DJL - Jupyter notebooks¶
Overview¶
This folder contains tutorials that illustrate how to accomplish basic AI tasks with Deep Java Library (DJL).
Beginner Tutorial¶
More Tutorial Notebooks¶
- Run object detection with model zoo
- Load pre-trained PyTorch model
- Load pre-trained Apache MXNet model
- Transfer learning example
- Question answering example
You can run our notebook online: 
Setup¶
JDK 11 (not jre)¶
JDK 11 (or above are required) to run the examples provided in this folder.
to confirm the java path is configured properly:
java --list-modules | grep "jdk.jshell"
> jdk.jshell@12.0.1
Install jupyter notebook on python3¶
pip3 install jupyter
Install IJava kernel for jupyter¶
git clone https://github.com/frankfliu/IJava.git
cd IJava/
./gradlew installKernel
Start jupyter notebook¶
jupyter notebook
Docker setup¶
You may want to use docker for simple installation or you are using Windows.
Run docker image¶
cd jupyter
docker run -itd -p 127.0.0.1:8888:8888 -v $PWD:/home/jupyter deepjavalibrary/jupyter
You can open the http://localhost:8888 to see the hosted instance on docker.
Build docker image by yourself¶
You can read Dockerfile for detail. To build docker image:
cd jupyter
docker build -t deepjavalibrary/jupyter .
Run docker compose¶
cd jupyter
docker-compose build
docker-compose up -d
You can open the http://localhost:8888 to see the hosted instance on docker compose.