描述
大家好，在关注谷歌密码后，Codelabs我在ERRO[4334] error getting events from daemon: EOF
Creating bottleneck at /tf_files/bottlenecks/roses/13231224664_4af5293a37.jpg.txt
更新：
我重申它，这显示出来
ERRO[53469] error getting events from daemon: EOF
重现此问题的步骤： 1。 ``` python tensorflow / examples / image_retraining / retrain.py \
- bottleneck_dir = / tf_files / bottlenecks \ --how_many_training_steps 500 \ --model_dir = / tf_files / inception \ --output_graph = / tf_files / retrained_graph.pb \ --output_labels = / tf_files / retrained_labels.txt \ --image_dir / tf_files / flower_photos
```
描述您收到的结果：
ERRO[4334] error getting events from daemon: EOF
描述您期望的结果：
Finish the retraining
docker version的输出：
Docker version 1.13.1, build 092cba3
docker info的输出：
Containers: 6
Running: 0
Paused: 0
Stopped: 6
Images: 2
Server Version: 1.13.1
Storage Driver: overlay2
Backing Filesystem: extfs
Supports d_type: true
Native Overlay Diff: true
Logging Driver: json-file
Cgroup Driver: cgroupfs
Plugins:
Volume: local
Network: bridge host ipvlan macvlan null overlay
Swarm: inactive
Runtimes: runc
Default Runtime: runc
Init Binary: docker-init
containerd version: aa8187dbd3b7ad67d8e5e3a15115d3eef43a7ed1
runc version: 9df8b306d01f59d3a8029be411de015b7304dd8f
init version: 949e6fa
Security Options:
seccomp
Profile: default
Kernel Version: 4.9.8-moby
Operating System: Alpine Linux v3.5
OSType: linux
Architecture: x86_64
CPUs: 2
Total Memory: 1.952 GiB
Name: moby
ID: UNXQ:IPAT:2ZHG:3443:M7XI:M3FW:W7Q7:G4HV:IKKW:W5TU:72TI:SH3G
Docker Root Dir: /var/lib/docker
Debug Mode (client): false
Debug Mode (server): true
File Descriptors: 16
Goroutines: 27
System Time: 2017-02-21T14:43:50.071749826Z
EventsListeners: 1
No Proxy: *.local, 169.254/16
Registry: https://index.docker.io/v1/
Experimental: true
Insecure Registries:
127.0.0.0/8
Live Restore Enabled: false
其他环境详细信息（AWS，VirtualBox，物理等）：
OS X与python 2.7，
这显示出来了
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.
Thank you so much
答案 0 :(得分：2)
解决方案是在Docker首选项中增加CPU大小和Ram。