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Using the MMLSpark Docker Image

Quickstart: install and run the Docker image

Begin by installing Docker for your OS. Then, to get the MMLSpark image and run it, open a terminal (powershell/cmd on Windows) and run

docker run -it -p 8888:8888 microsoft/mmlspark

In your browser, go to http://localhost:8888/ — you'll see the Docker image EULA, and once you accept it, the Jupyter notebook interface will start. To skip this step, add -e ACCEPT_EULA=yes to the Docker command:

docker run -it -p 8888:8888 -e ACCEPT_EULA=y microsoft/mmlspark

You can now select one of the sample notebooks and run it, or create your own.

Note: The EULA is needed only for running the MMLSpark Docker image; the source code is released under the MIT license (see the LICENSE file).

Running a specific version

In the above, microsoft/mmlspark specifies the project and image name that you want to run. There is another component implicit here which is the tag (= version) that you want to use — specifying it explicitly looks like microsoft/mmlspark:0.14 for the 0.14 tag.

Leaving microsoft/mmlspark by itself has an implicit latest tag, so it is equivalent to microsoft/mmlspark:latest. The latest tag is identical to the most recent stable MMLSpark version. You can see the current mmlspark tags on our Docker Hub repository.

A more practical example

The previous section had a rather simplistic command. A more complete command that you will probably want to use can look as follows:

docker run -it --rm \
           -e ACCEPT_EULA=y \
           -p 127.0.0.1:80:8888 \
           -v ~/myfiles:/notebooks/myfiles \
           microsoft/mmlspark:0.14

In this example, backslashes are used to break things up for readability; you can enter it as one long like. Note that in powershell, the myfiles local path and line breaks looks a little different:

docker run -it --rm `
           -e ACCEPT_EULA=y `
           -p 127.0.0.1:80:8888 `
           -v C:\myfiles:/notebooks/myfiles `
           microsoft/mmlspark:0.14

Let's break this command and go over the meaning of each part:

  • -it

    This is a combination of -i and -t (which could also be specified as --interactive --tty). The combination of these two flags mean that the image is running interactively, which in this example means that you can see messages that the server prints out, and it also makes it possible to use Ctrl+C to shut down the Jupyter notebook server.

  • --rm

    When Docker runs any image, it creates a container to hold any additional filesystem data for files that were created or modified. If you ran the above quickstart command, you can see the container that is left behind with docker container list -a. You can reclaim such containers with docker container rm <id>, or reclaim all containers from stopped run with docker container prune, or even more generally, reclaim all unused Docker resources with docker system prune.

    Back to --rm: this flag tells Docker to discard the image when the image exits. You should be aware that this means that any data created while running the image is discarded when the run is done — but see the description of the -v flag below.

  • -e ACCEPT_EULA=y

    The -e flag is used to set environment variables in the running container. In this case, we use it to bypass the EULA check. Additional flags can be added for other variables, for example, you can add a -e MMLSPARK_JUPYTER_PORT=80 to change the port that the Jupyter server listens to.

  • -p 127.0.0.1:80:8888

    The Jupyter server in the MMLSpark image listens to port 8888 — but that is normally isolated from the actual network. Previously, we have used -p 8888:8888 to say that we want to map port 8888 (LHS) on our actual machine to port 8888 (RHS) in the container. One problem with this is that 8888 might be hard to remember, but a more serious problem is that your machine now serves the Jupyter interface to any one on your network.

    This more complete example resolves these issues: we replaced 8888:8888 with 80:8888 so HTTP port 80 goes to the container's running Jupyter (making just http://localhost/ work); and we also added a 127.0.0.1: prefix which means that this is available only from your own machine rather than being exposed.

    You can repeat this flag to forward additional ports similarly. For example, you can expose some of the Spark ports, e.g.,: -p 127.0.0.1:4040:4040.

  • -v ~/myfiles:/notebooks/myfiles

    As described above, we're using --rm to remove the container when the run exits. This is usually fine since pulling out files from these containers can be a little complicated. Instead, we use this flag to map a directory from your machine (the ~/myfiles on the LHS) to a directory that is available inside the running container. Any modifications to this directory that are done by the Docker image are performed directly on the actual directory.

    Note that the local directory follows the local filename conventions, so on Windows you'd use a Windows-looking path. On Windows you also need to share the drive you want to use in the Docker settings.

    The path on the right side is used inside the container and it is therefore a Linux path. The MMLSpark image runs Jupyter in the /notebooks directory, so it is a good place for making your files available conveniently.

    This flag can be used more than once, to make several directories available in the running container. Both paths must be absolute, so if you want to specify a path relatively, you can use something like -v $PWD/myfiles:/notebooks/myfiles.

    With such directory sharing in place, you can create/edit notebooks, and code in notebooks can use the shared directory for additional data, for example:

    data = spark.read.csv('myfiles/mydata.csv')
    ...
    model.write().overwrite().save('myfiles/myTrainedModel.mml')
  • microsoft/mmlspark:0.14

    Finally, this specifies an explicit version tag for the image that we want to run.

Running the container as a server

An alternative to running the Docker image interactively with -it is running it in a "detached" mode, as a server, using the -d (or --detach) flag. An additional flag that is useful for this is --name that gives a convenient label to the running image:

docker run -d --name my-mmlspark ...flags... microsoft/mmlspark

When running in this mode, you can use

  • docker stop my-mmlspark: to stop the image

  • docker start my-mmlspark: to start it again

  • docker logs my-mmlspark: to see the log output it produced

Running other commands in an active container

Another useful docker command is exec, which runs a command in the context of an existing active container. To use it, you specify the container name, and the command to run. For example, with a detached container started as above, you can use

docker exec -it my-mmlspark bash

to start a shell in the context of the server, roughly equivalent to starting a terminal in the Jupyter interface.

Other common Linux executables can be used, e.g.,

docker exec -it my-mmlspark top
docker exec my-mmlspark ps auxw

(Note that ps does not need -it since it's not an interactive command.)

These commands can be used with interactive containers too, and --name can be used to make them easy to target. If you don't use --name, Docker assigns a random name to the container; you can use docker ps to see it --- and you can also get the container IDs and use those instead of names.

Remember that the command given to docker exec is running in the context of the running container: you can only run executables that exist in the container, and the run is subject to the same resource restrictions (FS/network access, etc) as the container. The MMLSpark image is based on a rather basic Ubuntu installation (the ubuntu image from Docker Hub).

Running other Spark executables

docker run can accept another optional argument after the image name, specifying an alternative executable to run instead of the default launcher that fires up the Jupyter notebook server. This makes it possible to use the Spark environment directly in the container if you start it as:

docker run -it ...flags... microsoft/mmlspark bash

This starts the container with bash instead of Jupyter. This environment has all of the Spark executables available in its $PATH. You still need to specify the command-line flags that load the MMLSpark package, but there are convenient environment variables that hold the required package and repositories to use:

pyspark --repositories "$MML_M2REPOS" --packages "$MML_PACKAGE" --master "local[*]"

Many of the above listed flags are useful in this case too, such as mapping work directories with -v.

Updating the MMLSpark image

New releases of MMLSpark are published from time to time, and they include a new Docker image. As an image consumer, you will normlly not notice such new versions: docker run will download an image if a copy of it does not exist locally, but if it does, then docker run will blindly run it, without checking for new tags that were pushed.

This means that you need to explicitly tell Docker to check for a new version and pull it if one exists. You do this with the pull command:

docker pull microsoft/mmlspark

Since we didn't specify an explicit tag here, docker adds the implied :latest tag, and checks the available microsoft/mmlspark image with this tag on Docker Hub. When it finds a different image with this tag, it will fetch a copy to your machine, changing the image that an unqualified microsoft/mmlspark refers to.

Docker normally knows only about the tags that it fetched, so if you've always used microsoft/mmlspark to refer to the image without an explicit version tag, then you wouldn't have the version-tagged image too. Once the tag is updated, the previous version will still be in your system, only without any tag. Using docker images to list the images in your system will now show you two images for microsoft/mmlspark, one with a tag of latest and one with no tag, shown as <none>. Assuming that you don't have active containers (including detached ones), docker system prune will remove this untagged image, reclaiming the used space.

If you have used an explicit version tag, then it will still exist after a new pull, which means that you can continue using this version. Note that if you used an unqualified name first and then a version-tagged one, Docker will fetch both tags, only the second fetch is fast since the it points to contents that was already loaded. In this case, doing a pull when there's a new version will fetch the new latest tag and change its meaning to the newer version, but the older version will still be available under its own version tag.

Finally, if there are such version-tagged older versions that you want to get rid of, you can use docker images to check the list of installed images and their tags, and docker rmi <name>:<tag> to remove the unwanted ones.

A note about security

Executing code in a Docker container can be unsafe if the running user is root. For this reason, the MMLSpark image uses a proper username instead. If you still want to run as root (e.g., if you want to apt install an additional ubuntu package), then you should use --user root. This can be useful when combined with docker exec too do such administrative work while the image continues to run as usual.

Further reading

This text covers very briefly some of the useful things that you can do with the MMLSpark Docker image (and other images in general). You can find much more documentation online.