# Python DAG Runner
[](https://opensource.org/licenses/MIT)
Python DAG Runner is a Python library for running Directed Acyclic Graphs (DAGs) efficiently. It provides a simple and intuitive interface for defining and executing complex workflows. It uses networkx library to generate directed acyclic graph from the tasks and it's dependencies.
## Installation
You can install python-dag-runner library easily using pip:
```bash
pip install python-dag-runner
```
## Usage
You can easily define a dag in python-dag-runner using Dag class. Furthermore, task can be defined using Task class. You can easily define dependencies of tasks using the "|=" operator.
```python
from python_dag_runner import Dag, Task
# Create an executable function
def print_task_1():
"""A sample function"""
print("Task 1 called")
def print_task_1():
"""A sample function"""
print("Task 2 called")
# Create Task objects
task_1 = Task(name="task one", executable=print_task_1)
task_2 = Task(name="task two", executable=print_task_2)
# Set dependencies of task using bitwise or operator
task_2 |= {task_1}
# Create a DAG by providing name and sequence of tasks
dag = Dag("My first DAG", tasks=[task_1, task_2])
# Initiate execution of Dag using initialize method
dag.initiate()
```
## Example of complex dependencies
Multiple internal dependencies can be defined using dependency operator.
```python
task_2 |= {task_1}
task_3 |= {task_2}
task_4 |= {task_2}
task_5 |= {task_2}
task_6 |= {task_3, task_4, task_5}
task_7 |= {task_6}
task_8 |= {task_7}
task_9 |= {task_7}
```
The above steps result in the dependency graph as shown below
```mermaid
%%{init: {'theme': 'neutral'}}%%
stateDiagram
direction LR
task_one --> task_two
task_two --> task_three
task_two --> task_four
task_two --> task_five
task_three --> task_six
task_four --> task_six
task_five --> task_six
task_six --> task_seven
task_seven --> task_eight
task_seven --> task_nine
```
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"description": "\n# Python DAG Runner\n\n\n[](https://opensource.org/licenses/MIT)\n\n\nPython DAG Runner is a Python library for running Directed Acyclic Graphs (DAGs) efficiently. It provides a simple and intuitive interface for defining and executing complex workflows. It uses networkx library to generate directed acyclic graph from the tasks and it's dependencies.\n\n## Installation\nYou can install python-dag-runner library easily using pip:\n```bash\npip install python-dag-runner\n```\n\n## Usage\nYou can easily define a dag in python-dag-runner using Dag class. Furthermore, task can be defined using Task class. You can easily define dependencies of tasks using the \"|=\" operator.\n```python\nfrom python_dag_runner import Dag, Task\n\n# Create an executable function\ndef print_task_1():\n \"\"\"A sample function\"\"\"\n print(\"Task 1 called\")\n\ndef print_task_1():\n \"\"\"A sample function\"\"\"\n print(\"Task 2 called\")\n\n# Create Task objects\ntask_1 = Task(name=\"task one\", executable=print_task_1)\ntask_2 = Task(name=\"task two\", executable=print_task_2)\n\n# Set dependencies of task using bitwise or operator\ntask_2 |= {task_1}\n\n# Create a DAG by providing name and sequence of tasks\ndag = Dag(\"My first DAG\", tasks=[task_1, task_2])\n\n# Initiate execution of Dag using initialize method\ndag.initiate()\n```\n\n## Example of complex dependencies\nMultiple internal dependencies can be defined using dependency operator.\n```python\ntask_2 |= {task_1}\ntask_3 |= {task_2}\ntask_4 |= {task_2}\ntask_5 |= {task_2}\ntask_6 |= {task_3, task_4, task_5}\ntask_7 |= {task_6}\ntask_8 |= {task_7}\ntask_9 |= {task_7}\n```\n\nThe above steps result in the dependency graph as shown below\n```mermaid\n%%{init: {'theme': 'neutral'}}%%\nstateDiagram\ndirection LR\n task_one --> task_two\n task_two --> task_three\n task_two --> task_four\n task_two --> task_five\n task_three --> task_six\n task_four --> task_six\n task_five --> task_six\n task_six --> task_seven\n task_seven --> task_eight\n task_seven --> task_nine\n```\n",
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