tensor device transfer

inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss +…

# Define the resizing module class ResizingModule(nn.Module): def __init__(self): super(ResizingModule, self).__init__() self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): …

3. **Device Management**: Explicitly manage the device (CPU/GPU) to ensure the model and data are on the same device. 4. **Gradient Management**: Since you are using the model for scoring, ensure that gradients are disabled to improve perf…

- Use tools like `torch.utils.benchmark` to measure and compare the performance of different configurations. ### Example with Error Handling Here's an example with error handling: ```python import torch import torch.nn as nn class Sc…

for epoch in range(100): for batch in data_loader: inputs = batch['query'].float().to(device) labels = batch['label'].long().to(device) optimizer.zero_grad() outputs = model(input…

data_loader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4) model = DebugModel().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer try: for epoc…

def __len__(self): return len(self.queries) def __getitem__(self, idx): query = self.queries[idx] label = self.labels[idx] return {'query': query, 'label': label} # Define the model class DebugModel…

Here's how you can implement these optimizations: #### 1. Batch Processing Process multiple texts in a single batch to take advantage of parallel processing. #### 2. Model Quantization Use quantization to reduce the precision of the mod…