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ADAPT is the open source white-box testing framework for deep neural networks

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# ADAPT
ADAPT is an open source white-box testing framework for deep neural networks, which is first introduced
in [Effective White-Box Testing for Deep Neural Networks with Adaptive Neuron-Selection Strategy](http://prl.korea.ac.kr/~pronto/home/papers/issta20.pdf).

## Docker
ADAPT offers a docker image with pre-installed ADAPT.
If you want to install docker, please see [here](https://docs.docker.com/get-docker/).
Instructions for GPU support for Docker can be found in [here](https://github.com/NVIDIA/nvidia-docker).
Following command will download an image.
```bash
$ docker pull ghcr.io/kupl/adapt
```
You can see the downloaded image with the following command.
```
$ docker images
REPOSITORY              TAG                           IMAGE ID            CREATED             SIZE
ghcr.io/kupl/adapt      latest                        ...
```

### Docker container
To open a bash using the docker image, use the following command.
```bash
$ docker run --rm -it ghcr.io/kupl/adapt bash
```
If no commands are pass to docker, jupyter notebook server will be launched. The command will open a server at [http://localhost:8888/](http://localhost:8888).
*If you are using a server, replace localhost with the ip address (or domain) of your server.*
```bash
$ docker run -p 8888:8888 ghcr.io/kupl/adapt
```

### Build docker image
If you want to build your docker image by yourself, the following command will download this repository and create a docker image with a tag of ```adapt```.
```bash
$ git clone https://github.com/kupl/adapt.git
$ cd adapt
$ docker build . --tag adapt
```

## Local Installation
If you do not prefer docker, or want to implement your own idea based on ADAPT, you can install ADAPT in your local machine.
First, download this repository using git.
```bash
$ git clone https://github.com/kupl/adapt.git
$ cd adapt
```
ADAPT uses [Tensorflow 2.0](https://www.tensorflow.org/). To install Tensorflow 2.0, you need a ```pip>=19.0```.
The following commands will create a virtual environment and update ```pip``` with the Ubuntu machine.
If you successfully create a virtual environment, your shell will be prefixed by ```(venv)```.
*Updating system ```pip``` using ```pip``` can cause some [problems](https://github.com/pypa/pip/issues/5599), so using virtual enviroment is **highly recommended**.*
More detailed instructions, including instructions about setting GPU acceleration, can be found in [here](https://www.tensorflow.org/install/pip).
``` bash
$ sudo apt update
$ sudo apt install python3-dev python3-pip python3-venv
$ python3 -m venv --system-site-packages ./venv
$ source ./venv/bin/activate
(venv) $ pip install --upgrade pip
```
Install ADAPT with the following command. ADAPT will automatically install all dependancies, including ```tensorflow```.
```bash
(venv) $ pip install .
```
To deactivate the virtual environment, type the following command. Then, ```(venv)``` in front of your shell will disappear.
```
(venv) $ deactivate
```

### Jupyter notebook
The following command will install jupyter notebook in your virtual environment.
```bash
(venv) $ pip install notebook
```
To launch jupyter notebook server, type the following command.
```bash
(venv) $ jupyter notebook
# If you are using a server machine, use following command instead of above one, and visit http://(your server's ip address or domain):8888.
(venv) $ jupyter notebook --ip 0.0.0.0
```
If your machine have jupyter notebook installed in system-wide, you can install a kernel with the following commands.
```bash
(venv) $ pip install -I ipykernel
(venv) $ python -m ipykernel install --user --name adapt --display-name ADAPT
```
Now, you can see the ```ADAPT``` entry when you create a new notebook.

## Tutorial
ADAPT offeres some tutorials at [tutorial](tutorial).

## Issues
We are welcome any issues. Please, leave them in the Issues tab.

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