This folder contains demo notebooks for TRTorch.
The most convenient way to run these notebooks is via a docker container, which provides a self-contained, isolated and re-producible environment for all experiments.
First, clone the repository:
git clone https://github.com/NVIDIA/TRTorch
Next, build the NVIDIA TRTorch container (from repo root):
docker build -t trtorch -f notebooks/Dockerfile.notebook .
Then launch the container with:
docker run --runtime=nvidia -it --rm --ipc=host --net=host trtorch
Within the docker interactive bash session, start Jupyter with
jupyter notebook --allow-root --ip 0.0.0.0 --port 8888
And navigate a web browser to the IP address or hostname of the host machine
at port 8888: http://[host machine]:8888
Use the token listed in the output from running the jupyter command to log in, for example:
http://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b
Within the container, the notebooks themselves are located at /workspace/TRTorch/notebooks.
- lenet-getting-started.ipynb: simple example on a LeNet network.
- ssd-object-detection-demo.ipynb: demo for compiling a pretrained SSD model using TRTorch.