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update: README
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README.md

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## Usage
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`airfly` expects the implementations are populated in a Python module(or package), the task dependencies are declared by assigning `upstreams` and `downstreams` attributes to each object. The task objects are actually wrappers for Airflow operators, when `airfly` walks through the entire module, all tasks are discovered and collected, the dependency tree and the `DAG` context are automatically built, with some `ast` helpers, `airfly` can wrap all these information, convert them into python code, and finally save them to `dag.py`.
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`airfly` expects the implementations are populated in a Python module(or package), the task dependencies are declared by assigning `upstream` and `downstream` attributes to each object. The task objects are actually wrappers for Airflow operators, when `airfly` walks through the entire module, all tasks are discovered and collected, the dependency tree and the `DAG` context are automatically built, with some `ast` helpers, `airfly` can wrap all these information, convert them into python code, and finally save them to `dag.py`.
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```
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main_dag
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├── __init__.py
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├── mod_a.py
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│ ├── task_a1
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│ └── task_a2
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│ └── upstreams: task_a1
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│ └── upstream: task_a1
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├── mod_b.py
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│ └── task_b1
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│ └── downstreams: task_a1, task_a2
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│ └── downstream: task_a1, task_a2
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├── sub_dag
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│ ├── __init__.py
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│ ├── mod_c.py
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### Declare task dependency
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Use `upstreams` and `downstreams` to specify task dependencies.
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Use `upstream` and `downstream` to specify task dependencies.
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```python
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# in demo.py
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bash_command="sleep 5",
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retries=3)
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upstreams = print_date
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downstreams = (templated,)
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upstream = print_date
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downstream = (templated,)
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```
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