General Chassis Template

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Chassisml Version Update Warning
Note: this guide only works when using Chassisml version 1.4.13 or lower. Please visit the latest framework examples here.

This page serves as a generic guide for automating the containerization of your machine learning model using Chassis.ml. Follow this guide if the framework you are working with is not listed here. You will notice that generic pseudo code is used throughout. You are expected to replace these chunks with your model code where appropriate.

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What you will need

Dockerhub account

Connection to running Chassis.ml service (either from a local deployment or via publicly-hosted service)

Trained model

Python environment

Set Up Environment

Create a Python virtual environment and install the Python packages required to load and run your model. At a minimum, pip install the following packages:

pip install chassisml modzy-sdk

Depending on the framework or model development process you are working with, ensure your virtual environment includes all required dependencies.

Load Model into Memory

If you plan to use the Chassis service, you must first load your model into memory. If you have your trained model file saved locally (.pth, .pkl, .h5, .joblib, or other file format), you can load your model from the weights file directly, or alternatively train and use the model object.

model = framework.load("path/to/model.file")

Define `process` Function

You can think of this function as your "inference" function that will take input data as raw bytes, process the inputs, make predictions, and return the results. This method is the sole parameter required to create a ChassisModel object.

def process(input_bytes):
  # preprocess
  data = preprocess(input_bytes)

  # run inference
  predictions = model.predict(data)

  # post process predictions
  formatted_results = postprocess(predictions)

  return formatted_results

Create `ChassisModel` Object and Publish Model

First, connect to a running instance of the Chassis service - either by deploying on your machine or by connecting to the publicly hosted version of the service. Then you can use the process function you defined to create a ChassisModel object and publish your model.

chassis_client = chassisml.ChassisClient("http://localhost:5000")
chassis_model = chassis_client.create_model(process_fn=process)

dockerhub_user = <my.username>
dockerhub_pass = <my.password>

response = chassis_model.publish(
   model_name="Sample ML Model",
   model_version="0.0.1",
   registry_user=dockerhub_user,
   registry_pass=dockerhub_pass
)

job_id = response.get('job_id')
final_status = chassis_client.block_until_complete(job_id)

When this job completes and you can print the final_status object, you have now successfully completed the packaging of your model. In your Dockerhub account, you should see your new container listed in the "Repositories" tab.

1438 — Figure 1. Example Chassis-built Container

Congratulations! In just minutes you automatically created a Docker container with just a few lines of code. To deploy your new model container to Modzy, follow one of the following guides:

Set Up Environment

Load Model into Memory

Define process Function

Create ChassisModel Object and Publish Model

Define `process` Function

Create `ChassisModel` Object and Publish Model