Merge pull request #8 from zzstoatzz/dev

update documentation on prefect 1.0 usage

README.md

@@ -2,17 +2,17 @@
 
 ## Welcome!
 
-A collection of Prefect Tasks for memory profiling in Prefect 1.0
+A collection of Prefect Tasks for memory profiling in Prefect 1.0.
 
 ## Getting Started
 
 ### Python setup
 
 Requires an installation of Python 3.7+.
 
-We recommend using a Python virtual environment manager such as pipenv, conda or virtualenv.
+We recommend using a Python virtual environment manager such as `pipenv`, `conda` or `virtualenv`.
 
-These tasks are designed to work with Prefect 2.0. For more information about how to use Prefect, please refer to the [Prefect documentation](https://orion-docs.prefect.io/).
+For more information about how to use Prefect, please refer to the [Prefect documentation](https://docs.prefect.io/).
 
 ### Installation
 
@@ -25,19 +25,22 @@ pip install prefect-memory-profiling
 ### Write and run a flow
 
 ```python
-from prefect import flow
-from prefect_memory_profiling.tasks import (
-    goodbye_prefect_memory_profiling,
-    hello_prefect_memory_profiling,
-)
-
-
-@flow
-def example_flow():
-    hello_prefect_memory_profiling
-    goodbye_prefect_memory_profiling
-
-example_flow()
+from prefect import Flow
+from prefect_memory_profiling.tasks import profiled_task
+
+@profiled_task(name='My Profiled Task', stream=open('logfile.txt', 'a+'))
+def resource_intensive_task(n: int = 100):
+    a = [n**n for n in range(2*n)]
+    b = [n**n for n in range(4*n)]
+    c = [n**n for n in range(n**2)]
+
+    return sum(a + b + c)
+
+with Flow('Memory Profiled Flow') as flow:
+    resource_intensive_task()
+
+if __name__ == "__main__":
+    flow.run(run_on_schedule=False)
 ```
 
 ## Resources