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Build a Model (Data Scientists)


If you just want to try Chassis, you can use the test drive, which will deploy it for you so that you can go straight to building a MLflow model into a Docker container and pushing it to your Docker Hub account in the svc_demo.ipynb sample notebook:

💻 Launch Test Drive 💻

In order to connect to Chassis service we are going to use the SDK. We will transform our model into MLFlow format and we will upload it by making a request. After that, the image that have been created will be uploaded to Docker Hub and we will be able to use it.

Please note that in addition to the tutorial on this page, there are example Jupyter notebooks available in the Chassis repo here. Instructions to run those notebooks are provided in the README in that repo subdirectory.

Install the SDK

First step is to install the SDK and additional packages required for this tutorial using pip.

pip install chassisml scikit-learn mlflow joblib requests

Build or import the model

We can start from an existing model or create a new one. After that, we will need to transform it to MLFlow format so Chassis service will be able to manage it.

Import required libraries

Since we are going to train our own model as an example, we need to import all the libraries that we will need to do that.

import chassisml
import sklearn
from joblib import dump, load

Create the model

Just as an example we are going to create and train a simple SKLearn model.

from sklearn import datasets, svm
from sklearn.model_selection import train_test_split

digits = datasets.load_digits()
data = digits.images.reshape((len(digits.images), -1))

# Create a classifier: a support vector classifier
clf = svm.SVC(gamma=0.001)

# Split data into 50% train and 50% test subsets
X_train, X_test, y_train, y_test = train_test_split(
    data,, test_size=0.5, shuffle=False)

# Learn the digits on the train subset, y_train)

Transform the model to Chassis format

Once that we have our model we transform it to Chassis format.

First we prepare the context dict, initializing anything here that should persist across inference runs. In this case, just the model:

context = {"model": clf}

Notice that the SKLearn model that we created before is loaded into memory so that it will be packaged inside the MLFlow model.

Next, we prepare the process function, which must take input file bytes and the context dict we prepared as input. It is responsible for preprocessing the bytes, running inference, and returning formatted results. It can leverage anything in the context dict to do so:

def process(input_bytes,context):
    inputs = np.array(json.loads(input_bytes))/2
    inference_results = context["model"].predict(inputs)
    structured_results = []
    for inference_result in inference_results:
        structured_output = {
            "data": {
                "result": {"classPredictions": [{"class": str(inference_result), "score": str(1)}]}
    return structured_results

The process function can call other functions if needed.

Next, we initialize our Chassis client, which we'll use to communicate with the Chassis service. Here, we assume our instance of Chassis is running locally on port 5000:

chassis_client = chassisml.ChassisClient("http://localhost:5000")

Now let's create a Chassis model with our context dict and process function, test it locally with a local input file, and then also test the creation of the environment and the execution of the model with a sample input file within that created environment (this will occur within the Chassis service):

# create Chassis model
chassis_model = chassis_client.create_model(context=context,process_fn=process)

# test Chassis model locally (can pass filepath, bufferedreader, bytes, or text here):
sample_filepath = './examples/modzy/input_sample.json'
results = chassis_model.test(sample_filepath)

# test environment and model within Chassis service, must pass filepath here:
test_env_result = chassis_model.test_env(sample_filepath)

Build the image and publish to Modzy

Now that we have our model in Chassis format we need to make a request against the Chassis service to build the Docker image that exposes it. You can optionally define your desired conda environment and pass it to publish(), but if you don't the dependencies will be automatically inferred for you. We'll let Chassis handle inferring dependencies in this case. We just need to provide a model name and semantic version, dockerhub credentials, and we can optionally provide a sample input file and Modzy API key if we'd like to publish the model to Modzy:

response = chassis_model.publish(model_name="Sklearn Digits",model_version="0.0.1",

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

The block_until_complete call will terminate once the Chassis job completes.

Pull the image

Now that the process has completely finished we can pull and see our built image. The image name will be the model_name specified in the publish() call, but lowercase and with dashes instead of spaces. The image tag will be the model_version.

docker pull <user>/sklearn-digits:0.0.1
docker images <user>/sklearn-digits:0.0.1

If everything has gone as expected we will see something similar to this.

REPOSITORY                        TAG       IMAGE ID       CREATED         SIZE
<user>/sklearn-digits            latest    0e5c5815f2ec   3 minutes ago   2.19GB

Run an inference job in Modzy

If you provided the required arguments to publish() to publish the model to Modzy, you can use the Modzy SDK to submit an inference job to your newly-published model.

from modzy import ApiClient

client = ApiClient(base_url='', api_key=modzy_api_key)

input_name = final_status['result']['inputs'][0]['name']
model_id = final_status['result'].get("model").get("modelId")
model_version = final_status['result'].get("version")

inference_job =, model_version, {input_name: sample_filepath})
inference_job_result = client.results.block_until_complete(inference_job, timeout=None)
inference_job_results_json = inference_job_result.get_first_outputs()['results.json']