Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Modified the notebooks directory's README file #1102

Merged
merged 1 commit into from
Jun 9, 2022
Merged

Modified the notebooks directory's README file #1102

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 12 additions & 11 deletions notebooks/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ The most convenient way to run these notebooks is via a docker container, which
First, clone the repository:

```
git clone https://github.com/NVIDIA/Torch-TensorRT
git clone https://github.com/pytorch/TensorRT
```

Next, navigate to the repo's root directory:
Expand All @@ -23,10 +23,10 @@ At this point, we recommend pulling the [PyTorch container](https://catalog.ngc.
from [NVIDIA GPU Cloud](https://catalog.ngc.nvidia.com/) as follows:

```
docker pull nvcr.io/nvidia/pytorch:21.12-py3
docker pull nvcr.io/nvidia/pytorch:22.05-py3
```

Replace ```21.12``` with a different string in the form ```yy.mm```,
Replace ```22.05``` with a different string in the form ```yy.mm```,
where ```yy``` indicates the last two numbers of a calendar year, and
```mm``` indicates the month in two-digit numerical form, if you wish
to pull a different version of the container.
Expand All @@ -36,14 +36,18 @@ Therefore, you can run the container and the notebooks therein without
mounting the repo to the container. To do so, run

```
docker run --gpus=all --rm -it --net=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:21.12-py3 bash
docker run --gpus=all --rm -it --net=host --ipc=host \
--ulimit memlock=-1 --ulimit stack=67108864 \
nvcr.io/nvidia/pytorch:22.05-py3 bash
```

If, however, you wish for your work in the notebooks to persist, use the
```-v``` flag to mount the repo to the container as follows:

```
docker run --gpus=all --rm -it -v $PWD:/Torch-TensorRT --net=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io/nvidia/pytorch:21.12-py3 bash
docker run --gpus=all --rm -it -v $PWD:/Torch-TensorRT \
--net=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
nvcr.io/nvidia/pytorch:22.05-py3 bash
```

### b. Building a Torch-TensorRT container from source
Expand All @@ -57,7 +61,9 @@ docker build -t torch_tensorrt -f ./docker/Dockerfile .
To run this container, enter the following command:

```
docker run --gpus=all --rm -it -v $PWD:/Torch-TensorRT --net=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 torch_tensorrt:latest bash
docker run --gpus=all --rm -it -v $PWD:/Torch-TensorRT \
--net=host --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
torch_tensorrt:latest bash
```

### c. Running the notebooks inside the container
Expand Down Expand Up @@ -100,8 +106,3 @@ Within the container, the notebooks themselves are located at `/Torch-TensorRT/n
- [vgg-qat.ipynb](vgg-qat.ipynb): Quantization Aware Trained models in INT8 using Torch-TensorRT
- [EfficientNet-example.ipynb](EfficientNet-example.ipynb): Simple use of 3rd party PyTorch model library
- [CitriNet-example.ipynb](CitriNet-example.ipynb): Optimizing the Nemo Citrinet acoustic model


```python

```