PyTorch
This guide demonstrates the process of automatically containerizing your PyTorch model.
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
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
process
FunctionYou 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
ChassisModel
Object and Publish ModelFirst, 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/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.
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:
Updated 7 months ago