This guide demonstrates the process of automatically containerizing your Fastai model.

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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 Fastai model that can be loaded into memory or code to train a Fastai model from scratch
  • Python environment

NOTE: To follow along, you can reference the Jupyter notebook example and data files here.

Set Up Environment

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We recommend you follow this guide using a Jupyter Notebook. Follow the appropriate install instructions based on your 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

If you would like to follow this guide directly, pip install the following additional packages:

fastai>=2.6.3
numpy>=1.22.3

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.

import os
import chassisml
import numpy as np
import json
import pandas as pd
from io import StringIO
from fastai.tabular.all import TabularDataLoaders, RandomSplitter, TabularPandas, tabular_learner, Categorify, FillMissing, Normalize, range_of, accuracy

# load and preprocess dataset
df = pd.read_csv("./data/adult_sample/adult.csv")
df.head()

dls = TabularDataLoaders.from_csv("./data/adult_sample/adult.csv", path=os.path.join(os.getcwd(), "data/adult_sample"), y_names="salary",
    cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race'],
    cont_names = ['age', 'fnlwgt', 'education-num'],
    procs = [Categorify, FillMissing, Normalize])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

splits = RandomSplitter(valid_pct=0.2)(range_of(df))
to = TabularPandas(df, procs=[Categorify, FillMissing,Normalize],
                   cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race'],
                   cont_names = ['age', 'fnlwgt', 'education-num'],
                   y_names='salary',
                   splits=splits)

# save test subset
test_df = df.copy()
test_df.drop(['salary'], axis=1, inplace=True)
test_df[:20].to_csv("./data/sample_adult_data.csv", index=False)

# rebuild dataloaders with preprocessed data
dls = to.dataloaders(bs=64)

# train model
learn = tabular_learner(dls, metrics=accuracy)
learn.fit_one_cycle(1)

# define labels
labels = ['<50k', '>50k']

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):
    inputs = pd.read_csv(StringIO(str(input_bytes, "utf-8")))
    dl = learn.dls.test_dl(inputs)
    preds = learn.get_preds(dl=dl)[0].numpy()

    inference_result = {
        "classPredictions": [
            {
                "row": i+1,
                "predictions": [
                    {"class": labels[j], "score": round(pred[j], 4)} for j in range(2)
                ]
            } for i, pred in enumerate(preds)
        ]
    }

    structured_output = {
        "data": {
            "result": inference_result,
            "explanation": None,
            "drift": None,
        }
    }

    return structured_output

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, run a few tests to ensure your model object returns the expected results, and finally publish your model.

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

Define sample file from local filepath and run a series of tests.

NOTE: test_env method is not available on publicly-hosted service.

sample_filepath = './data/sample_adult_data.csv'
results = chassis_model.test(sample_filepath)
print(results)

test_env_result = chassis_model.test_env(sample_filepath)
print(test_env_result)

Define your Dockerhub credentials and publish your model.

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

response = chassis_model.publish(
   model_name="Fast AI Salary Prediction",
   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)

You have successfully completed the packaging of your Fastai model. In your Dockerhub account, you should see your new container listed in the "Repositories" tab.

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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 of your new model container to Modzy, follow the Import Container guide.







What’s Next
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