A graphical toolbox for training, evaluating and deploying state-of-the-art AI models

DashAI needs Python 3.8 or greater to be installed. Once that requirement is satisfied, you can install DashAI via pip:

$ pip install dashai

Then, to initialize the server and the graphical interface, run:

$ dashai

Finally, go to http://localhost:3000/ in your browser to access to the DashAI graphical interface.

Some datasets you can use to try DashAI are available here.

To download and run the development version of DashAI, first, download the repository and switch to the developing branch: :

$ git clone https://github.com/DashAISoftware/DashAI.git
$ git checkout develop

$ cd DashAI/front

1. Install the LTS node version.
2. Install Yarn package manager following the instructions located on the yarn getting started page.
3. Move to DashAI/front and Install the project packages using yarn:

$ cd DashAI/front
$ yarn install

Move to DashAI/front if you are not on that route:

$ cd DashAI/front

Then, launch the front-end development server by running the following command:

$ yarn start

If you want to launch the front-end test server (without launching the backend) with dummy data, run:

$ yarn json-server

The project uses as default linter eslint with the react/recommended, standard-with-typescript` and prettier` styles.

To manually run the linter, move to DashAI/front and run:

$ yarn eslint src

The project uses prettier as default formatter.

To format the code manually, move to DashAI/front and execute:

$ yarn prettier --write src

Execute from DashAI/front:

$ yarn build

First, set the python enviroment using conda:

Then, move to DashAI/back

$ cd DashAI/back

Later, install the requirements:

$ pip install -r requirements.txt
$ pip install -r requirements-dev.txt

There are three ways to run DashAI:

1. By executing DashAI as a module:

$ python -m DashAI

2. Or, installing the default build:

$ pip install .
$ dashai

If you chose the second way, remember to install it each time you make changes.

Setting the local execution path

With the --local-path option you can determine where DashAI will save its local files, such as datasets, experiments, runs and others. The following example shows how to set the folder in the local .DashAI directory:

$ python -m DashAI --local-path "~/.DashAI"

Setting the logging level

Through the --logging_level parameter, you can set which logging level the DashAI backend server will have.

$ python -m DashAI --logging-level INFO

The possible levels available are: DEBUG, INFO, WARNING, ERROR, CRITICAL.

Note that the --logging-level not only affects the DashAI loggers, but also the datasets (which is set to the same level as DashAI) and the SQLAlchemy (which is only activated when logging level is DEBUG).

Checking Available Options

You can check all available options through the command:

$ python -m DashAI --help

DashAI uses pytest to perform the backend tests. To execute the backend tests

1. Move to DashAI/back

$ cd DashAI/back

2. Run:

$ pytest tests/

The project uses as default backend linter ruff:

To manually run the linter, move to DashAI/back and execute:

$ ruff .

The project uses black as default formatter.

To manually format the code, move to DashAI/back and execute:

$ black .

This project is sponsored by the National Center for Artificial Intelligence - CENIA (FB210017), and the Millennium Institute for Foundational Data Research - IMFD (ICN17_002).

The core of the development is carried out by students from the Computer Science Department of the University of Chile and the Federico Santa Maria Technical University.

To see the full list of contributors, visit in Contributors the DashAI repository on Github.