Tripy: A Python Programming Model For TensorRT
Project description
Tripy: A Python Programming Model For TensorRT
Quick Start | Installation | Examples | Notebooks | Contributing | Documentation
Tripy is a debuggable, Pythonic frontend for TensorRT, a deep learning inference compiler.
What you can expect:
- High performance by leveraging TensorRT's optimization capabilties.
- An intuitive API that follows conventions of the ecosystem.
- Debuggability with features like eager mode to interactively debug mistakes.
- Excellent error messages that are informative and actionable.
- Friendly documentation that is comprehensive but concise, with code examples.
Installation
Option 1: Install with pip
python3 -m pip install nvtripy -f https://nvidia.github.io/TensorRT-Incubator/packages.html
Option 2: Use the container image
docker run --pull always --gpus all -it --rm ghcr.io/nvidia/tensorrt-incubator/nvtripy:latest
Quick Start
See the Introduction To Tripy guide for details:
-
Defining a model:
class Model(tp.Module): def __init__(self): self.conv = tp.Conv(in_channels=1, out_channels=1, kernel_dims=[3, 3]) def forward(self, x): x = self.conv(x) x = tp.relu(x) return x
-
Initializing it:
model = Model() model.load_state_dict( { "conv.weight": tp.ones((1, 1, 3, 3)), "conv.bias": tp.ones((1,)), } ) dummy_input = tp.ones((1, 1, 4, 4)).eval()
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Executing in eager mode:
eager_out = model(dummy_input)
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Compiling and executing:
compiled_model = tp.compile( model, args=[tp.InputInfo(shape=(1, 1, 4, 4), dtype=tp.float32)], ) compiled_out = compiled_model(dummy_input)
Building Wheels From Source
For the latest changes, build Tripy wheels from source:
-
Install
build:python3 -m pip install build
-
Build a wheel from the
tripyroot directory:python3 -m build . -w
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Install the wheel from the
tripyroot directory:python3 -m pip install -f https://nvidia.github.io/TensorRT-Incubator/packages.html dist/nvtripy-*.whl
-
[Optional] Sanity check:
python3 -c "import nvtripy as tp; x = tp.ones((5,), dtype=tp.int32); assert x.tolist() == [1] * 5"
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