无法使用 Plaidml 在 GPU 上运行 Keras 模型

Question

I Want to Run this Keras Model on My GPU but it runs on my cpu I used Plaidml to use my AMD GPU, plaidml is properly set and it runs perfectly on other models I think Maybe Becaouse I'm importing tensoflow but I'm Not sure about that, I need the model to run on the GPU, I have other Models that doesn't import tensorflow that works perfectly with Plaidml

源代码

import numpy as np

from os import environ
environ["KERAS_BACKEND"] = "plaidml.keras.backend"
import keras
from keras.layers import Dense


import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers


master_url_root = "https://raw.githubusercontent.com/numenta/NAB/master/data/"

df_small_noise_url_suffix = "artificialNoAnomaly/art_daily_small_noise.csv"
df_small_noise_url = master_url_root + df_small_noise_url_suffix
df_small_noise = pd.read_csv(
    df_small_noise_url, parse_dates=True, index_col="timestamp"
)

df_daily_jumpsup_url_suffix = "artificialWithAnomaly/art_daily_jumpsup.csv"
df_daily_jumpsup_url = master_url_root + df_daily_jumpsup_url_suffix
df_daily_jumpsup = pd.read_csv(
    df_daily_jumpsup_url, parse_dates=True, index_col="timestamp"
)



fig, ax = plt.subplots()
df_small_noise.plot(legend=False, ax=ax)
plt.show()



training_mean = df_small_noise.mean()
training_std = df_small_noise.std()
df_training_value = (df_small_noise - training_mean) / training_std
print("Number of training samples:", len(df_training_value))




TIME_STEPS = 288

# Generated training sequences for use in the model.
def create_sequences(values, time_steps=TIME_STEPS):
    output = []
    for i in range(len(values) - time_steps + 1):
        output.append(values[i : (i + time_steps)])
    return np.stack(output)


x_train = create_sequences(df_training_value.values)
print("Training input shape: ", x_train.shape)



model = keras.Sequential(
    [
        layers.Input(shape=(x_train.shape[1], x_train.shape[2])),
        layers.Conv1D(
            filters=32, kernel_size=7, padding="same", strides=2, activation="relu"
        ),
        layers.Dropout(rate=0.2),
        layers.Conv1D(
            filters=16, kernel_size=7, padding="same", strides=2, activation="relu"
        ),
        layers.Conv1DTranspose(
            filters=16, kernel_size=7, padding="same", strides=2, activation="relu"
        ),
        layers.Dropout(rate=0.2),
        layers.Conv1DTranspose(
            filters=32, kernel_size=7, padding="same", strides=2, activation="relu"
        ),
        layers.Conv1DTranspose(filters=1, kernel_size=7, padding="same"),
    ]
)
model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss="mse")
model.summary()


history = model.fit(
    x_train,
    x_train,
    epochs=50,
    batch_size=128,
    validation_split=0.1,
    callbacks=[
        keras.callbacks.EarlyStopping(monitor="val_loss", patience=5, mode="min")
    ],
)

plt.plot(history.history["loss"], label="Training Loss")
plt.plot(history.history["val_loss"], label="Validation Loss")
plt.legend()
plt.show()

这是我的任务管理器的图像（Plaidml 在 GPU 0 上设置为运行 [它适用于其他源代码]）

Answer 1

尝试从 keras 导入 keras，而不是使用 tensorflow keras。

 import keras
 from keras import layers

您应该仍然可以访问所有相同的层、模型等。您可能需要使用

安装没有 tensorflow 的 keras

 pip install keras

同时查看您的 GPU 内存使用情况。它应该跳起来，因为数据存储在上面。在 plaidml 中，大多数计算实际上并不在 GPU 上运行，而是将权重和数据存储在其上。

此外，也许使用以下代码块代替您当前的第一行代码。

 import numpy as np
 import plaidml.keras
 import os
 plaidml.keras.install_backend()

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