| title | order | snippet | summary-home | featured-home |
|---|---|---|---|---|
Production Ready |
1 |
```python
import torch
class MyModule(torch.nn.Module):
def __init__(self, N, M):
super(MyModule, self).__init__()
self.weight = torch.nn.Parameter(torch.rand(N, M))
def forward(self, input):
if input.sum() > 0:
output = self.weight.mv(input)
else:
output = self.weight + input
return output
# Compile the model code to a static representation
my_script_module = torch.jit.script(MyModule(3, 4))
# Save the compiled code and model data so it can be loaded elsewhere
my_script_module.save("my_script_module.pt")
```
|
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. |
true |
With TorchScript, PyTorch provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.