Tensorflow 似乎使用的是系统内存而不是 GPU，并且

问题描述

我刚刚为我的桌面添加了一个新的 GTX 1070 Founders Addition，我正在尝试在这个新的 GPU 上运行 tensorflow.我正在使用 tensorflow.device() 在我的 GPU 上运行 tensorflow，但似乎没有发生这种情况.相反，它使用的是 cpu，而我的几乎所有系统都使用 8GB 内存.这是我的代码:

I just got a new GTX 1070 Founders Addition for my desktop, and I am trying to run tensorflow on this new GPU. I am using tensorflow.device() to run tensorflow on my GPU, but it seems like this is not happening. Instead it is using cpu, and almost all of my systems 8GB of ram. Here is my code:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import matplotlib.image as mpimg
import math

print("

")
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#
with tf.device("/gpu:0"):
    # Helper Function To Print Percentage
    def showPercent(num, den, roundAmount):
        print(  str( round((num / den) * roundAmount )/roundAmount ) + " % ", end="
")
    # Defince The Number Of Images To Get
    def getFile(dir, getEveryNthLine):
        allFiles = list(os.listdir(dir))
        fileNameList = []

        numOfFiles = len(allFiles)
        i = 0
        for fichier in allFiles:
            if(i % 100 == 0):
                showPercent(i, numOfFiles, 100)

            if(i % getEveryNthLine == 0):
                if(fichier.endswith(".png")):
                    fileNameList.append(dir + "/" + fichier[0:-4])
            i += 1
        return fileNameList

    # Other Helper Functions
    def init_weights(shape):
        init_random_dist = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float16)
        return tf.Variable(init_random_dist)
    def init_bias(shape):
        init_bias_vals = tf.constant(0.1, shape=shape, dtype=tf.float16)
        return tf.Variable(init_bias_vals)
    def conv2d(x, W):
        # x --> [batch, H, W, Channels]
        # W --> [filter H, filter W, Channels IN, Channels Out]

        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
    def max_pool_2by2(x):
        # x --> [batch, H, W, Channels]
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
    def convolutional_layer(input_x, shape):
        W = init_weights(shape)
        b = init_bias([ shape[3] ])
        return tf.nn.relu(conv2d(input_x, W) + b)
    def normal_full_layer(input_layer, size):
        input_size = int(input_layer.get_shape()[1])
        W = init_weights([input_size, size])
        b = init_bias([size])
        return tf.matmul(input_layer, W) + b

    print("Getting Images")
    fileNameList = getFile("F:cartoonset10k-small", 1000)
    print("
loaded " + str(len(fileNameList)) + " files")

    print("Defining Placeholders")
    x_ph = tf.placeholder(tf.float16, shape=[None, 400, 400, 4])
    y_ph = tf.placeholder(tf.float16, shape=[None])

    print("Defining Conv and Pool layer 1")
    convo_1 = convolutional_layer(x_ph, shape=[5, 5, 4, 32])
    convo_1_pooling = max_pool_2by2(convo_1)

    print("Defining Conv and Pool layer 2")
    convo_2 = convolutional_layer(convo_1_pooling, shape=[5, 5, 32, 64])
    convo_2_pooling = max_pool_2by2(convo_2)

    print("Define Flat later and a Full layer")
    convo_2_flat = tf.reshape(convo_2_pooling, [-1, 400 * 400 * 64])
    full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat, 1024))
    y_pred = full_layer_one # Add Dropout Later

    def getLabels(filePath):
        df = []
        with open(filePath, "r") as file:
            for line in list(file):
                tempList = line.replace("
", "").replace('"', "").replace(" ", "").split(",")
                df.append({
                    "attr": tempList[0],
                    "value":int(tempList[1]),
                    "maxValue":int(tempList[2])
                })
        return df

    print("
Splitting And Formating X, and Y Data")
    x_data = []
    y_data = []
    numOfFiles = len(fileNameList)
    i = 0
    for file in fileNameList:
        if i % 10 == 0:
            showPercent(i, numOfFiles, 100)
        x_data.append(mpimg.imread(file + ".png"))
        y_data.append(pd.DataFrame(getLabels(file + ".csv"))["value"][0])
        i += 1

    print("
Conveting x_data to list")
    i = 0
    for indx in range(len(x_data)):
        if i % 10 == 0:
            showPercent(i, numOfFiles, 100)
        x_data[indx] = x_data[indx].tolist()
        i += 1

    print("

Performing Train Test Split")
    train_x, test_x, train_y, test_y = train_test_split(x_data, y_data, test_size=0.2)

    print("Defining Loss And Optimizer")
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits_v2(
            labels=y_ph,
            logits=y_pred
        )
    )
    optimizer = tf.train.AdadeltaOptimizer(learning_rate=0.001)
    train = optimizer.minimize(cross_entropy)

    print("Define Var Init")
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        print("Checkpoint Before Initializer")
        sess.run(init)
        print("Checkpoint After Initializer")
        batch_size = 8
        steps = 1
        i = 0
        for i in range(steps):
            if i % 10:
                print(i / 100, end="
")

            batch_x = []
            i = 0
            for i in np.random.randint(len(train_x), size=batch_size):
                showPercent(i, len(train_x), 100)
                train_x[i]
            batch_x = [train_x[i] for i in np.random.randint(len(train_x), size=batch_size) ]
            batch_y = [train_y[i] for i in np.random.randint(len(train_y), size=batch_size) ]
            print(sess.run(train, {
                x_ph:train_x,
                y_ph:train_y,
            }))

如果你运行它，当我运行 global_variable_initializer() 时，这个程序似乎退出了.它还在终端中打印:20971520000 的分配超过了系统内存的 10%. 在查看我的任务管理器时，我看到了这个:

If you run this, this program seems to quit when I run global_variable_initializer(). It also prints in the terminal: Allocation of 20971520000 exceeds 10% of system memory. When looking at my task manager, I see this:

该程序占用了我的大量 CPU.

程序占用了我的大量内存.

程序没有使用我的 GPU.

我不知道为什么会发生这种情况.我正在使用 anaconda 环境，并安装了 tensorflow-gpu.我非常感谢任何人的建议和帮助.

I am not shore why this is happening. I am using an anaconda environment, and have installed tensorflow-gpu. I would really appreciate anyones suggestions and help.

另外，当我运行它时，程序在 global_variable_initializer() 之后停止.我不确定这是否与上述问题有关.

In addition, when I run this, the program stops after global_variable_initializer(). I am not sure if this is related to the problem above.

Tensorflow 是 1.12 版.CUDA 是 10.0.130 版本.

Tensorflow is version 1.12. CUDA is version 10.0.130.

我们将不胜感激.

推荐答案

尝试用这个简单的例子比较时间(GPU vs CPU):

Try compare time (GPU vs CPU) with this simple example:

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

def create_model():
    model = tf.keras.models.Sequential([
      tf.keras.layers.Flatten(input_shape=(28, 28)),
      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'])
    return model    

epoch = 3

print('GPU:')
with tf.device('/gpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)

print('
CPU:')
with tf.device('/cpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)

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