A Quick Tour

The best way to experience whisk is by creating your first project. The short self-guided tour below shows the power of whisk.

We use demo as the project name in the examples below. If you use a different project name, be sure to replace demo with the name of your project.

Create a project

Start by creating a project. Begin a terminal session and run the commands below:

$ pip install whisk
$ whisk create demo
$ cd demo
$ source venv/bin/activate

The commands above do the following:

• Install the whisk package

• Create a project named “demo”

• Generate the project directory structure

• Activate the project’s venv

Try the sample model

The project contains a naive model stub that returns the length of each inner list within a 2D array. The model is saved to the disk using pickle. We can use the model stub to demonstrate the full capabilities of a whisk project.

Since a whisk project uses a standard Python project code structure, there are multiple ways to invoke the model.

From Python code

The Model class resides in the src/demo/models/model.py file. The source code is installed as an editable Python package, so you can run the model from the Python console or from within a notebook cell:

from demo.models.model import Model
model = Model()
model.predict([[0,1],[2,3]])

The Model class loads the model from disk and Model.predict passes the data into the model. Try making a change to src/demo/models/model.py to see how model inference works.

From the command line

The project is pre-wired with Click, a Python package for creating beautiful command line interfaces. Invoke the model right from the terminal:

$ demo predict [[0,1],[2,3]]
[2, 2]

The CLI code is in src/demo/cli/main.py.

From the Flask App

There’s a ready-to-go Flask web service in the app/main.py file. Start the web service:

$ whisk app start

Make a request in another terminal session:

$ curl --location --request POST 'http://localhost:5000/predict' \
--header 'Content-Type: application/json' \
--data-raw '{"data":[[0, 1], [2, 3]]}'

Sharing the model

You can let others use your model by releasing a Python package and deploying a web service.

Release a Python Package for your model

Create a Python package containing your model. This lets anyone use your model that has Python installed on their computer:

$ whisk package dist
Python Package created in /Users/dlite/projects/whisk_examples/demo/dist:
demo-0.1.0.tar.gz

$ pip install dist/demo-0.1.0.tar.gz

After installing the package, another user can invoke the model via the CLI or by importing the demo module like we did earlier. You can modify the package metadata (author, description, etc) and list the package dependencies in setup.py.

Learn more about packaging a model in our packaging guide.

Deploy a web service

Deploy the model inference web service to Heroku (a free account works fine with this demo):

$ whisk app create demo-[INSERT YOUR NAME]

Learn more about deploying a model to Heroku.

What next?

Change the example model

To change the example model, head over to src/demo/models/model.py and make a small change to the predict method. After saving this, you’ll see all whisk functionality automatically update with this new logic.

Open the example notebook

The project includes an interactive notebook that shows how to save your trained model to disk, use the saved model to generate predictions, how to load Python functions and classes from the project’s src directory, and more. It’s the guide rails for your own ML project.

$ jupyter-notebook notebooks/getting_started.ipynb

Explore a real whisk project

The whisk-ml GitHub org contains example whisk projects. Checkout these examples and clone them locally. Since whisk makes reproducibility “just work”, in most cases you simply need to run whisk setup to use the models generated by the projects.