Docker 从守护进程获取事件时出错：EOF
Docker Error getting events from daemon: EOF
错误报告信息
描述
大家好，在完成 google codelab 之后，Codelabs 我在 Creating bottleneck at /tf_files/bottlenecks/roses/13231224664_4af5293a37.jpg.txt
之后收到错误 ERRO[4334] error getting events from daemon: EOF
更新：
我重新运行它，这出现了
ERRO[53469] error getting events from daemon: EOF
重现问题的步骤：
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
描述您预期的结果：
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，
这出现了
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
解决方案是增加 CPU 大小和 Docker 首选项中的 Ram。
错误报告信息
描述
大家好，在完成 google codelab 之后，Codelabs 我在 Creating bottleneck at /tf_files/bottlenecks/roses/13231224664_4af5293a37.jpg.txt
ERRO[4334] error getting events from daemon: EOF
更新：
我重新运行它，这出现了
ERRO[53469] error getting events from daemon: EOF
重现问题的步骤： 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
描述您预期的结果：
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，
这出现了
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
解决方案是增加 CPU 大小和 Docker 首选项中的 Ram。