BentoML Example: PyTorch Image Classifier¶
BentoML is an open source platform for machine learning model serving and deployment. In this project we will use BentoML to package the image classifier model, and build a containerized REST API model server.
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 bentoml
!pip install torch torchvision pillow
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(2): # 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 PIL import Image
from torch.autograd import Variable
from torchvision import transforms
import bentoml
from bentoml.artifact import PytorchModelArtifact
from bentoml.handlers import ImageHandler
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
@bentoml.env(pip_dependencies=['torch', 'numpy', 'torchvision', 'scikit-learn'])
@bentoml.artifacts([PytorchModelArtifact('net')])
class ImageClassifier(bentoml.BentoService):
@bentoml.api(ImageHandler)
def predict(self, img):
img = Image.fromarray(img).resize((32, 32))
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
input_data = transform(img)
outputs = self.artifacts.net(Variable(input_data).unsqueeze(0))
_, output_classes = outputs.max(dim=1)
return classes[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 ImageClassifier
# 2) `pack` it with required artifacts
bento_service = ImageClassifier.pack(net=net)
# 3) save your BentoSerivce to file archive
saved_path = bento_service.save()
Test saved BentoService from CLI¶
!pip install {saved_path}
!ImageClassifier predict --input=bird.jpg
Model Serving via REST API¶
In your termnial, run the following command to start the REST API server:
!bentoml serve {saved_path}
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