mlflowonnxpytorch.py

!pip3 install torch torchvision pandas numpy onnx onnxruntime

import argparse

import torch
import torch.nn.functional as F
from torch import nn, optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms

import mlflow
import mlflow.pytorch
import onnx
import mlflow.onnx

class Net(nn.Module):
    def __init__(self):
        super().__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


def train(model, device, train_loader, optimizer, epoch):
    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 % 10 == 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 main():
    


    batch_size=64
    seed=1
    test_batch_size=100
    epochs=1
    lr=0.1
    gamma=0.7
    no_cuda=True # make it false for non gpu workloads
    dry_run=False
    log_interval=10
    use_cuda = not no_cuda and torch.cuda.is_available()


    torch.manual_seed(seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    train_kwargs = {"batch_size": batch_size}
    test_kwargs = {"batch_size": test_batch_size}
    if use_cuda:
        cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
        train_kwargs.update(cuda_kwargs)
        test_kwargs.update(cuda_kwargs)

    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 = Net().to(device)
    scripted_model = torch.jit.script(model)  # scripting the model
    optimizer = optim.Adadelta(model.parameters(), lr=lr)

    scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
    for epoch in range(1, epochs + 1):
        train(scripted_model, device, train_loader, optimizer, epoch)
        scheduler.step()
    with mlflow.start_run():
        f = io.BytesIO()
        torch.onnx.export(model, data, f)
        mlflow.pytorch.log_model(scripted_model, "model")  # logging scripted model
        model_path = mlflow.get_artifact_uri("model")
        loaded_pytorch_model = mlflow.pytorch.load_model(model_path)  # loading scripted model
        model.eval()
        with torch.no_grad():
            test_datapoint, test_target = next(iter(test_loader))
            prediction = loaded_pytorch_model(test_datapoint[0].unsqueeze(0).to(device))
            actual = test_target[0].item()
            predicted = torch.argmax(prediction).item()
            print(f"\nPREDICTION RESULT: ACTUAL: {actual!s}, PREDICTED: {predicted!s}")


if __name__ == "__main__":
    main()