Device Management
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Device Management is Explicitly manage the device (CPU/GPU) to ensure the model and data are on the same device.
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ctx:claims/beam/9c95419a-99e1-4237-800b-9b4747989acb- full textbeam-chunktext/plain1 KB
doc:beam/9c95419a-99e1-4237-800b-9b4747989acbShow excerpt
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…
ctx:claims/beam/551f91b2-91df-4c5b-9dc6-135e98ae92bf- full textbeam-chunktext/plain1 KB
doc:beam/551f91b2-91df-4c5b-9dc6-135e98ae92bfShow excerpt
import torch import torch.nn as nn class ScoringModel(nn.Module): def __init__(self): super(ScoringModel, self).__init__() self.model = torch.nn.Linear(10, 1) def forward(self, input_data): scores = self.mo…
ctx:claims/beam/c8102774-0736-45ab-8d51-87fae35d0377- full textbeam-chunktext/plain1 KB
doc:beam/c8102774-0736-45ab-8d51-87fae35d0377Show excerpt
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…
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