/task:0/device:GPU:0
training/SGD/mul_18/ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-21 21:54:33.485895: I tensorflow/core/common_runtime/placer.cc:884]
training/SGD/mul_18/ReadVariableOp: (ReadVariableOp)/job:localhost/replica:0/task:0/device:GPU:0
training/SGD/Variable_9/Read/ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-21 21:54:33.485903: I tensorflow/core/common_runtime/placer.cc:884]
training/SGD/Variable_9/Read/ReadVariableOp: (ReadVariableOp)/job:localhost/replica:0/task:0/device:GPU:0
...

Note the GPU:0 at the end of many lines.

Tensorflow manual's relevant page: Using GPU: Logging Device Placement .

Answer 2

Put this near the top of your jupyter notebook. Comment out what you don't need.

# confirm TensorFlow sees the GPU
from tensorflow.python.client import device_lib
assert 'GPU' in str(device_lib.list_local_devices())

# confirm Keras sees the GPU (for TensorFlow 1.X + Keras)
from keras import backend
assert len(backend.tensorflow_backend._get_available_gpus()) > 0

# confirm PyTorch sees the GPU
from torch import cuda
assert cuda.is_available()
assert cuda.device_count() > 0
print(cuda.get_device_name(cuda.current_device()))

---

NOTE: With the release of TensorFlow 2.0, Keras is now included as part of the TF API.

Originally answerwed here .

Answer 3

Considering keras is a built-in of tensorflow since version 2.0:

import tensorflow as tf
tf.test.is_built_with_cuda()  
tf.test.is_gpu_available(cuda_only = True)  

NOTE: the latter method may take several minutes to run.