trainer

import torch from torch import nn, optim from torch.utils.data import DataLoader, Dataset from trainer import Trainer import os # 1. Dummy Dataset class DummyDataset(Dataset): def __init__(self, num_samples=100, input_dim=10, output_dim=1): self.X = torch.randn(num_samples, input_dim) self.y = torch.randn(num_samples, output_dim) def __len__(self): return len(self.X) def __getitem__(self, idx): return self.X[idx], self.y[idx] # 2. Dummy Model class DummyModel(nn.Module): def __init__(self, input_dim=10, output_dim=1): super().__init__() self.linear = nn.Linear(input_dim, output_dim) def forward(self, x): return self.linear(x) # 3. Setup components input_dim = 10 output_dim = 1 model = DummyModel(input_dim, output_dim) optimizer = optim.Adam(model.parameters(), lr=0.001) criterion = nn.MSELoss() train_dataset = DummyDataset(num_samples=100, input_dim=input_dim, output_dim=output_dim) eval_dataset = DummyDataset(num_samples=20, input_dim=input_dim, output_dim=output_dim) dataloader_train = DataLoader(train_dataset, batch_size=4, shuffle=True) dataloader_eval = DataLoader(eval_dataset, batch_size=4, shuffle=False) # 4. Minimal Config (usually from argparse) config = { "output_path": "./trainer_quickstart_output", "epochs": 2, "start_by_epochs": True, "print_step": 1, "save_step": 1, "eval_step": 1 } # Ensure output path exists for trainer to save checkpoints/logs os.makedirs(config["output_path"], exist_ok=True) # 5. Initialize and run Trainer trainer_instance = Trainer( config=config, model=model, optimizer=optimizer, criterion=criterion, dataloader_train=dataloader_train, dataloader_eval=dataloader_eval, ) print(f"Starting training for {config['epochs']} epochs...") trainer_instance.train_loop() print("Training finished.") # Output files will be created in ./trainer_quickstart_output # In a real application, you might add cleanup or more complex logging.