問題描述

我剛剛為我的桌面添加了一個新的 GTX 1070 Founders Addition，我正在嘗試在這個新的 GPU 上運行 tensorflow.我正在使用 tensorflow.device() 在我的 GPU 上運行 tensorflow，但似乎沒有發生這種情況.相反，它使用的是 cpu，而我的幾乎所有系統都使用 8GB 內存.這是我的代碼:

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,
            }))

如果你運行它，當我運行 global_variable_initializer() 時，這個程序似乎退出了.它還在終端中打印:20971520000 的分配超過了系統內存的 10%. 在查看我的任務管理器時，我看到了這個:

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:

該程序占用了我的大量 CPU.

程序占用了我的大量內存.

程序沒有使用我的 GPU.

我不知道為什么會發生這種情況.我正在使用 anaconda 環境，并安裝了 tensorflow-gpu.我非常感謝任何人的建議和幫助.

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，當我運行它時，程序在 global_variable_initializer() 之后停止.我不確定這是否與上述問題有關.

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.

我們將不勝感激.

推薦答案

嘗試用這個簡單的例子比較時間(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)

這篇關于Tensorflow 似乎使用的是系統內存而不是 GPU，并且程序在 global_variable_initializer() 之后停止的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！