pages tagged Docker

Installing and configuring Docker

Sun, 02 Jul 2017 01:09:02 +0000

macOS

Homebrew

brew cask install docker
brew cask install docker-toolbox

Create a virtual machine

bash$ docker-machine create default

Start the virtual machine

Start the virtual machine default:

bash$ docker-machine start
bash$ docker-machine env
export DOCKER_TLS_VERIFY="1"
export DOCKER_HOST="tcp://192.168.99.100:2376"
export DOCKER_CERT_PATH="/Users/meng/.docker/machine/machines/default"
export DOCKER_MACHINE_NAME="default"
# Run this command to configure your shell:
# eval $(docker-machine env)
bash$ eval "$(docker-machine env default)"

Verify that the docker machine is working

bash$ docker run hello-world
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
b04784fba78d: Pull complete
Digest: sha256:f3b3b28a45160805bb16542c9531888519430e9e6d6ffc09d72261b0d26ff74f
Status: Downloaded newer image for hello-world:latest

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
 3. The Docker daemon created a new container from that image which runs the
    executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it
    to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:
 https://cloud.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/engine/userguide/

Run and test the 'tensorflow/tensorflow' Docker image

bash$ docker run -it tensorflow/tensorflow:1.1.0 bash
Unable to find image 'tensorflow/tensorflow:1.1.0' locally
1.1.0: Pulling from tensorflow/tensorflow
c62795f78da9: Pull complete
d4fceeeb758e: Pull complete
5c9125a401ae: Pull complete
0062f774e994: Pull complete
6b33fd031fac: Pull complete
353b34ef0a98: Pull complete
4f6aefc14b68: Pull complete
ce066374c6ca: Pull complete
c0755a91ab3a: Pull complete
f03279b52d25: Pull complete
d1c27c29b7e3: Pull complete
23807c5f4b3e: Pull complete
Digest: sha256:27bd43f1cf71c45eb48cb6e067b7cef47b168ac11c685d55a3495d27f0d59543
Status: Downloaded newer image for tensorflow/tensorflow:1.1.0
root@d000fef7e8dc:/notebooks#
root@d000fef7e8dc:/notebooks# python
Python 2.7.12 (default, Nov 19 2016, 06:48:10)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> 
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session() # It will print some warnings here.
2017-06-29 06:08:55.848559: 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.
2017-06-29 06:08:55.848626: 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.
2017-06-29 06:08:55.848663: 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.
>>> print(sess.run(hello))
Hello, TensorFlow!
>>>

List all virtual machines

$ docker-machine ls                                                
NAME      ACTIVE   DRIVER       STATE     URL   SWARM   DOCKER    ERRORS
default   -        virtualbox   Stopped                 Unknown

Delete a virtual machine

$ docker-machine rm default
About to remove default
WARNING: This action will delete both local reference and remote instance.
Are you sure? (y/n): y
Successfully removed default

References

https://stackoverflow.com/questions/21871479/docker-cant-connect-to-docker-daemon

Installing and configuring TensorFlow

Sat, 01 Jul 2017 21:23:24 +0000

Install TensorFlow using 'tensorflow/tensorflow' Docker image

Start the Docker virtual machine

Create a virtual machine if not already

bash$ docker-machine create default

Start the virtual machine default:

bash$ docker-machine start
Starting "default"...
(default) Check network to re-create if needed...
(default) Waiting for an IP...
Machine "default" was started.
Waiting for SSH to be available...
Detecting the provisioner...
Started machines may have new IP addresses. You may need to re-run the `docker-machine env` command.
>>> elapsed time 35s
bash$ docker-machine env
export DOCKER_TLS_VERIFY="1"
export DOCKER_HOST="tcp://192.168.99.100:2376"
export DOCKER_CERT_PATH="/Users/meng/.docker/machine/machines/default"
export DOCKER_MACHINE_NAME="default"
# Run this command to configure your shell:
# eval $(docker-machine env)
bash$ eval "$(docker-machine env default)"

Start 'tensorflow/tensorflow' Docker image

$ export MY_WORKSPACE_DIR='/Users/meng/workspace'
$ docker run -it \
--net=host \
--publish 6006:6006 \
--volume ${MY_WORKSPACE_DIR}/tensorflow_test:/tensorflow_test \
--workdir /tensorflow_test \
tensorflow/tensorflow:1.1.0 bash
root@30d79c2e5fc3:/tensorflow_test# pwd
/tensorflow_test

Start TensorBoard

root@30d79c2e5fc3:/tensorflow_test# tensorboard --logdir training_summaries &
[1] 12
root@30d79c2e5fc3:/tensorflow_test# Starting TensorBoard 47 at http://0.0.0.0:6006
(Press CTRL+C to quit)

root@30d79c2e5fc3:/tensorflow_test#

Open TensorBoard at

http://192.168.99.100:6006

in a Web browser, where the IP is the same as DOCKER_HOST in the output of docker-machine env.

Download training data

# curl -O http://download.tensorflow.org/example_images/flower_photos.tgz
# tar xzf flower_photos.tgz
# find flower_photos -type f | wc -l
3671
# cp -R flower_photos flower_photos_subset
# rm flower_photos_subset/*/[2-9]*
# find flower_photos_subset/ -type f | wc -l
1202

Download training script

# curl -O https://raw.githubusercontent.com/tensorflow/tensorflow/r1.1/tensorflow/examples/image_retraining/retrain.py

Training

# python retrain.py \
      --bottleneck_dir=bottlenecks \
      --how_many_training_steps=500 \
      --model_dir=inception \
      --summaries_dir=training_summaries/basic \
      --output_graph=retrained_graph.pb \
      --output_labels=retrained_labels.txt \
      --image_dir=flower_photos_subset

Python script to classify new images of flowers

Create a new file label_image.py:

import os, sys

import tensorflow as tf

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# change this as you see fit
image_path = sys.argv[1]

# Read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()

# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line 
                   in tf.gfile.GFile("retrained_labels.txt")]

# Unpersists graph from file
with tf.gfile.FastGFile("retrained_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')

with tf.Session() as sess:
    # Feed the image_data as input to the graph and get first prediction
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')

    predictions = sess.run(softmax_tensor, \
             {'DecodeJpeg/contents:0': image_data})

    # Sort to show labels of first prediction in order of confidence
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]

    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
        print('%s (score = %.5f)' % (human_string, score))

Classify a new image:

# python label_image.py flower_photos/daisy/99306615_739eb94b9e_m.jpg
daisy (score = 0.47996)
dandelion (score = 0.24667)
sunflowers (score = 0.24508)
tulips (score = 0.02018)
roses (score = 0.00811)

Exit the Docker image and stop the Docker virtual machine

Ctrl+D to exit Docker image and then run

$ docker-machine stop

References

https://codelabs.developers.google.com/codelabs/tensorflow-for-poets