gdev | Class GPU Resource Management : Device Drivers | GPU library

As I found out the following MRE reproduces each time it is executed the same error:

I have retrained the inception model according to instructions from the website

https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#4

But I cant find out why in its step 5 I am unable to classify a image by using label_image.py .After following the steps in 5 part. and finally using

python /tf_files/label_image.py /tf_files/flower_photos/daisy/21652746_cc379e0eea_m.jpg in docker, I get error message in following image:

Error message

I have a record in a table like:

I am trying to build a software that will allow remote PC control.My code so far has been able to share the server screen to the client when both the client and the server are on the same PC but it shows the "Connection refused" error when I try to keep the clients and servers in different laptops.

Description

Hello everyone, after following the google codelabs, Codelabs I have received an error ERRO[4334] error getting events from daemon: EOF after Creating bottleneck at /tf_files/bottlenecks/roses/13231224664_4af5293a37.jpg.txt

Update: I reran it and this shows up ERRO[53469] error getting events from daemon: EOF

Steps to reproduce the issue: 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

```

Describe the results you received: ERRO[4334] error getting events from daemon: EOF

Describe the results you expected: Finish the retraining

Output of docker version:

Docker version 1.13.1, build 092cba3

Output of docker info:

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

Additional environment details (AWS, VirtualBox, physical, etc.): OS X with python 2.7, and this shows up 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