This site is for helping you getting started with Docker and containers.
Docker is a platform for running applications inside containers. If you are familiar with python virtual environments, you can think about a container as a system-wide virtual environment; it provides what feels and looks like a clean ubuntu installation where your application can live and run, without the hassle and overhead of setting up a full-fletched virtual machine.
Containers are ubiquitous in the software industry, so I would recommend taking the time to read the official Docker documentation (https://docs.docker.com/get-started/). However, if you're in a hurry and just want to get a GPU-powered python environment up and running asap, the guide below should help you get started.
Step-by-step guide
1 - Making sure you have access to docker
By default (for security reasons), you should not have access to docker, even if you have access to the host machine. To check whether you have access, log into the host machine and run any docker command, e.g.:
If you get an error message complaining about access rights, it means that you are not part of the docker group on the machine. Ask your supervisor to relay a request to one of the engineers at IDI, who should be able to grant you the necessary privileges.
2 - The Dockerfile - building an image
Our end goal is to make a container from which we can run our own code. However, to achieve this, we need to create something called an image first. An image is a prototype of a container; it serves as a premade snapshot that can be used to spawn any number of containers. An image is created from something called a Dockerfile, which in its most basic form is just a list of prerequisites you want installed and commands you want to run before every startup. The example below should be a nice starting point.
The snippet above should be saved as a simple text file called Dockerfile. To build an image from it, I would recommend putting it in a dedicated folder, e.g.:
Then change working directory to the newly created folder and build the image:
Note the dot ‘.’ at the end of the command; don't forget it as it tells docker where to look for a Dockerfile. <image_name> is a user-specified name used to identify the created image. By convention, since all images created on the machine is stored in one place, it is common to include your username in the image name; e.g.: olanorm/testproject.
If the build command executed smoothly, your image should now be ready. You can verify this by running:
docker image list
It prints a list of all the available images on the current machine. You should find your newly created one at the top.
3 - Running the container
Once the image is successfully built we can run a container from it. The docker run command contains many different options that you might want to explore through the official reference. However, to keep things simple, here’s a command for running a container capable of providing a jupyter notebook that can be accessed from the outside:
-d means that the container will run in detached mode. I.e. it will run in the background while freeing up your current shell.
--rm means that the container will be cleaned up (everything inside it will be deleted) after it has exited. A container exists when its root process (which if you used the tensorflow:latest-gpu parent image is a jupyter notebook) terminates. Skip this flag if you want to keep the container around after it has exited, just remember to clean it up yourself so that you don’t clutter up the system.
-p maps a network port from the host machine to a port in the container. In the command above, we map YYYY, which should be an unused port number of the host machine, to port 8888 in the container, which is where the default jupyter process will listen.
--name sets a name for the container. It is not needed, since the container also gets a hash ID, but it is good practice to mark it with a human readable string as well. For instance, if you called your image olanorm/myproject, you can call the container olanorm_myproject (since slashes are not allowed in container names). This name can be used to access the container when running extra commands in it or when you wish to shut it down.
The final, positional argument is just the name of the image created in the previous section.
After executing the run command, docker will print the name of the container, or just a hash if you did not specify one. The container is now running and ready, you should be able to see it by executing:
docker ps
4 - Connecting to jupyter
Normally, when using jupyter notebook-like apps on a computer, we just run the server and access it through a web browser. However, since the server process is running on a different machine that most likely does not expose the serving port on the network, we need some extra magic to make it accessible. To achieve this, open a new SSH connection to the server that is running your docker container with the following command: