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Quickstart

First, you will need to set up a Python virtual enviornment and install the Chassis SDK. Include [quickstart] to install the extra dependencies required to use the quickstart model.

pip install "chassisml[quickstart]"

Build Container

With the SDK installed, you can now begin to build your first model container. Chassis's quickstart mode provides a pre-trained scikit-learn digits classification model as a simple import, so you do not need to bring your own model.

Container Build

Paste the below code snippet into your Python file (Jupyter notebook or script in other preferred IDE) to build a model container from the Chassis quickstart scikit-learn model.

import chassis.guides as guides
from chassis.builder import DockerBuilder

# Import a pre-trained scikit-learn digit classification model with pre-defined container metadata
model = guides.QuickstartDigitsClassifier
# Test the model with a picture of a handwritten "5"
results = model.test(guides.DigitsSampleData)
# View test results
print(results)

# Configure container builder option as Docker
builder = DockerBuilder(model)
# Build container for the model locally
job_results = builder.build_image("my-first-chassis-model")
# View container info after the build completes
print(job_results)

Execute this snippet to kick off the local Docker build.

This local container build should take just under a minute. The job_results of a successful build will display the details of your new container (note: the "Image ID" digest will be different for each build):

Generating Dockerfile...Done!
Copying libraries...Done!
Writing metadata...Done!
Compiling pip requirements...Done!
Copying files...Done!
Starting Docker build...Done!
Image ID: sha256:d222014ffe7bacd27382fb00cb8686321e738d7c80d65f0290f4c303459d3d65
Image Tags: ['my-first-chassis-model:latest']
Cleaning local context
Completed:       True
Success:         True
Image Tag:       my-first-chassis-model:latest

Congratulations! You just built your first ML container from a scikit-learn digits classification model. Next, run a sample inference through this container with Chassis's OMI inference client.

Run Inference

To quickly test your new model container, you can leverage Chassis's OMIClient.test_container convenience function. When executed, this function will spin up your container, run inference on sample data, and return the prediction results.

Open a Python file (new or existing) and paste the following inference code. Again, we will use Chassis's quickstart mode to import load a sample piece of data.

Inference

The below inference code leverages Chassis's OMIClient. This client provides a convenience wrapper around a gRPC client that allows you to interact with the gRPC server within your model container.

from chassis.client import OMIClient
from chassis.guides import DigitsSampleData

# Execute the test_container method to spin up the container, run inference, and return the results
res = await OMIClient.test_container(container_name="my-first-chassis-model", inputs=DigitsSampleData, pull=False)
# Parse results from output item
result = res.outputs[0].output["results.json"]
# View results
print(f"Result: {result}")

Execute this code to perform an inference against your running container.

The below inference code leverages Chassis's OMIClient to run inference. Notice this code is slighly different than when running it in a Jupyter notebook, due to the built-in async functionality that comes with IPython.

import asyncio
from chassis.client import OMIClient
from chassis.guides import DigitsSampleData

async def run_test():
    # Execute the test_container method to spin up the container, run inference, and return the results
    res = await OMIClient.test_container(container_name="my-first-chassis-model", inputs=DigitsSampleData, pull=False)
    # Parse results from output item
    result = res.outputs[0].output["results.json"]
    # View results
    print(f"Result: {result}")

if __name__ == '__main__':
    asyncio.run(run_test())

Execute this code to perform an inference against your running container.

A successful inference run should yield the following result:

Result: b'[{"data": {"result": {"classPredictions": [{"class": 5, "score": 0.71212}]}}}]'

What's next?

After completing this quickstart guide, you might be wondering how to integrate your own model into this workflow. This guide intentionally abstracts out much of the model configuration for a quick and easy experience to get up and running.

Visit the Full Chassis Workflow guide to learn how to use Chassis with your own model!