contrib/docs/templates/driver_builder.py.jinja2

from hamilton import dataflows, driver
{%- if use_executor %}
from hamilton.execution import executors
{% endif -%}
{%- if dynamic_import %}
# downloads into ~/.hamilton/dataflows and loads the module -- WARNING: ensure you know what code you're importing!
  {%- if is_user %}
{{ MODULE_NAME }} = dataflows.import_module("{{MODULE_NAME}}", "{{USER}}")
  {%- else %}
{{ MODULE_NAME }} = dataflows.import_module("{{MODULE_NAME}}")
  {%- endif %}
{%- else %}
  {%- if is_user %}
# Make sure you've done - `pip install sf-hamilton-contrib --upgrade`
from hamilton.contrib.user.{{USER}} import {{MODULE_NAME}}
  {%- else %}
# Make sure you've done - `pip install sf-hamilton-contrib --upgrade`
from hamilton.contrib.dagworks import {{MODULE_NAME}}
  {%- endif %}
{%- endif %}
{%- if use_executor %}
# Switch this out for ray, dask, etc. See docs for more info.
remote_executor = executors.MultiThreadingExecutor(max_tasks=20)
{% endif %}
dr = (
    driver.Builder()
    {%- if use_executor %}
    .enable_dynamic_execution(allow_experimental_mode=True)
    .with_remote_executor(remote_executor)
    {%- endif %}
    .with_config({})  # replace with configuration as appropriate
    .with_modules({{MODULE_NAME}})
    .build()
)
# If you have sf-hamilton[visualization] installed, you can see the dataflow graph
# In a notebook this will show an image, else pass in arguments to save to a file
# dr.display_all_functions()
# Execute the dataflow, specifying what you want back. Will return a dictionary.
result = dr.execute(
    [{{MODULE_NAME}}.CHANGE_ME, ...],  # this specifies what you want back
    inputs={...}  # pass in inputs as appropriate
)