Cuda, CuDNN 已安装但 Tensorflow 无法使用 GPU
Cuda, CuDNN installed But Tensorflow can't use the GPU
我的系统是 Ubuntu EC2 上的 14.04。:
nvidia-smi
Sun Oct 2 13:35:28 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GRID K520 Off | 0000:00:03.0 Off | N/A |
| N/A 37C P0 35W / 125W | 11MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
ubuntu@ip-XXX-XX-XX-990:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17
我安装了 CUDA 7.5 和 CuDNN 5.1。
我在 /usr/local/local/lib64 中有正确的文件并包含文件夹。
Tensorflow 行什么也没给出:
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
Device mapping: no known devices.
I tensorflow/core/common_runtime/direct_session.cc:252] Device mapping:
>>>
请帮忙(非常感谢:))。
你是如何构建 tensorflow 的?
如果你是用 bazel 做的,你是否正确添加了 --config=cuda?
如果你用 pip 安装它,你是否正确选择了启用 gpu 的那个?
编辑:
你可以在这里看到如何使用 pip 安装:
https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html#pip-installation
你需要选择二进制兼容gpu的:
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp27-none-linux_x86_64.whl
# Mac OS X, GPU enabled, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py2-none-any.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp35-cp35m-linux_x86_64.whl
# Mac OS X, GPU enabled, Python 3.4 or 3.5:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py3-none-any.whl
然后安装tensorflow:
# Python 2
$ sudo pip install --upgrade $TF_BINARY_URL
# Python 3
$ sudo pip3 install --upgrade $TF_BINARY_URL
我的系统是 Ubuntu EC2 上的 14.04。:
nvidia-smi
Sun Oct 2 13:35:28 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GRID K520 Off | 0000:00:03.0 Off | N/A |
| N/A 37C P0 35W / 125W | 11MiB / 4095MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
ubuntu@ip-XXX-XX-XX-990:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17
我安装了 CUDA 7.5 和 CuDNN 5.1。
我在 /usr/local/local/lib64 中有正确的文件并包含文件夹。
Tensorflow 行什么也没给出:
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
Device mapping: no known devices.
I tensorflow/core/common_runtime/direct_session.cc:252] Device mapping:
>>>
请帮忙(非常感谢:))。
你是如何构建 tensorflow 的?
如果你是用 bazel 做的,你是否正确添加了 --config=cuda?
如果你用 pip 安装它,你是否正确选择了启用 gpu 的那个?
编辑:
你可以在这里看到如何使用 pip 安装: https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html#pip-installation
你需要选择二进制兼容gpu的:
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp27-none-linux_x86_64.whl
# Mac OS X, GPU enabled, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py2-none-any.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp35-cp35m-linux_x86_64.whl
# Mac OS X, GPU enabled, Python 3.4 or 3.5:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py3-none-any.whl
然后安装tensorflow:
# Python 2
$ sudo pip install --upgrade $TF_BINARY_URL
# Python 3
$ sudo pip3 install --upgrade $TF_BINARY_URL