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, and we'll try to help!

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.

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...

Examples could also be executed through a docker image which you can build or pull yourself. Each example directory inside docker image contains a hamilton-env Python virtual environment. hamilton-env environment contains all the dependencies required to run the example.

NOTE: If you already have the container image you can skip to container initialization (step 3).

1. Change directory to examples.

cd hamilton/examples

2. Build the container image.

docker build --tag hamilton-example .

Docker build takes around 6m16.298s depending on the system configuration and network. Alternatively, you can pull the container image from https://hub.docker.com/r/skrawcz/sf-hamilton. docker pull skrawcz/sf-hamilton.

3. Starting the container. If you built it yourself:

docker run -it --rm --name hamilton-example hamilton-example

If you pulled it from dockerhub:

docker run -it --rm --name hamilton-example skrawcz/sf-hamilton

This will start the container and put you into a bash prompt.

4. Start running examples. E.g. running the hello_world example inside the container:

cd hamilton/examples/hello_world
source hamilton-env/bin/activate  # this will activate the right python environment
python my_script.py
deactivate # this will deactivate the virtual environment so you can activate another

To run another example:

1. change directory to it.
2. activate the environment (source hamilton-env/bin/activate).
3. run the code, e.g. python run.py.
4. deactivate the environment (deactivate). And then exit to quit out of the running docker container.