PyTorch

​PyTorch is a supported framework on Truss. To package a PyTorch model, follow the steps below. There is no one-click Colab notebook for this example as it requires multiple files to operate successfully.
Install packages
If you're using a Jupyter notebook, add a line to install the torch, torchvision, and truss packages. Otherwise, ensure the packages are installed in your Python environment.
!pip install --upgrade torch torchvision truss
Truss officially supports torch version 1.9.0 or higher. Especially if you're using an online notebook environment like Google Colab or a bundle of packages like Anaconda, ensure that the version you are using is supported. If it's not, use the --upgrade flag and pip will install the most recent version.
Create an in-memory model
This is the part you want to replace with your own code. Using PyTorch, build a machine learning model and keep it in-memory. The PyTorch model example is a bit more involved code-wise, here it is in a couple of chunks.
First, create a file model.py and add the following:
import torch
import torch.nn as nn
import torch.nn.functional as F
​
class MNISTNet(nn.Module):
    def __init__(self):
        super(MNISTNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)
​
    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output
Then, you can add the following functions in the notebook:
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms
import torch.nn.functional as F
​
#pytorch model class must be in a python file
from model import MNISTNet
​
def train(model, device, train_loader, optimizer, epoch, log_interval=10):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % log_interval == 0:
            print(
                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                    epoch,
                    batch_idx * len(data),
                    len(train_loader.dataset),
                    100.0 * batch_idx / len(train_loader),
                    loss.item(),
                )
            )
​
​
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(
                output, target, reduction="sum"
            ).item()  # sum up batch loss
            pred = output.argmax(
                dim=1, keepdim=True
            )  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()
​
    test_loss /= len(test_loader.dataset)
​
    print(
        "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
            test_loss,
            correct,
            len(test_loader.dataset),
            100.0 * correct / len(test_loader.dataset),
        )
    )
​
​
def train_the_model():
    device = "cpu"
    epochs = 1
    train_kwargs = {"batch_size": 64}
    test_kwargs = {"batch_size": 1000}
    transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
    )
    dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform)
    dataset2 = datasets.MNIST("../data", train=False, transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
    model = MNISTNet().to(device)
    optimizer = optim.Adadelta(model.parameters(), lr=1.0)
​
    scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
    for epoch in range(1, epochs + 1):
        train(model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)
        scheduler.step()
    return model, train_loader, test_loader
Finally, you can train your model:
model, _ , _ = train_the_model()
Create a Truss
Use the create command to package your model into a Truss.
from truss import create
​
tr = create(model, target_directory="pytorch_truss")
Check the target directory to see your new Truss!
Serve the model
With just a bit of helper code, we can serve a prediction:
transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
    )
inputs = datasets.MNIST("../data", train=False, transform=transform)
dataset = torch.utils.data.DataLoader(inputs, batch_size=1)
​
import numpy as np
print(tr.predict(np.array(next(iter(dataset))[0])))
For information on running the Truss locally, see local development.
Last modified 22d ago