How to Run Google TensorFlow on Alibaba Cloud

Hosting TensorFlow using Docker on Alibaba Cloud

What Are We Going to Be Building?

1. Install Docker on Ubuntu 16.04

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release-cs) stable"
sudo apt-get update
apt-cache policy docker-ce
Output of apt-cache policy docker-ce
docker-ce:
Installed: (none)
Candidate: 17.03.1~ce-0~ubuntu-xenial
Version table:
17.03.1~ce-0~ubuntu-xenial 500
500 https://download.docker.com/linux/ubuntu xenial/stable amd64 Packages
17.03.0~ce-0~ubuntu-xenial 500
500 https://download.docker.com/linux/ubuntu xenial/stable amd64 Packages
sudo apt-get install -y docker-ce
sudo systemctl status docker
docker.service - Docker Application Container Engine
Loaded: loaded (/lib/systemd/system/docker.service; enabled; vendor preset: enabled)
Active: active (running) since Sun 2016-05-01 06:53:52 CDT; 1 weeks 3 days ago
Docs: https://docs.docker.com
Main PID: 749 (docker)

2. Setup TensorFlow Docker Image on Alibaba Cloud

$ docker pull nikeshgogia/tensorflow:1.0
$ docker images
root@iZt4neefbpoojkuy4fdvqzZ:~# docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
nikeshgogia/tensorflow 1.0 ed0ee8133d06 26 minutes ago 1.62GB

3. Starting and Entering TensorFlow Container

docker run -it --publish 6006:6006 -p 80:5000 --volume ${HOME}/tf_files:/tf_files --workdir
/tf_files nikeshgogia/tensorflow:1.0 bash
root@17e62932b5b5:/tf_files#

4. Training and Testing Your Model

root@17e62932b5b5:/tf_files# ls -l
total 16
drwxr-xr-x 4 root root 4096 Nov 16 03:37 lights
drwxr-xr-x 2 root root 4096 Nov 16 03:37 testimages
drwxr-xr-x 6 root root 4096 Nov 16 03:37 tf
IMAGE_SIZE=224
ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"
python -m scripts.retrain \
--bottleneck_dir=/trained_files/bottlenecks \
--how_many_training_steps=500 \
--model_dir=/trained_files/models/ \
--summaries_dir=/trained_files/training_summaries/"${ARCHITECTURE}" \
--output_graph=/trained_files/retrained_graph.pb \
--output_labels=/trained_files/retrained_labels.txt \
--architecture="${ARCHITECTURE}" \
--image_dir=/tf_files/lights
python -m scripts.label_image \
--graph=/trained_files/retrained_graph.pb \
--image=/tf_files/testimages/l1.jpeg
light on 0.999999
light off 1.37755e-06

Conclusion

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