如何在AMD/ATI GPU上运行TensorFlow? [英] How to run TensorFlow on AMD/ATI GPU?
问题描述
在阅读了本教程之后 https://www.tensorflow.org/guide/using_gpu 我在这个简单的代码上检查了GPU会话
After reading this tutorial https://www.tensorflow.org/guide/using_gpu I checked GPU session on this simple code
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
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)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
x = sess.run(c)
print(x)
输出为
2018-08-07 18:44:59.019144:我 tensorflow/core/platform/cpu_feature_guard.cc:141]您的CPU支持 TensorFlow二进制文件未编译使用的指令:AVX2 FMA设备映射:没有已知的设备. 2018-08-07 18:44:59.019536:我 tensorflow/core/common_runtime/direct_session.cc:288]设备映射:
2018-08-07 18:44:59.019144: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA Device mapping: no known devices. 2018-08-07 18:44:59.019536: I tensorflow/core/common_runtime/direct_session.cc:288] Device mapping:
MatMul:(MatMul):/job:localhost/副本:0/任务:0/设备:CPU:0 2018-08-07 18:44:59.019902:我 tensorflow/core/common_runtime/placer.cc:886] MatMul: (MatMul)/作业:本地主机/副本:0/任务:0/设备:CPU:0 a:(常量): /作业:localhost/副本:0/任务:0/设备:CPU:0 2018-08-07 18:44:59.019926:我tensorflow/core/common_runtime/placer.cc:886] a: (常量)/作业:本地主机/副本:0/任务:0/设备:CPU:0 b:(常量): /作业:localhost/副本:0/任务:0/设备:CPU:0 2018-08-07 18:44:59.019934:我tensorflow/core/common_runtime/placer.cc:886] b: (const)/job:localhost/replica:0/task:0/device:CPU:0 [[22. 28.] [ 49. 64.]]
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:CPU:0 2018-08-07 18:44:59.019902: I tensorflow/core/common_runtime/placer.cc:886] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:CPU:0 a: (Const): /job:localhost/replica:0/task:0/device:CPU:0 2018-08-07 18:44:59.019926: I tensorflow/core/common_runtime/placer.cc:886] a: (Const)/job:localhost/replica:0/task:0/device:CPU:0 b: (Const): /job:localhost/replica:0/task:0/device:CPU:0 2018-08-07 18:44:59.019934: I tensorflow/core/common_runtime/placer.cc:886] b: (Const)/job:localhost/replica:0/task:0/device:CPU:0 [[ 22. 28.] [ 49. 64.]]
如您所见，GPU没有进行任何计算. 当我更改代码以使用GPU的配置和处理比例时:
As you see there is no calculation done by GPU. and when I changed the code to use GPU's configuration and process fraction:
conf = tf.ConfigProto()
conf.gpu_options.per_process_gpu_memory_fraction = 0.4
with tf.Session(config = conf) as sess:
x = sess.run(c)
print(x)
输出为
2018-08-07 18:52:22.681221:我 tensorflow/core/platform/cpu_feature_guard.cc:141]您的CPU支持 TensorFlow二进制文件未编译使用的指令:AVX2 FMA [[22. 28.] [49. 64.]]
2018-08-07 18:52:22.681221: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA [[ 22. 28.] [ 49. 64.]]
我该怎么做才能在GPU卡上运行会话?谢谢.
What can I do to run the session on GPU card? Thank you.
推荐答案
我相信TensorFlow-GPU仅支持NVIDIA CUDA计算能力> = 3.0 的GPU卡.
I believe TensorFlow-GPU only support GPU card with CUDA Compute Capability >= 3.0 of NVIDIA.
以下TensorFlow变体可用于安装:
The following TensorFlow variants are available for installation:
仅具有CPU支持的TensorFlow .如果您的系统没有NVIDIA®GPU，则必须安装此版本.通常，此版本的TensorFlow易于安装，因此，即使您具有NVIDIA GPU，我们也建议您先安装此版本.
TensorFlow with CPU support only. If your system does not have a NVIDIA® GPU, you must install this version. This version of TensorFlow is usually easier to install, so even if you have an NVIDIA GPU, we recommend installing this version first.
具有GPU支持的TensorFlow . TensorFlow程序通常在GPU而不是CPU上运行得更快.如果您运行对性能有要求的应用程序，并且系统具有满足先决条件的NVIDIA®GPU ，则应安装此版本.有关详细信息，请参见TensorFlow GPU支持.
TensorFlow with GPU support. TensorFlow programs usually run much faster on a GPU instead of a CPU. If you run performance-critical applications and your system has an NVIDIA® GPU that meets the prerequisites, you should install this version. See TensorFlow GPU support for details.
https://www.tensorflow.org/install/install_linux
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