無法使用 Plaidml 在 GPU 上運行 Keras Model

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確定這一點，我需要 model 在 GPU 上運行，我還有其他不導入 tensorflow 的模型與 Plaidml 完美配合

源代碼

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()

這是我的任務管理器的圖像（Plaidml 設置為在 GPU 0 [它適用於其他源代碼]上運行）

Answer 1

您同時使用keras and tensorflow.keras 。 僅使用一個版本的 Keras。

Answer 2

嘗試從 keras 導入 keras，而不是使用 tensorflow keras。

 import keras
 from keras import layers

您仍然應該可以訪問所有相同的層、模型等。您可能需要安裝 keras 而不使用 tensorflow

 pip install keras

另請查看您的 GPU memory 用法。 它應該跳起來，因為數據存儲在上面。 在 plaidml 中，大多數計算實際上並不是在 GPU 上運行，而是將權重和數據存儲在其上。