我用caffe-gpu和cuda 8创建了一个环境
conda create -n py27Cfe-gpu-p27h03f526a_2
conda install caffe-gpu=1.0=py27h03f526a_2
caffe-gpu 1.0 py27h03f526a_2
cudatoolkit 8.0 3
cudnn 6.0.21 cuda8.0_0
jupyter 1.0.0 py27_7
通过在“ conda install caffe-gpu”中选择特定的构建，我得到cuda 8。
我还使用cuda 9创建了caffe gpu环境
conda create -n p27cu9Cfegpu
conda install caffe-gpu=1.0=py27heda4471_3
caffe-gpu 1.0 py27heda4471_3
cudatoolkit 9.0 h13b8566_0
cudnn 7.3.1 cuda9.0_0
jupyter 1.0.0 py27_7
我同时测试了Google Deepdream jupyter笔记本。 cuda 8环境执行起来很轻松。 cuda 9环境在这一层令人窒息
I0505 12:29:44.577164 9839 net.cpp:744] Ignoring source layer loss2/loss
I0505 12:29:44.578850 9839 net.cpp:744] Ignoring source layer loss3/loss3
F0505 12:29:55.785749 9839 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory
*** Check failure stack trace: ***
我尝试将deploy.prototxt文件的第一个参数的批处理大小更改为1，如下所示：
name: "GoogleNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 1 dim: 3 dim: 224 dim: 224 } }
}
但是没有帮助。我意识到这两种环境之间还有许多其他变化。
other differences between the cuda9 environment and the cuda8 environment are:
(Cuda8 env lacks what has a minus but has what has a plus)
-backports_abc 0.5 py27_0
+backports_abc 0.5 py27h7b3c97b_0
-caffe-gpu 1.0 py27heda4471_3
+caffe-gpu 1.0 py27h03f526a_2
-cudatoolkit 9.0 h13b8566_0
-cudnn 7.3.1 cuda9.0_0
-cycler 0.10.0 py27_0
+cudatoolkit 8.0 3
+cudnn 6.0.21 cuda8.0_0
+cycler 0.10.0 py27hc7354d3_0
-h5py 2.7.1 py27h2697762_0
+h5py 2.8.0 py27h39dcb92_0
-hdf5 1.10.1 h9caa474_1
+hdf5 1.8.18 h6792536_1
-ipython_genutils 0.2.0 py27h89fb69b_0
+ipython_genutils 0.2.0 py27_0
-libprotobuf 3.5.2 h6f1eeef_0
+libprotobuf 3.4.1 h5b8497f_0
+linecache2 1.0.0 py27_0
-nbformat 4.4.0 py27hed7f2b2_0
+nbformat 4.4.0 py27_0
-opencv 3.3.1 py27hdcf4849_0
+opencv 3.3.1 py27h9bb06ff_1
-protobuf 3.5.2 py27hf484d3e_1
+protobuf 3.4.1 py27h2ba6a9c_0
traitlets 4.3.2 py27_0
-wcwidth 0.1.7 py27_0
+traceback2 1.4.0 py27_0
+traitlets 4.3.2 py27hd6ce930_0
+unittest2 1.1.0 py27_0
+wcwidth 0.1.7 py27h9e3e1ab_0
在每种情况下，脚本运行时都会出现另一个小错误，因此我不认为这是cuda9失败的原因
Network initialization done.
I0505 12:29:44.542949 9839 upgrade_proto.cpp:53] Attempting to upgrade input file specified using deprecated V1LayerParameter: ./modelZoo/bvlc_googlenet/bvlc_googlenet.caffemodel
I0505 12:29:44.575798 9839 upgrade_proto.cpp:61] Successfully upgraded file specified using deprecated V1LayerParameter
有人可以阐明这种记忆情况吗？ GPU是nvidia 1050Ti。 Ubuntu 18.04已从Nvidia安装了最新的驱动程序
nvidia-smi
Sun May 5 12:44:44 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.39 Driver Version: 418.39 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| 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 105... On | 00000000:01:00.0 On | N/A |
| 20% 32C P5 N/A / 75W | 406MiB / 4038MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1746 G /usr/lib/xorg/Xorg 26MiB |
| 0 2296 G /usr/bin/gnome-shell 48MiB |
| 0 3226 G /usr/lib/xorg/Xorg 195MiB |
| 0 3358 G /usr/bin/gnome-shell 132MiB |
+-----------------------------------------------------------------------------+