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❓ Question
hey team,
happy new year!
i am looking to reduce peak memory usage when loading a Torch-TensorRT module. given an ExportedProgram, is there something that i can be doing that is more optimal? this is on a Jetson.
What you have already tried
compiled_model = torch_tensorrt.dynamo.compile(...), using https://docs.pytorch.org/TensorRT/contributors/resource_management.htmltorch.save(compiled_model, path, pickle_protocol=5)torch.load(path, weights_only=False)(cold start, nothing else running)
the result is really high peak memory (a few times more than the serialized engine size).
thank you!! cc @peri044 @lanluo-nvidia @narendasan
Environment
Build information about Torch-TensorRT can be found by turning on debug messages
- PyTorch Version (e.g., 1.0):
- CPU Architecture:
- OS (e.g., Linux):
- How you installed PyTorch (
conda,pip,libtorch, source): - Build command you used (if compiling from source):
- Are you using local sources or building from archives:
- Python version:
- CUDA version:
- GPU models and configuration:
- Any other relevant information:
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