This example revisits 'Episode 06-statistics-redux: Computing in the Cloud'. With Metaflow, you don't need to make any code changes to schedule your flow in the cloud. In this example, we will schedule the
stats.py workflow using the
step-functions create command-line argument. This instructs Metaflow to schedule your flow on AWS Step Functions without changing any code. You can execute your flow on AWS Step Functions by using the
step-functions trigger command-line argument. You can use a notebook to set up a simple dashboard to monitor all of your Metaflow flows.
You can find the tutorial code on GitHub.
step-functions create command-line option
step-functions trigger command-line option
Accessing data locally or remotely through the Metaflow Client API
Before playing this episode:
python -m pip install notebook
python -m pip install plotly
To play this episode:
python 02-statistics/stats.py step-functions create --max-workers 4
python 02-statistics/stats.py step-functions trigger
Open autopilot.ipynb in your remote Sagemaker notebook.