Tensorflow 未在 jupyter notebook 的 GPU 上运行

Question

在 Ubuntu 上为 GTX 1080 ti 成功安装了 Cuda 和 cudnn，在 jupyter notebook 中运行一个简单的 TF 程序在运行 tensorflow-gpu==1.0 vs tensorflow==1.0 的 conda 环境中速度没有增加。

当我运行 nvidia-smi 时:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.66 Driver Version: 375.66 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 0000:01:00.0 On | N/A |
| 24% 45C P0 62W / 250W | 537MiB / 11171MiB | 0% Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1101 G /usr/lib/xorg/Xorg 310MiB |
| 0 1877 G compiz 219MiB |
| 0 3184 G /usr/lib/firefox/firefox 5MiB |
+-----------------------------------------------------------------------------+

我试过将“with tf.device("/gpu:0"):"放在矩阵乘法的前面，但它只是给我一个错误:

“InvalidArgumentError(回溯见上文):无法将设备分配给节点‘MatMul’:无法满足显式设备规范‘/device:GPU:0’，因为在此过程中没有注册符合该规范的设备；可用设备:/job:localhost/replica:0/task:0/cpu:0[[节点:MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/device:GPU:0"](Reshape, softmax/Variable/read)]]"

我知道 cudnn 已正确安装，因为我在终端中运行它时收到此消息。

import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally

我想这一定是 Jupiter notebook 的问题，是否存在兼容性问题？当我运行 TF session 时，我得到以下输出:

W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
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.
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.
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.
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.
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.
Device mapping: no known devices.

"""

Answer 1

我解决了这个问题。显然我在我的环境之外安装了 jupyter 和常规的 tensorflow。然而，我在我的环境中安装了 tensorflow-gpu。因此，当我运行 jupyter 时，它调用环境外的 tensorflow，而不是环境内安装的 tensorflow-gpu。