[英]How do I check if keras is using gpu version of tensorflow?
When I run a keras script, I get the following output:当我运行 keras 脚本时,我得到以下输出:
Using TensorFlow backend.
2017-06-14 17:40:44.621761: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use SSE4.1 instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:40:44.621783: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use SSE4.2 instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:40:44.621788: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:40:44.621791: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX2 instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:40:44.621795: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use FMA instructions, but these are
available
on your machine and could speed up CPU computations.
2017-06-14 17:40:44.721911: I
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful
NUMA node read from SysFS had negative value (-1), but there must be
at least one NUMA node, so returning NUMA node zero
2017-06-14 17:40:44.722288: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0
with properties:
name: GeForce GTX 850M
major: 5 minor: 0 memoryClockRate (GHz) 0.9015
pciBusID 0000:0a:00.0
Total memory: 3.95GiB
Free memory: 3.69GiB
2017-06-14 17:40:44.722302: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2017-06-14 17:40:44.722307: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2017-06-14 17:40:44.722312: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M,
pci bus id: 0000:0a:00.0)
What does this mean?这是什么意思? Am I using GPU or CPU version of tensorflow?
我使用的是 GPU 还是 CPU 版本的 tensorflow?
Before installing keras, I was working with the GPU version of tensorflow.在安装 keras 之前,我正在使用 GPU 版本的 tensorflow。
Also sudo pip3 list
shows tensorflow-gpu(1.1.0)
and nothing like tensorflow-cpu
.另外
sudo pip3 list
显示tensorflow-gpu(1.1.0)
和tensorflow-cpu
。
Running the command mentioned on [this stackoverflow question], gives the following:运行 [this stackoverflow question] 中提到的命令,会得到以下结果:
The TensorFlow library wasn't compiled to use SSE4.1 instructions,
but these are available on your machine and could speed up CPU
computations.
2017-06-14 17:53:31.424793: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use SSE4.2 instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:53:31.424803: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:53:31.424812: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX2 instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:53:31.424820: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use FMA instructions, but these are
available on your machine and could speed up CPU computations.
2017-06-14 17:53:31.540959: I
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful
NUMA node read from SysFS had negative value (-1), but there must be
at least one NUMA node, so returning NUMA node zero
2017-06-14 17:53:31.541359: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0
with properties:
name: GeForce GTX 850M
major: 5 minor: 0 memoryClockRate (GHz) 0.9015
pciBusID 0000:0a:00.0
Total memory: 3.95GiB
Free memory: 128.12MiB
2017-06-14 17:53:31.541407: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2017-06-14 17:53:31.541420: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2017-06-14 17:53:31.541441: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M,
pci bus id: 0000:0a:00.0)
2017-06-14 17:53:31.547902: E
tensorflow/stream_executor/cuda/cuda_driver.cc:893] failed to
allocate 128.12M (134348800 bytes) from device:
CUDA_ERROR_OUT_OF_MEMORY
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce
GTX 850M, pci bus id: 0000:0a:00.0
2017-06-14 17:53:31.549482: I
tensorflow/core/common_runtime/direct_session.cc:257] Device
mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce
GTX 850M, pci bus id: 0000:0a:00.0
You are using the GPU version.您正在使用 GPU 版本。 You can list the available tensorflow devices with (also check this question):
您可以列出可用的 tensorflow 设备(另请检查此问题):
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices()) # list of DeviceAttributes
EDIT:编辑:
With tensorflow >= 1.4 you can run the following function:使用 tensorflow >= 1.4,您可以运行以下函数:
import tensorflow as tf
tf.test.is_gpu_available() # True/False
# Or only check for gpu's with cuda support
tf.test.is_gpu_available(cuda_only=True)
EDIT 2:编辑2:
The above function is deprecated in tensorflow > 2.1
.上述函数在
tensorflow > 2.1
已弃用。 Instead you should use the following function:相反,您应该使用以下函数:
import tensorflow as tf
tf.config.list_physical_devices('GPU')
NOTE:注意:
In your case both the cpu and gpu are available, if you use the cpu version of tensorflow the gpu will not be listed.在您的情况下,cpu 和 gpu 都可用,如果您使用 tensorflow 的 cpu 版本,则不会列出 gpu。 In your case, without setting your tensorflow device (
with tf.device("..")
), tensorflow will automatically pick your gpu!在您的情况下,无需设置您的 tensorflow 设备(
with tf.device("..")
),tensorflow 将自动选择您的 GPU!
In addition, your sudo pip3 list
clearly shows you are using tensorflow-gpu.此外,您的
sudo pip3 list
清楚地显示您正在使用 tensorflow-gpu。 If you would have the tensoflow cpu version the name would be something like tensorflow(1.1.0)
.如果您有 tensoflow cpu 版本,则名称将类似于
tensorflow(1.1.0)
。
Check this issue for information about the warnings.检查此问题以获取有关警告的信息。
A lot of things have to go right in order for Keras to use the GPU.为了让 Keras 使用 GPU,很多事情都必须做对。 Put this near the top of your jupyter notebook:
把它放在你的 jupyter notebook 顶部附近:
# confirm TensorFlow sees the GPU
from tensorflow.python.client import device_lib
assert 'GPU' in str(device_lib.list_local_devices())
# confirm Keras sees the GPU (for TensorFlow 1.X + Keras)
from keras import backend
assert len(backend.tensorflow_backend._get_available_gpus()) > 0
# confirm PyTorch sees the GPU
from torch import cuda
assert cuda.is_available()
assert cuda.device_count() > 0
print(cuda.get_device_name(cuda.current_device()))
NOTE: With the release of TensorFlow 2.0, Keras is now included as part of the TF API.注意:随着 TensorFlow 2.0 的发布,Keras 现在包含在 TF API 中。
To find out which devices your operations and tensors are assigned to, create the session with log_device_placement configuration option set to True.要找出您的操作和张量分配给哪些设备,请在 log_device_placement 配置选项设置为 True 的情况下创建会话。
# Creates a graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))
You should see the following output:您应该看到以下输出:
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Tesla K40c, pci bus
id: 0000:05:00.0
b: /job:localhost/replica:0/task:0/device:GPU:0
a: /job:localhost/replica:0/task:0/device:GPU:0
MatMul: /job:localhost/replica:0/task:0/device:GPU:0
[[ 22. 28.]
[ 49. 64.]]
For more details, please refer the link Using GPU with tensorflow有关更多详细信息,请参阅将GPU 与 tensorflow 一起使用的链接
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