Running in a local Python environment ​

To quickly try out the code, you can run it in your local Python environment using pyflyte run:

shell
[~/wine-classification]:wine-classification
$ pyflyte run workflows/wine_classification_example.py \
              training_workflow \
              --hyperparameters '{"C": 0.1}'
[~/wine-classification]:wine-classification
$ pyflyte run workflows/wine_classification_example.py \
              training_workflow \
              --hyperparameters '{"C": 0.1}'

Here you are invoking pyflyte run and passing the name of the Python file and the name of the workflow within that file that you want to run. In addition, you are passing the named parameter hyperparameters and its value.

You should see the following output:

shell
LogisticRegression(C=0.1, max_iter=3000)
LogisticRegression(C=0.1, max_iter=3000)

This output above tells you that your workflow was executed successfully, but little else. To see actual results, we will need to run the workflow in a local Kubernetes cluster (see below).

Passing parameters ​

pyflyte run enables you to execute a specific workflow using the syntax:

shell
pyflyte run <path/to/script.py> <workflow_or_task_function_name>
pyflyte run <path/to/script.py> <workflow_or_task_function_name>

Keyword arguments can be supplied to pyflyte run by passing them in like this:

shell
--<keyword> <value>
--<keyword> <value>

For example, if file foo.py has a workflow bar with a named parameter baz , you would invoke it like this:

shell
pyflyte run foo.py bar --baz 'qux'
pyflyte run foo.py bar --baz 'qux'

Here the the value qux is passed for the parameter baz.

With snake_case argument names, you have to convert them to kebab-case. For example,

shell
pyflyte run foo.py bar --baz-luhrmann 'qux'
pyflyte run foo.py bar --baz-luhrmann 'qux'

would pass the value qux for the parameter baz_luhrmann.

Why pyflyte run rather than python? ​

Notice that example.py has a main guard at the end of the script.

python
if __name__ == "__main__":
    training_workflow(hyperparameters={"C": 0.1})
if __name__ == "__main__":
    training_workflow(hyperparameters={"C": 0.1})

This lets you run it with python example.py, though you have to hard code your arguments. It becomes even more verbose if you want to pass in arguments:

python
if __name__ == "__main__":
    import json
    from argparse import ArgumentParser

    parser = ArgumentParser()
    parser.add_argument("--hyperparameters", type=json.loads)
    ...  # add the other options

    args = parser.parse_args()
    training_workflow(hyperparameters=args.hyperparameters)Py
if __name__ == "__main__":
    import json
    from argparse import ArgumentParser

    parser = ArgumentParser()
    parser.add_argument("--hyperparameters", type=json.loads)
    ...  # add the other options

    args = parser.parse_args()
    training_workflow(hyperparameters=args.hyperparameters)Py

pyflyte run lets you dispense with this verbosity and run the workflow with the desired arguments conveniently.