BentoML Example: PyTorch Image Classifier¶
BentoML makes moving trained ML models to production easy:
- Package models trained with any ML framework and reproduce them for model serving in production
- Deploy anywhere for online API serving or offline batch serving
- High-Performance API model server with adaptive micro-batching support
- Central hub for managing models and deployment process via Web UI and APIs
- Modular and flexible design making it adaptable to your infrastrcuture
BentoML is a framework for serving, managing, and deploying machine learning models. It is aiming to bridge the gap between Data Science and DevOps, and enable teams to deliver prediction services in a fast, repeatable, and scalable way.
Before reading this example project, be sure to check out the Getting started guide to learn about the basic concepts in BentoML.
This notebook was built based on the official pytorch tutorial https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
%reload_ext autoreload
%autoreload 2
%matplotlib inline
!pip install -q bentoml "torch==1.6.0" "torchvision==0.7.0" "pillow==7.2.0"
import torch
import torchvision
import torchvision.transforms as transforms
Training an image classifier¶
We will do the following steps in order:
- Load and normalizing the CIFAR10 training and test datasets using torchvision
- Define a Convolutional Neural Network
- Define a loss function
- Train the network on the training data
- Test the network on the test data
- Model Serving with BentoML
1 Loading and Normalizing CIFAR10¶
The output of torchvision datasets are PILImage images of range [0, 1]. We transform them to Tensors of normalized range [-1, 1].
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
Let us show some of the training images, for fun.
import matplotlib.pyplot as plt
import numpy as np
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
2 Define a Convolutional Neural Network¶
Copy the neural network from the Neural Networks section before and modify it to take 3-channel images (instead of 1-channel images as it was defined).
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
3 Define a Loss function and optimize¶
Let’s use a Classification Cross-Entropy loss and SGD with momentum.
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
4 Train the network¶
This is when things start to get interesting. We simply have to loop over our data iterator, and feed the inputs to the network and optimize.
for epoch in range(1): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
5 Test the network on the test data¶
We have trained the network for 2 passes over the training dataset. But we need to check if the network has learnt anything at all.
We will check this by predicting the class label that the neural network outputs, and checking it against the ground-truth. If the prediction is correct, we add the sample to the list of correct predictions.
Okay, first step. Let us display an image from the test set to get familiar.
dataiter = iter(testloader)
images, labels = dataiter.next()
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
Let us look at how the network performs on the whole dataset.
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
6 Model Serving with BentoML¶
First, define ML service with BentoML
%%writefile pytorch_image_classifier.py
from typing import List, BinaryIO
from PIL import Image
import torch
from torch.autograd import Variable
from torchvision import transforms
import bentoml
from bentoml.frameworks.pytorch import PytorchModelArtifact
from bentoml.adapters import FileInput
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
@bentoml.env(pip_packages=['torch', 'numpy', 'torchvision', 'scikit-learn'])
@bentoml.artifacts([PytorchModelArtifact('net')])
class PytorchImageClassifier(bentoml.BentoService):
@bentoml.utils.cached_property
def transform(self):
return transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
@bentoml.api(input=FileInput(), batch=True)
def predict(self, file_streams: List[BinaryIO]) -> List[str]:
input_datas = []
for fs in file_streams:
img = Image.open(fs).resize((32, 32))
input_datas.append(self.transform(img))
outputs = self.artifacts.net(Variable(torch.stack(input_datas)))
_, output_classes = outputs.max(dim=1)
return [classes[output_class] for output_class in output_classes]
Now you can instantiate a BentoService defined above with the trained model, and save the entire BentoService to a file archive:
# 1) import the custom BentoService defined above
from pytorch_image_classifier import PytorchImageClassifier
# 2) `pack` it with required artifacts
bento_svc = PytorchImageClassifier()
bento_svc.pack('net', net)
# 3) save your BentoSerivce to file archive
saved_path = bento_svc.save()
print(saved_path)
REST API Model Serving (for debug only)¶
To start a REST API model server with the BentoService saved above, use the bentoml serve command:
!bentoml serve PytorchImageClassifier:latest --enable-microbatch
If you are running this notebook from Google Colab, you can start the dev server with --run-with-ngrok option, to gain acccess to the API endpoint via a public endpoint managed by ngrok:
!bentoml serve PytorchImageClassifier:latest --run-with-ngrok
Open a terminal and go to the directory of the current notebook, then run the following command to evaluate.
curl -X POST "http://127.0.0.1:5000/predict" -H "Content-Type: image/*" --data-binary "@bird.jpg"
alternatively:
curl -X POST "http://127.0.0.1:5000/predict" -F image=@bird.png
Go visit http://127.0.0.1:5000/ from your browser, click /predict -> Try it out -> Choose File -> Execute to sumbit an image from your computer
Containerize model server with Docker¶
One common way of distributing this model API server for production deployment, is via Docker containers. And BentoML provides a convenient way to do that.
Note that docker is not available in Google Colab. You will need to download and run this notebook locally to try out this containerization with docker feature.
If you already have docker configured, simply run the follow command to product a docker container serving the IrisClassifier prediction service created above:
!bentoml containerize PytorchImageClassifier:latest -t pytorchimageclassifier:latest
!docker run --rm -p 5000:5000 pytorchimageclassifier --workers 1
Launch inference job from CLI¶
BentoML cli supports loading and running a packaged model from CLI. With the DataframeInput adapter, the CLI command supports reading input Dataframe data from CLI argument or local csv or json files:
!bentoml run PytorchImageClassifier:latest predict --input-file bird.jpg
Deployment Options¶
If you are at a small team with limited engineering or DevOps resources, try out automated deployment with BentoML CLI, currently supporting AWS Lambda, AWS SageMaker, and Azure Functions:
If the cloud platform you are working with is not on the list above, try out these step-by-step guide on manually deploying BentoML packaged model to cloud platforms:
- AWS ECS Deployment
- Google Cloud Run Deployment
- Azure container instance Deployment
- Heroku Deployment
Lastly, if you have a DevOps or ML Engineering team who's operating a Kubernetes or OpenShift cluster, use the following guides as references for implementating your deployment strategy: