Docker Utilities/Patterns for Python Projects
Project description
Docker Utilities/Patterns for Python Projects
dockerutils
defines a set of patterns and utilities to facilitate use of docker with a python app or library
facilitating:
- seperating development/test dependencies out of production container, e.g. production container vs. dev/test container
- seperating data science notebook container from execution container
- environment experimentation
- cases in which you want to "freeze" any external dependencies in one container and use that as a base for containers that are dependent solely on the project
Conventions
-
Create a docker directory tree at the root of the project
In this directory tree there should be one sub-directory for each unique docker container type that is desired. Each of these sub-directories would contain the
Dockerfile
that will be used to create the image as well as any source specific to that image. -
Use versioneer for project versioning (Optional).
As part of the image build, a file,
_version.py.bld
, will be generated and placed at the project root. ADockerfile
can add that file to the image on creation to prevent the need for including the .git directory tree in the container context (usually quite expensive). -
Create a docker/base directory to make use of built in external dependency isolation (optional)
This capability supports environments where a docker build isn't able to access external dependencies (Docker Hub, pypi, etc.), for instance a server in a "locked-down" environment. A base image can be defined to isolate any dependencies that are required. That image can then be built and
transfer-image
used to transfer the base image to the target environment.Subsequent images can be built based off of that image that are "self-contained" (relying only on source from the project). The remote docker api can then be used to quickly iterate only requiring the more cumbersome transfer-image to be used when external dependencies change.
-
Building and running images controlled through configuration (<project_dir>/docker/dockerutils.cfg)
Includes setting most docker parameters, i.e. volume mounts, ports, networks, commands, etc. with replacement varilable support for things like user, project root, etc.
Command-line Interface
Image cli
build-image
takes the name of one of the sub-directories in the docker
directory and builds the
image defined therein. The image is named <project>-<subdir>:<user>
run-image
takes the name of one of the sub-directories (or one of the synthetic images defined in dockerutils.cfg
),
together with any of the configuration for that image defined in dockerutils.cfg
and starts a docker container
transfer-image
takes a docker image name and uses docker save
and load
to transfer the image to a remote host
publish-image
takes the name of one of the sub-directories in the docker
directory and pushes the image built by
the docker file to the defined repository (AWS or Docker)
Notebook cli
run-notebook
will start a docker container using either the notebook container found in the docker/notebook
directory
if it exists, or rappdw/docker-ds otherwise. The current directory will be mounted
into the container for use in the Juypter notebook environment. There are a couple of environment variable to be aware of
with this command:
- DOCKER_DS_DONT_PULL - if set, the version of rappdw/docker-ds currently available will be used rather than pulling the latest version from docker hub.
- DOCKER_DS_DIFFS - if set,
Dock cli
A "dock" is a remote system that has a docker daemon running and configured in a secure fashion (generally an EC2
instance). You can "dock" your terminal to a remote instance and any docker commands, including image and notebook cli
above will be run against the remote docker server. Once a "dock" is created, you can dock your terminal by issuing
the command source dock <server IP or moniker>
create-dock
will start an ec2 instance that can be used for remote docking. This instance is configured to provide
secure interaction with the docker server, as well as to support GPU utliziation (-g
option with run-image
)
destroy-dock
will terminate a remote dock instance and delete any local configuration files
stop-dock
will change the instances state of a remote dock to stopped
start-dock
will change the instance state of a remote dock to running
ssh-dock
opens a terminal on the remote dock with ssh
dockerutils.cfg
Format
Configuration in docker/dockerutils.cfg
is used to configure behavior of the dockerutils
scripts.
Image Section
The dockerutils.cfg
file format allows for configuration sections named corresponding to the sub-directories in the
docker directory tree. Each of these sections may contain one of the following:
environment
- in the form of (-e VAR=value
)+ to pass environment into the containerinteractive
- either -e or -itgpu
- conforms to NVIDIA Docker 2.0 specification, e.g.--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all
,run-image -g
will add this automaticallynetwork
- the name of the network for the containervolumes
- in the form of (-v <host-dir>:<container-dir>
)+ or (--mount ...
)+ or bothports
- in the form of (-p <host-port>:<container-port>
)+cmd
- any valid DockerCMD
specificationpull_FROM_on_force
- defaults to False, if True, add --pull to build command when force building image (or base image)image_repo
- the repository to publish the image topublication_tag
- the tag for publication (full image name + tag)pre_build_script
- A shell command or script to run before a docker build is issuedpost_build_script
- A shell command or script to run after a docker build has been compeleted (successfully)
Synthetic Images
Additionally, "synthetic" images can be specified by adding a run-image
section with a synthetic_images
definition
that contains a list of "synthetic" images. Each of these may also have a named section as defined for the docker
sub-directories, but must also contain a name
value that resolves to one of the docker sub-directories. For example:
[run_image]
synthetic_images=shell
[shell]
name=dev
...
Configuration-only Images
If there is a docker container that does what you want already, you can create a configuration-only image by
specifying name
, tag
and prefix=False
in the configuration section for the image. For example the base notebook
image rappdw/docker-ds
is often sufficient for running a Jupyter notebook against your code, as it auto detects a
setup.py
upon container start and installs the module into the notebook environment.
Image Tagging
The default tag for any image created/run/etc. is the user name in the host environment when running the
utility. This can be overriden by adding a tag
value to the desired section. For example:
[dev]
tag=experiment.2017.12.16
...
Volume Replacement Variables
The volume specification may contain either environment variables ($name
and ${name}
formats) as well as specific
variable replacement designations of the form {var}
. The supported variables include:
project_root
- will be replaced with the root directory name of the projectuser
- will be replaced with the user name of the user running the commandproject
- replace with project namge
Image Push Replacement Variables
The publication_tag
may contain either environment variables ($name
and ${name}
formats) as well as specific
variable replacement designations of the form {var}
. The supported variables include:
account
- AWS account designationregion
- AWS regionimage
- Image nametag
- Image taguser
- will be replaced with the user name of the user running the command
Publish to AWS
In order to publish to AWS, the repository will be the image name and the following should be configured for images that are published to AWS
image_repo=aws
publication_tag={account}.dkr.ecr.{region}.amazonaws.com/{image}:{tag}"
Patterns
Running your code in container, making live modifications outside container in your editor of choice
If you're like me, you have a whole set of tools in your host environment that you use to work with your project.
One of the disadvantages of working with Docker can be the difficulty of transplanting those tools into the container
environment. Perhaps there is a way to have your cake and eat it too!
The dev
example does a reasonable job of doing just this.
With this pattern, you create a Dockerfile
that has everything in the image except for your project source. An
empty WORKDIR
is created and then ENTRYPOINT
even does a pip install -e
of the contents of the empty WORKDIR
.
We get the desired results by mounting the source project directory into the container's WORKDIR
(see dev
section
of docker/dockertuils.cfg
).
With this pattern you can run tests, experiment, etc. in container, make changes to the project in your host environment toolset and immediately observe the changes that were made.
Working with a server in a locked-down environment
You may find yourself in a situation in which you need to work with a server hosting Docker in an environment that has
limited access to the "outside world". This pattern can be used to capture all external dependencies in a base
image that is built in an environment that is open, use transfer-image
to send this base image to the server and
then utilize a derived image dependent just on project sources and the base image to iterate without requiring open
access on the server.
Adding test frameworks, code analysis tools, etc. to a container for testing/validation
Tensorflow for both CPU and GPU in the same container
It is sometimes useful to try both the CPU and GPU versions of Tensorflow. The example in docker/tensorflow
provides
a pattern to do so. All tensorflow depdendencies are installed into global python site-packages. Then virtual
environments are created for both cpu and gpu versions and the appropriate version of tensorflow is install into
the respective virtual environments.
The run-image
script makes use of NVIDIA Docker 2.0 being installed on the host os. When run with the -g
option,
the NVIDIA runtime will be used to make GPUs available in container.
Upon container startup, the appropriate virtual environment (cpu or gpu) will be activated dependant upon the
-g
option.
To switch between virtual envrionments utilized the symbolic links, /cpu-env
and /gpu-env
, e.g.
source /cpu-env
.
Versioneer support
If you aren't using versioneer to version your Python projects, you
should be. Versioneer utilizes .git to determine the project version. Having correct version information in container
is desireable in many cases, but pushing .git into container usually isn't desireable. To capture version information
for container, the genversion
utility is available and uses capabilities of versioneer to generate a _version.py.bld
file in the project root. This file is available for Dockerfile
to add/overwrite the version.py file within the
Docker image. An example of this is found in the cicd sub-directory.
.dockerignore
In order to minimize the context sent to Docker to build images, please see examples in .dockerignore
in this
repository.
A Note on Implementation
We selected to use the Docker command line from python scripts rather than the Docker API available to Python as it integrates better into my development loop. As each CLI prints the Docker command it's using, if it does something unexpected, it's each to copy and paste the command used, modify it and be on your way without having to debug through CLI utility code, allowing bugs/additions to the CLI to be addressed at a later point outside the context of a project development loop.
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