Multiple Container Images in a Single Workflow¶
When working locally, it is typically preferable to install all requirements of your project locally (maybe in a single virtual environment). It gets complicated when you want to deploy your code to a remote environment. This is because most tasks in Flyte (function tasks) get deployed using a Docker Container.
A docker container allows you to create an expected environment for your tasks. Though it is completely possible to build a single Container Image that contains all your dependencies, it is complicated to achieve this in practice.
The following are reasons why it is complicated and not recommended:
All dependencies in one container increases the size of the container image
Some task executions like Spark, Sagemaker-based Training, and Deep Learning using GPUs need specific runtime configurations. For example,
Spark needs JavaVirtualMachine to be installed and Spark entrypoints to be set
NVIDIA drivers and other corresponding libraries need to be installed to use GPUs for deep learning. However, these are not required for a CPU.
Sagemaker expects the entrypoint to be specifically designed to accept its parameters
Building a singular image may increase the build time for the image itself
Flyte (Service) by default does not require that a Workflow is bound to a single Container Image. Flytekit offers a simple interface to easily alter the images that should be associated per task, yet keeping the local execution simple for the user.
flytekit.PythonFunctionTask type task or simply a task that is decorated with the
@task decorator, users can supply rules of how the container image should be bound. By default, flytekit will associate one container image with all tasks. This image is called the
To alter the image, users should use the
container_image parameter available in the
flytekit.task() decorator. Any one of the following is an acceptable
Image reference is specified, but the version is derived from the default images version
Both the FQN and the version are derived from the default image
- The images themselves are parameterizable in the config in the following format:
namerefers to the name of the image in the image configuration. The name
defaultis a reserved keyword and will automatically apply to the default image name for this repository.
fqnrefers to the fully qualified name of the image. For example it includes the repository and domain url of the image. E.g. docker.io/my_repo/xyz.
versionrefers to the tag of the image. E.g. latest, or python-3.8 etc. If the container_image is not specified then the default configured image for the project is used.
The default image (name + version) is always
Lets declare a task that uses an image that is derived from the defaults FQN and version
For another example where this is done please refer to Converting a Spark DataFrame to a Pandas DataFrame
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