update service data

optimize_model.py

@@ -0,0 +1,47 @@
+# optimize_model.py
+#
+# author: deng
+# date  : 20230418
+
+import shutil
+from pathlib import Path
+
+import torch
+import mlflow
+
+
+def optimize_pytorch_model(run_id: str) -> None:
+    """Optimize Pytorch model on MLflow server, the optimized model will be sent back
+
+    Args:
+        run_id (str): mlflow run id
+    """
+
+    download_path = Path('./model/downloaded_pytorch_model')
+    if download_path.is_dir():
+        print(f'Remove existed dir: {download_path}')
+        shutil.rmtree(download_path)
+
+    # Download Pytorch model to local file system
+    mlflow_model = mlflow.pytorch.load_model(f'runs:/{run_id}/model')
+    mlflow.pytorch.save_model(mlflow_model, download_path)
+
+    # Optimize model
+    model = torch.load(download_path.joinpath('data/model.pth'))
+    dummy_input = torch.randn(5)
+    torch.onnx.export(model, dummy_input, download_path.joinpath('data/model.onnx'))
+    # we can not call TensorRT on macOS, so imagine we get a serialized model
+    download_path.joinpath('data/model.trt').touch()
+
+    # Save optimized model to given run
+    with mlflow.start_run(run_id=run_id):
+        mlflow.log_artifact(download_path.joinpath('data/model.trt'), 'model/data')
+        print(f'Optimized model had been uploaded to server: {mlflow.get_tracking_uri()}')
+
+
+if __name__ == '__main__':
+
+    mlflow.set_tracking_uri('http://127.0.0.1:5001')
+    optimize_pytorch_model(
+        run_id='f1b7b9a5ba934f158c07975a8a332de5'
+    )

service/artifacts/3/f1b7b9a5ba934f158c07975a8a332de5/artifacts/code/train.py

@@ -0,0 +1,98 @@
+# train.py
+#
+# author: deng
+# date  : 20230221
+
+import torch
+import torch.nn as nn
+from torch.optim import SGD
+import mlflow
+from mlflow.models.signature import ModelSignature
+from mlflow.types.schema import Schema, ColSpec
+from tqdm import tqdm
+
+
+class Net(nn.Module):
+    """ define a simple neural network model """
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = nn.Linear(5, 3)
+        self.fc2 = nn.Linear(3, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        return x
+
+
+def train(model, dataloader, criterion, optimizer, epochs):
+    """ define the training function """
+    for epoch in tqdm(range(epochs), 'Epochs'):
+
+        for batch, (inputs, labels) in enumerate(dataloader):
+
+            # forwarding
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+
+            # update gradient
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        # log loss
+        mlflow.log_metric('train_loss', loss.item(), step=epoch)
+
+    return loss
+
+
+if __name__ == '__main__':
+
+    # set hyper parameters
+    learning_rate = 1e-2
+    batch_size = 10
+    epochs = 20
+
+    # create a dataloader with fake data
+    dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
+    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=batch_size)
+
+    # create the model, criterion, and optimizer
+    model = Net()
+    criterion = nn.MSELoss()
+    optimizer = SGD(model.parameters(), lr=learning_rate)
+
+    # set the tracking URI to the model registry
+    mlflow.set_tracking_uri('http://127.0.0.1:5001')
+    mlflow.set_experiment('train_fortune_predict_model')
+
+    # start a new MLflow run
+    with mlflow.start_run():
+
+        # train the model
+        loss = train(model, dataloader, criterion, optimizer, epochs)
+
+        # log some additional metrics
+        mlflow.log_metric('final_loss', loss.item())
+        mlflow.log_param('learning_rate', learning_rate)
+        mlflow.log_param('batch_size', batch_size)
+
+        # create a signature to record model input and output info
+        input_schema = Schema([
+            ColSpec('float', 'age'),
+            ColSpec('float', 'mood level'),
+            ColSpec('float', 'health level'),
+            ColSpec('float', 'hungry level'),
+            ColSpec('float', 'sexy level')
+        ])
+        output_schema = Schema([ColSpec('float', 'fortune')])
+        signature = ModelSignature(inputs=input_schema, outputs=output_schema)
+
+        # log trained model
+        mlflow.pytorch.log_model(model, 'model', signature=signature)
+
+        # log training code
+        mlflow.log_artifact('./train.py', 'code')
+
+    print('Completed.')