为什么 Tensorflow 不在 GPU 上 运行 而 GPU 设备在 python 中被识别？
Why Tensorflow not running on GPU while GPU devices are identified in python?
我在 windows 10 中安装了 TensorFlow 2.2.0 和 TensorFlow-gpu 2.2.0。另外，我安装了 CUDA Toolkit v10.1 并在 CUDA directories 中复制了 cuDNN 7.6.5 个文件。我的 GPU 是 NVIDIA GeForce 940 MX。
此外，我在 windows 上设置了 CUDA 路径。当我通过以下代码测试设备时，CPU 和 GPU 都被识别：
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
输出为：
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 13265748925766868529,
name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 14569071601529958377
physical_device_desc: "device: XLA_CPU device",
name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 15045400394346252324
physical_device_desc: "device: XLA_GPU device"]
但是，当我 运行 我的代码时，似乎代码 运行 就在 CPU 上。另外，当我使用 tf.test.is_gpu_available() 测试 GPU 可用性时，无法识别 GPU 设备并显示 False value。
或者当我们 运行 tf.config.list_physical_devices('GPU') 时，打印 empty list 或 []。而当我运行tf.config.experimental.list_physical_devices()时，这三个physical devices显示在一个列表中：
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),
PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),
PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]
重要的是，当我运行 tf.config.list_physical_devices('XLA_GPU')时，会打印出：
[PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]
此外，当我们 运行 代码时，任务管理器显示 CPU 使用了 96% 的能力，而 GPU 只使用了 1% 的能力。
我们运行的代码如下：
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from yahoo_fin import stock_info as si
from collections import deque
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
import os
import random
# set seed, so we can get the same results after rerunning several times
np.random.seed(314)
tf.random.set_seed(314)
random.seed(314)
def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1,
test_size=0.2, feature_columns=['adjclose', 'volume', 'open', 'high', 'low']):
# see if ticker is already a loaded stock from yahoo finance
if isinstance(ticker, str):
# load it from yahoo_fin library
df = si.get_data(ticker)
elif isinstance(ticker, pd.DataFrame):
# already loaded, use it directly
df = ticker
# this will contain all the elements we want to return from this function
result = {}
# we will also return the original dataframe itself
result['df'] = df.copy()
# make sure that the passed feature_columns exist in the dataframe
for col in feature_columns:
assert col in df.columns, f"'{col}' does not exist in the dataframe."
if scale:
column_scaler = {}
# scale the data (prices) from 0 to 1
for column in feature_columns:
scaler = preprocessing.MinMaxScaler()
df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1))
column_scaler[column] = scaler
# add the MinMaxScaler instances to the result returned
result["column_scaler"] = column_scaler
# add the target column (label) by shifting by `lookup_step`
df['future'] = df['adjclose'].shift(-lookup_step)
# last `lookup_step` columns contains NaN in future column
# get them before droping NaNs
last_sequence = np.array(df[feature_columns].tail(lookup_step))
# drop NaNs
df.dropna(inplace=True)
sequence_data = []
sequences = deque(maxlen=n_steps)
for entry, target in zip(df[feature_columns].values, df['future'].values):
sequences.append(entry)
if len(sequences) == n_steps:
sequence_data.append([np.array(sequences), target])
# get the last sequence by appending the last `n_step` sequence with `lookup_step` sequence
# for instance, if n_steps=50 and lookup_step=10, last_sequence should be of 59 (that is 50+10-1) length
# this last_sequence will be used to predict in future dates that are not available in the dataset
last_sequence = list(sequences) + list(last_sequence)
# shift the last sequence by -1
last_sequence = np.array(pd.DataFrame(last_sequence).shift(-1).dropna())
# add to result
result['last_sequence'] = last_sequence
# construct the X's and y's
X, y = [], []
for seq, target in sequence_data:
X.append(seq)
y.append(target)
# convert to numpy arrays
X = np.array(X)
y = np.array(y)
# reshape X to fit the neural network
X = X.reshape((X.shape[0], X.shape[2], X.shape[1]))
# split the dataset
result["X_train"], result["X_test"], result["y_train"], result["y_test"] = train_test_split(X, y, test_size=test_size, shuffle=shuffle)
# return the result
return result
def create_model(sequence_length, units=256, cell=LSTM, n_layers=2, dropout=0.3,
loss="mean_absolute_error", optimizer="rmsprop", bidirectional=False):
model = Sequential()
for i in range(n_layers):
if i == 0:
# first layer
if bidirectional:
model.add(Bidirectional(cell(units, return_sequences=True), input_shape=(None, sequence_length)))
else:
model.add(cell(units, return_sequences=True, input_shape=(None, sequence_length)))
elif i == n_layers - 1:
# last layer
if bidirectional:
model.add(Bidirectional(cell(units, return_sequences=False)))
else:
model.add(cell(units, return_sequences=False))
else:
# hidden layers
if bidirectional:
model.add(Bidirectional(cell(units, return_sequences=True)))
else:
model.add(cell(units, return_sequences=True))
# add dropout after each layer
model.add(Dropout(dropout))
model.add(Dense(1, activation="linear"))
model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer)
return model
# Window size or the sequence length
N_STEPS = 100
# Lookup step, 1 is the next day
LOOKUP_STEP = 1
# test ratio size, 0.2 is 20%
TEST_SIZE = 0.2
# features to use
FEATURE_COLUMNS = ["adjclose", "volume", "open", "high", "low"]
# date now
date_now = time.strftime("%Y-%m-%d")
### model parameters
N_LAYERS = 3
# LSTM cell
CELL = LSTM
# 256 LSTM neurons
UNITS = 256
# 40% dropout
DROPOUT = 0.4
# whether to use bidirectional RNNs
BIDIRECTIONAL = False
### training parameters
# mean absolute error loss
# LOSS = "mae"
# huber loss
LOSS = "huber_loss"
OPTIMIZER = "adam"
BATCH_SIZE = 64
EPOCHS = 400
# Apple stock market
ticker = "AAPL"
ticker_data_filename = os.path.join("data", f"{ticker}_{date_now}.csv")
# model name to save, making it as unique as possible based on parameters
model_name = f"{date_now}_{ticker}-{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"
if BIDIRECTIONAL:
model_name += "-b"
# create these folders if they does not exist
if not os.path.isdir("results"):
os.mkdir("results")
if not os.path.isdir("logs"):
os.mkdir("logs")
if not os.path.isdir("data"):
os.mkdir("data")
# load the data
data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, feature_columns=FEATURE_COLUMNS)
# save the dataframe
data["df"].to_csv(ticker_data_filename)
# construct the model
model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS,
dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL)
# some tensorflow callbacks
checkpointer = ModelCheckpoint(os.path.join("results", model_name + ".h5"), save_weights_only=True, save_best_only=True, verbose=1)
tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name))
history = model.fit(data["X_train"], data["y_train"],
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(data["X_test"], data["y_test"]),
callbacks=[checkpointer, tensorboard],
verbose=1)
model.save(os.path.join("results", model_name) + ".h5")
**GPU and CPU performance**如下：
你能帮帮我吗？
我用setNVIDIA Control Panel解决了这个问题。我在桌面上右击并选择 NVIDIA Control panel：
然后，通过 Set PhysX Configuration，我像这样转到 Select a PhysX Processor 和 select Auto-Select recommended：
此外，从 Manage 3D settings 开始，我通过单击 Restore button 恢复了设置：
此外，您可以从本节的 Program Settings 开始在 GPU 上设置 Python。我做了 that.Please，在每个阶段应用了所有更改。
最后，使用 运行 以上代码中的每一个有利结果如下所示：
Code1:
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
Output1:
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 12330560057435677891
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 14076398930644318194
physical_device_desc: "device: XLA_CPU device"
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 3186897715
locality {
bus_id: 1
links {
}
}
incarnation: 5889399188264267952
physical_device_desc: "device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0, compute capability: 5.0"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 8080361800351872259
physical_device_desc: "device: XLA_GPU device"
]
Code2:
import tensorflow as tf
tf.config.list_physical_devices('GPU')
Output2:
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Code3:
tf.test.is_gpu_available()
Output3:
True
代码应该是这样的：
def load_data(ticker, n_steps=N_STEPS)
所以你可以在代码中把你定义为100的变量提前传进去
我在 windows 10 中安装了 TensorFlow 2.2.0 和 TensorFlow-gpu 2.2.0。另外，我安装了 CUDA Toolkit v10.1 并在 CUDA directories 中复制了 cuDNN 7.6.5 个文件。我的 GPU 是 NVIDIA GeForce 940 MX。
此外，我在 windows 上设置了 CUDA 路径。当我通过以下代码测试设备时，CPU 和 GPU 都被识别：
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
输出为：
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 13265748925766868529,
name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 14569071601529958377
physical_device_desc: "device: XLA_CPU device",
name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 15045400394346252324
physical_device_desc: "device: XLA_GPU device"]
但是，当我 运行 我的代码时，似乎代码 运行 就在 CPU 上。另外，当我使用 tf.test.is_gpu_available() 测试 GPU 可用性时，无法识别 GPU 设备并显示 False value。
或者当我们 运行 tf.config.list_physical_devices('GPU') 时，打印 empty list 或 []。而当我运行tf.config.experimental.list_physical_devices()时，这三个physical devices显示在一个列表中：
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),
PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),
PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]
重要的是，当我运行 tf.config.list_physical_devices('XLA_GPU')时，会打印出：
[PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]
此外，当我们 运行 代码时，任务管理器显示 CPU 使用了 96% 的能力，而 GPU 只使用了 1% 的能力。
我们运行的代码如下：
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from yahoo_fin import stock_info as si
from collections import deque
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
import os
import random
# set seed, so we can get the same results after rerunning several times
np.random.seed(314)
tf.random.set_seed(314)
random.seed(314)
def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1,
test_size=0.2, feature_columns=['adjclose', 'volume', 'open', 'high', 'low']):
# see if ticker is already a loaded stock from yahoo finance
if isinstance(ticker, str):
# load it from yahoo_fin library
df = si.get_data(ticker)
elif isinstance(ticker, pd.DataFrame):
# already loaded, use it directly
df = ticker
# this will contain all the elements we want to return from this function
result = {}
# we will also return the original dataframe itself
result['df'] = df.copy()
# make sure that the passed feature_columns exist in the dataframe
for col in feature_columns:
assert col in df.columns, f"'{col}' does not exist in the dataframe."
if scale:
column_scaler = {}
# scale the data (prices) from 0 to 1
for column in feature_columns:
scaler = preprocessing.MinMaxScaler()
df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1))
column_scaler[column] = scaler
# add the MinMaxScaler instances to the result returned
result["column_scaler"] = column_scaler
# add the target column (label) by shifting by `lookup_step`
df['future'] = df['adjclose'].shift(-lookup_step)
# last `lookup_step` columns contains NaN in future column
# get them before droping NaNs
last_sequence = np.array(df[feature_columns].tail(lookup_step))
# drop NaNs
df.dropna(inplace=True)
sequence_data = []
sequences = deque(maxlen=n_steps)
for entry, target in zip(df[feature_columns].values, df['future'].values):
sequences.append(entry)
if len(sequences) == n_steps:
sequence_data.append([np.array(sequences), target])
# get the last sequence by appending the last `n_step` sequence with `lookup_step` sequence
# for instance, if n_steps=50 and lookup_step=10, last_sequence should be of 59 (that is 50+10-1) length
# this last_sequence will be used to predict in future dates that are not available in the dataset
last_sequence = list(sequences) + list(last_sequence)
# shift the last sequence by -1
last_sequence = np.array(pd.DataFrame(last_sequence).shift(-1).dropna())
# add to result
result['last_sequence'] = last_sequence
# construct the X's and y's
X, y = [], []
for seq, target in sequence_data:
X.append(seq)
y.append(target)
# convert to numpy arrays
X = np.array(X)
y = np.array(y)
# reshape X to fit the neural network
X = X.reshape((X.shape[0], X.shape[2], X.shape[1]))
# split the dataset
result["X_train"], result["X_test"], result["y_train"], result["y_test"] = train_test_split(X, y, test_size=test_size, shuffle=shuffle)
# return the result
return result
def create_model(sequence_length, units=256, cell=LSTM, n_layers=2, dropout=0.3,
loss="mean_absolute_error", optimizer="rmsprop", bidirectional=False):
model = Sequential()
for i in range(n_layers):
if i == 0:
# first layer
if bidirectional:
model.add(Bidirectional(cell(units, return_sequences=True), input_shape=(None, sequence_length)))
else:
model.add(cell(units, return_sequences=True, input_shape=(None, sequence_length)))
elif i == n_layers - 1:
# last layer
if bidirectional:
model.add(Bidirectional(cell(units, return_sequences=False)))
else:
model.add(cell(units, return_sequences=False))
else:
# hidden layers
if bidirectional:
model.add(Bidirectional(cell(units, return_sequences=True)))
else:
model.add(cell(units, return_sequences=True))
# add dropout after each layer
model.add(Dropout(dropout))
model.add(Dense(1, activation="linear"))
model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer)
return model
# Window size or the sequence length
N_STEPS = 100
# Lookup step, 1 is the next day
LOOKUP_STEP = 1
# test ratio size, 0.2 is 20%
TEST_SIZE = 0.2
# features to use
FEATURE_COLUMNS = ["adjclose", "volume", "open", "high", "low"]
# date now
date_now = time.strftime("%Y-%m-%d")
### model parameters
N_LAYERS = 3
# LSTM cell
CELL = LSTM
# 256 LSTM neurons
UNITS = 256
# 40% dropout
DROPOUT = 0.4
# whether to use bidirectional RNNs
BIDIRECTIONAL = False
### training parameters
# mean absolute error loss
# LOSS = "mae"
# huber loss
LOSS = "huber_loss"
OPTIMIZER = "adam"
BATCH_SIZE = 64
EPOCHS = 400
# Apple stock market
ticker = "AAPL"
ticker_data_filename = os.path.join("data", f"{ticker}_{date_now}.csv")
# model name to save, making it as unique as possible based on parameters
model_name = f"{date_now}_{ticker}-{LOSS}-{OPTIMIZER}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}"
if BIDIRECTIONAL:
model_name += "-b"
# create these folders if they does not exist
if not os.path.isdir("results"):
os.mkdir("results")
if not os.path.isdir("logs"):
os.mkdir("logs")
if not os.path.isdir("data"):
os.mkdir("data")
# load the data
data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, feature_columns=FEATURE_COLUMNS)
# save the dataframe
data["df"].to_csv(ticker_data_filename)
# construct the model
model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS,
dropout=DROPOUT, optimizer=OPTIMIZER, bidirectional=BIDIRECTIONAL)
# some tensorflow callbacks
checkpointer = ModelCheckpoint(os.path.join("results", model_name + ".h5"), save_weights_only=True, save_best_only=True, verbose=1)
tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name))
history = model.fit(data["X_train"], data["y_train"],
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(data["X_test"], data["y_test"]),
callbacks=[checkpointer, tensorboard],
verbose=1)
model.save(os.path.join("results", model_name) + ".h5")
**GPU and CPU performance**如下：
你能帮帮我吗？
我用setNVIDIA Control Panel解决了这个问题。我在桌面上右击并选择 NVIDIA Control panel：
然后，通过 Set PhysX Configuration，我像这样转到 Select a PhysX Processor 和 select Auto-Select recommended：
此外，从 Manage 3D settings 开始，我通过单击 Restore button 恢复了设置：
Program Settings 开始在 GPU 上设置 Python。我做了 that.Please，在每个阶段应用了所有更改。
最后，使用 运行 以上代码中的每一个有利结果如下所示：
Code1:
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
Output1:
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 12330560057435677891
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 14076398930644318194
physical_device_desc: "device: XLA_CPU device"
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 3186897715
locality {
bus_id: 1
links {
}
}
incarnation: 5889399188264267952
physical_device_desc: "device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0, compute capability: 5.0"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 8080361800351872259
physical_device_desc: "device: XLA_GPU device"
]
Code2:
import tensorflow as tf
tf.config.list_physical_devices('GPU')
Output2:
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Code3:
tf.test.is_gpu_available()
Output3:
True
代码应该是这样的：
def load_data(ticker, n_steps=N_STEPS)
所以你可以在代码中把你定义为100的变量提前传进去