PyMLpipe - run

with mlp.run():
    mlp.register_artifact("churndata.csv",df)
    mlp.log_params({
        "lr":0.01,
        "optimizer":"SGD",
        "loss_fuction":"BCEloss"
    })
    for epoch in range(epochs):
        loss_batch=0
        for batch in range(1000,5000,1000):
            optimizer.zero_grad()
            train_data=train_x[batch-1000:batch]
            output=model(train_data)
            loss=criterion(output,train_y[batch-1000:batch].unsqueeze(1))
            loss.backward()
            optimizer.step()
            loss_batch+=loss.item()

        metrics=validate(model,test_x,test_y)
        metrics["loss"]=loss_batch
        metrics["epoch"]=epoch
        mlp.log_metrics_continious(metrics)
    mlp.pytorch.register_model("pytorch_example1", model)