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我可以在GPU上运行Keras模型吗？

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问题：我可以在GPU上运行Keras模型吗？
回答 0
回答 1
回答 2
回答 3
回答 4

问题：我可以在GPU上运行Keras模型吗？

我正在运行Keras模型，提交截止日期为36小时，如果我在cpu上训练我的模型大约需要50个小时，是否可以在gpu上运行Keras？

我正在使用Tensorflow后端，并在未安装anaconda的Jupyter笔记本上运行它。

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I’m running a Keras model, with a submission deadline of 36 hours, if I train my model on the cpu it will take approx 50 hours, is there a way to run Keras on gpu?

I’m using Tensorflow backend and running it on my Jupyter notebook, without anaconda installed.

回答 0

是的，您可以在GPU上运行keras模型。几件事您将必须首先检查。

  1. 您的系统具有GPU（Nvidia。因为AMD尚未运行）
  2. 您已经安装了Tensorflow的GPU版本
  3. 您已安装CUDA 安装说明
  4. 验证Tensorflow是否与GPU一起运行，检查GPU是否正常工作

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

要么

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

输出将是这样的：

[
  name: "/cpu:0"device_type: "CPU",
  name: "/gpu:0"device_type: "GPU"
]

完成所有这些操作后，您的模型将在GPU上运行：

要检查keras（> = 2.1.1）是否使用GPU：

from keras import backend as K
K.tensorflow_backend._get_available_gpus()

祝一切顺利。

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Yes you can run keras models on GPU. Few things you will have to check first.

  1. your system has GPU (Nvidia. As AMD doesn’t work yet)
  2. You have installed the GPU version of tensorflow
  3. You have installed CUDA installation instructions
  4. Verify that tensorflow is running with GPU check if GPU is working

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

OR

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

output will be something like this:

[
  name: "/cpu:0"device_type: "CPU",
  name: "/gpu:0"device_type: "GPU"
]

Once all this is done your model will run on GPU:

To Check if keras(>=2.1.1) is using GPU:

from keras import backend as K
K.tensorflow_backend._get_available_gpus()

All the best.

回答 1

当然。我想您已经安装了TensorFlow for GPU。

导入keras后，需要添加以下块。我正在使用具有56核心cpu和gpu的计算机。

import keras
import tensorflow as tf


config = tf.ConfigProto( device_count = {'GPU': 1 , 'CPU': 56} ) 
sess = tf.Session(config=config) 
keras.backend.set_session(sess)

当然，这种用法会强制执行我的计算机的最大限制。您可以减少cpu和gpu消耗值。

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Sure. I suppose that you have already installed TensorFlow for GPU.

You need to add the following block after importing keras. I am working on a machine which have 56 core cpu, and a gpu.

import keras
import tensorflow as tf


config = tf.ConfigProto( device_count = {'GPU': 1 , 'CPU': 56} ) 
sess = tf.Session(config=config) 
keras.backend.set_session(sess)

Of course, this usage enforces my machines maximum limits. You can decrease cpu and gpu consumption values.

回答 2

2.0兼容答案：虽然上面提到的答案详细说明了如何在Keras Model上使用GPU，但我想说明如何实现Tensorflow Version 2.0。

要知道有多少个GPU可用，我们可以使用以下代码：

print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

要找出您的操作和张量分配给哪些设备，请将其tf.debugging.set_log_device_placement(True)作为程序的第一条语句。

启用设备放置日志记录将导致打印任何Tensor分配或操作。例如，运行以下代码：

tf.debugging.set_log_device_placement(True)

# Create some tensors
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)

print(c)

给出如下所示的输出：

在设备/ job：localhost / replica：0 / task：0 / device：GPU：0 tf.Tensor（[[22. 28.] [49. 64.]]，shape =（2，2）中执行op MatMul dtype = float32）

有关更多信息，请参考此链接

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2.0 Compatible Answer: While above mentioned answer explain in detail on how to use GPU on Keras Model, I want to explain how it can be done for Tensorflow Version 2.0.

To know how many GPUs are available, we can use the below code:

print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

To find out which devices your operations and tensors are assigned to, put tf.debugging.set_log_device_placement(True) as the first statement of your program.

Enabling device placement logging causes any Tensor allocations or operations to be printed. For example, running the below code:

tf.debugging.set_log_device_placement(True)

# Create some tensors
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)

print(c)

gives the Output shown below:

Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0 tf.Tensor( [[22. 28.] [49. 64.]], shape=(2, 2), dtype=float32)

For more information, refer this link

回答 3

当然。如果您在Tensorflow或CNTk后端上运行，则代码将默认在GPU设备上运行。但是，如果Theano后端，则可以使用以下代码

Theano标志：

“ THEANO_FLAGS = device = gpu，floatX = float32 python my_keras_script.py”

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Of course. if you are running on Tensorflow or CNTk backends, your code will run on your GPU devices defaultly.But if Theano backends, you can use following

Theano flags:

“THEANO_FLAGS=device=gpu,floatX=float32 python my_keras_script.py”

回答 4

在任务管理器中查看脚本是否正在运行GPU。如果不是，请怀疑您的CUDA版本是您所使用的tensorflow版本的正确版本，其他答案已经建议了。

此外，需要使用适用于CUDA版本的适当CUDA DNN库，才能使用tensorflow运行GPU。从此处下载/提取它，并将DLL（例如cudnn64_7.dll）放入CUDA bin文件夹（例如C：\ Program Files \ NVIDIA GPU Computing Toolkit \ CUDA \ v10.1 \ bin）。

点击查看英文原文

See if your script is running GPU in Task manager. If not, suspect your CUDA version is right one for the tensorflow version you are using, as the other answers suggested already.

Additionally, a proper CUDA DNN library for the CUDA version is required to run GPU with tensorflow. Download/extract it from here and put the DLL (e.g., cudnn64_7.dll) into CUDA bin folder (e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin).