PyTorch

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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 guide demonstrates the process of automatically containerizing your PyTorch 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 PyTorch model that can be loaded into memory or code to train a PyTorch 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:

opencv-python>=4.5.5.64
numpy>=1.22.3
torch>=1.11.0
torchvision>=0.12.0

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 chassisml
import pickle
import cv2
import torch
import numpy as np
import torchvision.models as models
from torchvision import transforms

model = models.resnet50(pretrained=True)
model.eval()

labels = pickle.load(open('./data/imagenet_labels.pkl','rb'))

transform = transforms.Compose([
            transforms.ToPILImage(),
            transforms.Resize(224),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])        

device = 'cpu'

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
    decoded = cv2.imdecode(np.frombuffer(input_bytes, np.uint8), -1)
    img_t = transform(decoded)
    batch_t = torch.unsqueeze(img_t, 0).to(device)

    # run inference
    predictions = model(batch_t)

    # postprocess
    percentage = torch.nn.functional.softmax(predictions, dim=1)[0]

    _, indices = torch.sort(predictions, descending=True)
    inference_result = {
        "classPredictions": [
            {"class": labels[idx.item()], "score": percentage[idx].item()}
        for idx in indices[0][:5] ]
    }

    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") #This URL can be replaced with free, public URL found on the chassis website
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/airplane.jpg'
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="PyTorch ResNet50 Image Classification",
   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 PyTorch 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