9.5. Clara AI Liver Tumor Segmentation Operator
This asset requires the Clara Deploy SDK. Follow the instructions on the Clara Bootstrap page to install the Clara Deploy SDK.
This example is a containerized AI inference application, developed for use as one of the operators in the Clara Deploy pipelines. The application is built on the base AI application container, which provides the application framework to deploy Clara Train TLT trained models. The same execution configuration file, set of transform functions, and scanning window inference logic are used; however, inference is performed on the TensorRT Inference Server.
This application, in the form of a Docker container, expects an input folder (/input
by default),
which can be mapped to the host volume when the Docker container is started. This folder must
contain a volume image file in the Nifti or MetaImage
format. Furthermore, the volume image must be constructed from a single series of a
DICOM study, typically an axial series with the data type of the original primary.
This application saves the segmentation results to an output folder, /output
by default,
which can also be mapped to a folder on the host volume. After the successful completion
of the application, a segmentation volume image of format
MetaImage is saved in the output folder.
The name of the output file is the same as that of the input file due to
certain limitations of the downstream consumer.
The example container also publishes data for the Clara Deploy Render Server to the
/publish
folder by default. The original volume image, segmented volume image, and metadata file,
along with a render configuration file, are saved in this folder.
The NVIDIA® Clara Train Transfer Learning Toolkit (TLT) for Medical Imaging provides pre-trained models unique to medical imaging, with additional capabilities such as integration with the AI-assisted Annotation SDK for speeding up annotation of medical images. This allows access to AI-assisted labeling [Reference].
The application uses the segmentation_liver_v1
model provided by the NVIDIA Clara Train TLT
for liver tumor segmentation, which is converted from the TensorFlow Checkpoint model to
tensorflow_graphdef
using the TLT model export tool. The input tensor is of shape 96x96x96
with a single channel. The output is of the same shape with three channels.
The application also uses the same transforms library and configuration file for
the validation/inference pipeline during TLT model training. The key model attributes (e.g.
the model name) are saved in the config_inference.json
file and consumed by the application at
runtime.
9.5.4.1. NVIDIA TensorRT Inference Server (TRTIS)
This application performs inference on the NVIDIA TensorRT Inference Server (TRTIS), which provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or gRPC endpoint, allowing remote clients to request inferencing for any model managed by the server.
The application source code files are in the directory structure shown below.
/
├── app_base_inference_v2
├── ai4med
├── config
│ ├── config_render.json
│ ├── config_inference.json
│ └── __init__.py
├── dlmed
├── inferers
├── model_loaders
├── ngc
├── public
├── utils
├── writers
├── app.py
├── Dockerfile
├── executor.py
├── logging_config.json
├── main.py
└── requirements.txt
The following describes the directory contents:
The
ai4med
anddlmed
directories contain the library modules shared with Clara Train SDK, mainly for its transforms functions and base inference client classes.The
config
directory contains model-specific configuration files, which is needed when building a customized container for a specific model.The
config_inference.json
file contains the configuration sections for pre- and post-transforms, as well as the model loader, inferer, and writer.The
config_render.json
contains the configuration for the Clara Deploy Render Server.
The
inferers
directory contains the implementation of the simple and scanning window inference client using the Triton API client libraryThe
model_loaders
directory contains the implementation of the model loader that gets model details from Triton Inference Server.The
ngc
andpublic
directories contain the user documentation.The
utils
directory contains utilities for loading modules and creating application objects.The
Writers
directory contains the specialized output writer required by Clara Deploy SDK, which saves the segmentation result to a volume image file as MetaImage.
9.5.6.1. Prerequisites
Check if the Docker image of
Triton
has been imported into the local Docker repository with the following command: .. code-block:: bashdocker images | grep tritonserver
Look for the image name
tritonserver
and the correct tag for the release, e.g.20.07-v1-py3
. If the image does not exist locally, it will be pulled from NVIDIA Docker registry.Download both the input dataset and the trained model from the
MODEL SCRIPTS
section for this container on NGC, following the steps in theSetup
section.
9.5.6.2. Step 1
Change to your working directory (e.g. test_docker
).
9.5.6.3. Step 2
Create, if they do not exist, the following directories under your working directory:
input
containing the input image fileoutput
for the segmentation outputpublish
for publishing data for the Render Serverlogs
for the log filesmodels
containing models copied from thesegmentation_liver_v1
folder
9.5.6.4. Step 3
In your working directory,
Create a shell script (
run_docker.sh
, or another name if you prefer.Copy the sample content below, change the
APP_NAME
to the full name of this docker, e.g.nvcr.io/ea-nvidia-clara/clara/ai-livertumor:0.7.2-2009.3
.Save the file.
#!/bin/bash
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
# Define the name of the app (aka operator); assumed the same as the project folder name
APP_NAME="app_livertumor"
# Define the model name for use when launching TRTIS with only the specific model
MODEL_NAME="segmentation_liver_v1"
# Define the Triton Inference Server Docker image, which will be used for testing
# Use either local repo or NVIDIA repo
TRITON_IMAGE="nvcr.io/nvidia/tritonserver:20.07-v1-py3"
# Launch the container with the following environment variables
# to provide runtime information.
export NVIDIA_CLARA_TRTISURI="localhost:8000"
# Create a Docker network so that containers can communicate on this network
NETWORK_NAME="container-demo"
# Create network
docker network create ${NETWORK_NAME}
# Run TRTIS(name: triton), maping ./models/${MODEL_NAME} to /models/${MODEL_NAME}
# (localhost:8000 will be used)
RUN_TRITON="nvidia-docker run --name triton --network${NETWORK_NAME}-d --rm --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
-p 8000:8000 \
-v$(pwd)/models/${MODEL_NAME}:/models/${MODEL_NAME}${TRITON_IMAGE}\
tritonserver --model-repository=/models"
# Display the command
echo ${RUN_TRITON}
# Run the command to start the inference server Docker
eval ${RUN_TRITON}
# Wait until Triton is ready
triton_local_uri=$(docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' triton)
echo -n "Wait until Triton${triton_local_uri}is ready..."
while [ $(curl -s ${triton_local_uri}:8000/api/status | grep -c SERVER_READY) -eq 0 ]; do
sleep 1
echo -n "."
done
echo "done"
export NVIDIA_CLARA_TRTISURI="${triton_local_uri}:8000"
# Run ${APP_NAME} container
# Launch the app container with the following environment variables internally,
# to provide input/output path information
docker run --name ${APP_NAME} --network ${NETWORK_NAME} -it --rm \
-v $(pwd)/input:/input \
-v $(pwd)/output:/output \
-v $(pwd)/logs:/logs \
-v $(pwd)/publish:/publish \
-e NVIDIA_CLARA_TRTISURI \
-e DEBUG_VSCODE \
-e DEBUG_VSCODE_PORT \
-e NVIDIA_CLARA_NOSYNCLOCK=TRUE \
${APP_NAME}
echo "${APP_NAME}is done."
# Stop Triton container
echo "Stopping Triton"
docker stop triton > /dev/null
# Remove network
docker network remove ${NETWORK_NAME} > /dev/null
9.5.6.5. Step 4
Execute the script as shown below and wait for the application container to finish:
./run_docker.sh
9.5.6.6. Step 5
Check for the following output files:
Segmentation results in the
output
directory:One file of the same name as your input file, with extension
.mhd
One file of the same name, with extension
.raw
Published data in the
publish
directory:Original volume image, in either MHD or NIfTI format
Segmentation volume image (
<input file name only>.output.mhd
and<input file name only>.output.raw
)Render Server config file (
config_render.json
)Metadata file describing the above file (
config.meta
)
9.5.6.7. Step 6
To visualize the segmentation results, any tool that support MHD or NFiTI can be used, e.g. 3D Slicer.
To see the internals of the container or to run the application within the container, please follow the following steps.
See the above section on how to run the container with the required environment variables and volume mapping, and start the container by replacing the
docker run
command with the following: .. code-block:: bashdocker run -it –rm –entrypoint /bin/bash
Once in the Docker terminal, ensure the current directory is
/
.Execute the following command: .. code-block:: bash
python3 ./app_base_inference_v2/main.py
When finished, type
exit
.