# ⚠️ Notice: Limited Maintenance

This project is no longer actively maintained. While existing releases remain available, there are no planned updates, bug fixes, new features, or security patches. Users should be aware that vulnerabilities may not be addressed.

# [Workflow examples](#workflow-examples)

Workflows can be used to compose an ensemble of Pytorch models and Python functions and package them in a `war` file. A workflow is executed as a DAG where the nodes can be either Pytorch models packaged as `mar` files or function nodes specified in the workflow handler file. The DAG can be used to define both sequential or parallel pipelines.

As an example a sequential pipeline may look something like

```
input -> function1 -> model1 -> model2 -> function2 -> output
```

And a parallel pipeline may look something like

```
                          model1
                         /       \
input -> preprocessing ->         -> aggregate_func
                         \       /
                          model2
```

You can experiment with much more complicated workflows by configuring a `YAML` file. We've included 2 reference examples including a sequential pipeline and parallel pipeline.
* [Parallel workflow using nmt transformers example](nmt_transformers_pipeline/)
* [Sequential workflow using resnet for dog breed classification](dog_breed_classification/)

For a more detailed explanation of Workflows and what is currently supported please refer to the main [documentation](../../docs/workflows.md)