Flyte enables production-grade orchestration for machine learning workflows and data processing created to accelerate local workflows to production.

Flyte

<https://github.com/flyteorg/flytekit-python-template/pull/34|#34 split MNIST training, added multiple entrypoints>
Pull request opened by <https://github.com/zeryx|zeryx>
As Dynamic workflows are not able to be registered using the default IO given in the dynamic function description.

To ensure we can test GPU accelerated workflows, I've split the dynamic workflow into two separate workflows; one for CPU acceleration and another for GPU acceleration.
<https://github.com/flyteorg/flytekit-python-template|flyteorg/flytekit-python-template>
GitHub Actions: integration_tests
:white_check_mark: 3 other checks have passed
3/4 successful checks

<https://github.com/flyteorg/flytekit-python-template/pull/34|#34 split MNIST training, added multiple entrypoints>
Pull request merged by <https://github.com/zeryx|zeryx>
<https://github.com/flyteorg/flytekit-python-template|flyteorg/flytekit-python-template>