| # Examples |
| |
| Here you'll find some very simple hello world type examples. |
| |
| If you have questions, or need help with these examples, |
| join us on [slack](https://join.slack.com/t/hamilton-opensource/shared_invite/zt-1bjs72asx-wcUTgH7q7QX1igiQ5bbdcg), and we'll try to help! |
| |
| ## Show casing scaling Pandas |
| The `hello_world` folder shows a simple example of how to create a Hamilton DAG and run it. |
| |
| Say you want to scale it? Well then, take a look at the `dask`, `ray`, and `spark` folders. |
| They each define a `hello_world` folder. Key thing to note, is that their `business_logic.py` files, |
| are in fact all identical, and symbolic links to the `my_functions.py` in our classic `hello_world` example. |
| The reason for this, is to show you, that you can infact scale Pandas, and also have your choice of framework |
| to run it on! |
| |
| For information on how to run Hamilton on `dask`, `ray`, `spark`, we invite you to read the READMEs in those |
| folders. |
| |
| ## A reusable scikit-learn model pipeline |
| Under `model_examples` you'll find a how you could apply Hamilton to model your ML workflow. |
| Check it out to get a sense for how Hamilton could make your ML pipelines reusable/general |
| components... |
