# Pure Python

Flyte 2 introduces a new way of writing workflows that is based on pure Python, removing the constraints of a domain-specific language (DSL) and enabling full use of Python's capabilities.

## From `@workflow` DSL to pure Python

| Flyte 1 | Flyte 2 |
| --- | --- |
| `@workflow`-decorated functions are constrained to a subset of Python for defining a static directed acyclic graph (DAG) of tasks. | **No more `@workflow` decorator**: Everything is a `@env.task`, so your top-level “workflow” is simply a task that calls other tasks. |
| `@task`-decorated functions could leverage the full power of Python, but only within individual container executions. | `@env.task`s can call other `@env.task`s and be used to construct workflows with dynamic structures using loops, conditionals, try/except, and any Python construct anywhere. |
| Workflows were compiled into static DAGs at registration time, with tasks as the nodes and the DSL defining the structure. | Workflows are simply tasks that call other tasks. Compile-time safety will be available in the future as `compiled_task`. |

### Flyte 1

```python
import flytekit

image = flytekit.ImageSpec(
    name="hello-world-image",
    packages=["requests"],
)

@flytekit.task(container_image=image)
def mean(data: list[float]) -> float:
    return sum(list) / len(list)

@flytekit.workflow
def main(data: list[float]) -> float:
    output = mean(data)

    # ❌ performing trivial operations in a workflow is not allowed
    # output = output / 100

    # ❌ if/else is not allowed
    # if output < 0:
    #     raise ValueError("Output cannot be negative")

    return output
```

*Source: https://github.com/unionai/unionai-examples/blob/main/v2/user-guide/flyte-2/pure-python/flyte_1.py*

### Flyte 2

```
import flyte

env = flyte.TaskEnvironment(
    "hello_world",
    image=flyte.Image.from_debian_base().with_pip_packages("requests"),
)

@env.task
def mean(data: list[float]) -> float:
    return sum(data) / len(data)

@env.task
def main(data: list[float]) -> float:
    output = mean(data)

    # ✅ performing trivial operations in a workflow is allowed
    output = output / 100

    # ✅ if/else is allowed
    if output < 0:
        raise ValueError("Output cannot be negative")

    return output
```

*Source: https://github.com/unionai/unionai-examples/blob/main/v2/user-guide/flyte-2/pure-python/flyte_2.py*

These fundamental changes bring several transformative benefits:

- **Flexibility**: Harness the complete Python language for workflow definition, including all control flow constructs previously forbidden in workflows.
- **Dynamic workflows**: Create workflows that adapt to runtime conditions, handle variable data structures, and make decisions based on intermediate results.
- **Natural error handling**: Use standard Python `try`/`except` patterns throughout your workflows, making them more robust and easier to debug.
- **Intuitive composability**: Build complex workflows by naturally composing Python functions, following familiar patterns that any Python developer understands.

---
**Source**: https://github.com/unionai/unionai-docs/blob/main/content/user-guide/flyte-2/pure-python.md
**HTML**: https://www.union.ai/docs/v2/union/user-guide/flyte-2/pure-python/