TensorFlow running in Docker deployed on Ubuntu
This entry will provide a view of the different steps required to setup TensorFlow on a Ubuntu environment by running it within a docker container .
Whether your starting off on Machine learning with TensorFlow or your a veteran and you want to setup an infra with a docker container running Tensorflow then this article is for you
Update (17/3/2018): For doing the setup using GPU instances on AWS check my article here
Why we want to use docker ?
Prerequisites :
1. Ubuntu VM , am using one with 8Gb RAM , 100Gb SSD
2. Ubuntu CE installed , follow this link
3. Make sure you have TCP access to 8888, 9000 if running on AWS ( or other cloud platforms )
Once docker has been installed do make sure that non-root users can also execute the docker command by following instructions from docker site .
Running Tensorflow Container
At time of writing the current version of tensorflow is 1.5 so kick start just execute the following command which is document here .
docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
The best way I have found to manage by containers in a practical manner is through Portainer and you can install this on docker using the following command ( site here):
$docker volume create portainer_data
$docker run -d -p 9000:9000 -v /var/run/docker.sock:/var/run/docker.sock -v portainer_data:/data portainer/portainer
Now login to http://your_ip_adddress:9000/ to set a password for admin
Container shell access
If you want to connect to your running container's shell and assuming that this is called tensorflow as in our case just do :
docker exec -it tensorflow /bin/bash
Else you can also use Portainer as explained below .
Accessing Tensorflow
You should be able to log onto your docker containers directly through Portainer by clicking on the container name and clicking on console and the click connect on bash
This by default will give you access to /notebook directory :
I found it particularly useful to use this feature of portainer as it meant that directly from portainer web app you could access the bash of your running container .
Also keeping files under /notebook allows you to view them through your jupyter notebook instance.
Reverse Proxy (optional step)
Although not absolutely required I find it useful to be able to access to the tools all directly from port 80 you can install a reverse proxy in front of the Portainer and Tensorflow Jupyter notebook by installing nginx .
Installation is pretty straight forward please check instructions here :
Now assuming you are using the default ports as mentioned above (else modify as required ) you need to create a file with a *.conf e.g myreveseproxysettings.conf (or whatever suits you) , then you sudo cp (copy) this file to directory /etc/nginx/conf.d
Not that nginx main config includes configurations files which have a *.conf extension within the /etc/nginx/conf.d directory .
Remember to change My_IP_ADDRESS_OR_DOMAIN_NAME with your IP Address or Domain name .
==================myreveseproxysettings.conf=============================
server {
listen 80;
listen [::]:80;
server_name My_IP_ADDRESS_OR_DOMAIN_NAME;
# root /usr/share/nginx/html;
proxy_set_header Host $http_host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
location /portainer/ {
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_pass "http://localhost:9000/";
}
location / {
proxy_pass "http://localhost:8888/";
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header Host $http_host;
proxy_http_version 1.1;
proxy_redirect off;
proxy_buffering off;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_read_timeout 86400;
}
error_page 404 /404.html;
location = /40x.html {
}
error_page 500 502 503 504 /50x.html;
location = /50x.html {
}
}
=================================================
Following commands are useful:
1. Start nginx
sudo systemctl start nginx
2. Stop nginx
sudo systemctl stop nginx
3. Check status nginx
systemctl status nginx
To get out of the status message just do a " :" followed by a "q"
Whether your starting off on Machine learning with TensorFlow or your a veteran and you want to setup an infra with a docker container running Tensorflow then this article is for you
Update (17/3/2018): For doing the setup using GPU instances on AWS check my article here
Why we want to use docker ?
- We do not want to have our configurations for Tensorflow being messed up by other python versions and configs for other applications so we are isolating it
- Installing from Docker image is very practical and saves us a lot of time so we can focus directly on the our coding
Prerequisites :
1. Ubuntu VM , am using one with 8Gb RAM , 100Gb SSD
2. Ubuntu CE installed , follow this link
3. Make sure you have TCP access to 8888, 9000 if running on AWS ( or other cloud platforms )
Once docker has been installed do make sure that non-root users can also execute the docker command by following instructions from docker site .
Running Tensorflow Container
At time of writing the current version of tensorflow is 1.5 so kick start just execute the following command which is document here .
docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow
Note that there will be some output when the docker container runs with a token url please copy paste and keep somewhere .
Using Tensorflow
When launching the run command for the TensorFlow docker container above a url with a token looking something like this would be shown on your console:
http://your_ip_adddress:8888/?token=eXXXXXXXXXXXXXXXXXXXXXXXXX
Use this url directly to login to your jupyter notebook.
Now this is great but we need to also create a volume such as we can easily access files for example pulled from a git repository .
So you might want to remove the docker container that you just started in the last step and use docker-compose file below
Docker compose
Docker compose needs to be installed using the following instructions .
Here is a simple docker-compose file to be able to run the command to run a Tensorflow :
------------docker-compose-tensorflow.yml--------------------------------------
version: '3'
services:
tensorflow:
build: .
image: gcr.io/tensorflow/tensorflow
container_name: tensorflow
volumes:
- notebooks:/notebooks
ports:
- "8888:8888"
- "6006:6006"
environment:
- IMAGE_SIZE=224
- ARCHITECTURE=mobilenet_0.50_224
volumes:
notebooks:
-----------------------------------------------------------------------------------------
The command then to start the container is simple
docker-compose -f docker-compose-tensorflow up
and to stop the container :
docker-compose -f docker-compose-tensorflow down
Using Tensorflow
When launching the run command for the TensorFlow docker container above a url with a token looking something like this would be shown on your console:
http://your_ip_adddress:8888/?token=eXXXXXXXXXXXXXXXXXXXXXXXXX
Use this url directly to login to your jupyter notebook.
Now this is great but we need to also create a volume such as we can easily access files for example pulled from a git repository .
So you might want to remove the docker container that you just started in the last step and use docker-compose file below
Docker compose
Docker compose needs to be installed using the following instructions .
Here is a simple docker-compose file to be able to run the command to run a Tensorflow :
------------docker-compose-tensorflow.yml--------------------------------------
version: '3'
services:
tensorflow:
build: .
image: gcr.io/tensorflow/tensorflow
container_name: tensorflow
volumes:
- notebooks:/notebooks
ports:
- "8888:8888"
- "6006:6006"
environment:
- IMAGE_SIZE=224
- ARCHITECTURE=mobilenet_0.50_224
volumes:
notebooks:
-----------------------------------------------------------------------------------------
The command then to start the container is simple
docker-compose -f docker-compose-tensorflow up
and to stop the container :
docker-compose -f docker-compose-tensorflow down
Note that the docker-compose file contains a port mapping for 6006 which is used for Tensorboard and a volume mapping to notebooks .
The volume notebooks ensure that you persist your notebooks on subsequent up - down cycles of your container . Else you would lose all your contents on each shutdown of the container.
Managing containers The volume notebooks ensure that you persist your notebooks on subsequent up - down cycles of your container . Else you would lose all your contents on each shutdown of the container.
The best way I have found to manage by containers in a practical manner is through Portainer and you can install this on docker using the following command ( site here):
$docker volume create portainer_data
$docker run -d -p 9000:9000 -v /var/run/docker.sock:/var/run/docker.sock -v portainer_data:/data portainer/portainer
Now login to http://your_ip_adddress:9000/ to set a password for admin
Container shell access
If you want to connect to your running container's shell and assuming that this is called tensorflow as in our case just do :
docker exec -it tensorflow /bin/bash
Else you can also use Portainer as explained below .
Accessing Tensorflow
You should be able to log onto your docker containers directly through Portainer by clicking on the container name and clicking on console and the click connect on bash
This by default will give you access to /notebook directory :
I found it particularly useful to use this feature of portainer as it meant that directly from portainer web app you could access the bash of your running container .
Also keeping files under /notebook allows you to view them through your jupyter notebook instance.
Reverse Proxy (optional step)
Although not absolutely required I find it useful to be able to access to the tools all directly from port 80 you can install a reverse proxy in front of the Portainer and Tensorflow Jupyter notebook by installing nginx .
Installation is pretty straight forward please check instructions here :
- sudo apt-get update
- sudo apt-get install nginx
Now assuming you are using the default ports as mentioned above (else modify as required ) you need to create a file with a *.conf e.g myreveseproxysettings.conf (or whatever suits you) , then you sudo cp (copy) this file to directory /etc/nginx/conf.d
Not that nginx main config includes configurations files which have a *.conf extension within the /etc/nginx/conf.d directory .
Remember to change My_IP_ADDRESS_OR_DOMAIN_NAME with your IP Address or Domain name .
==================myreveseproxysettings.conf=============================
server {
listen 80;
listen [::]:80;
server_name My_IP_ADDRESS_OR_DOMAIN_NAME;
# root /usr/share/nginx/html;
proxy_set_header Host $http_host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
location /portainer/ {
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_pass "http://localhost:9000/";
}
location / {
proxy_pass "http://localhost:8888/";
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header Host $http_host;
proxy_http_version 1.1;
proxy_redirect off;
proxy_buffering off;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_read_timeout 86400;
}
error_page 404 /404.html;
location = /40x.html {
}
error_page 500 502 503 504 /50x.html;
location = /50x.html {
}
}
=================================================
Following commands are useful:
1. Start nginx
sudo systemctl start nginx
2. Stop nginx
sudo systemctl stop nginx
3. Check status nginx
systemctl status nginx
To get out of the status message just do a " :" followed by a "q"
