DashAI 0.3.0

DashAI: a graphical toolbox for training, evaluating and deploying state-of-the-art AI models.

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

Quick installation (Pypi)

DashAI needs Python 3.10 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.

Test datasets

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

Development

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

Frontend

Warning

All commands executed in this section must be run from DashAI/front. To move there, run:

$ cd DashAI/front

Prepare the environment

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

Running the frontend

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

Backend

Prepare the environment

First, set the python enviroment, for that you can use conda:

Then, move to DashAI/back

$ cd DashAI/back

Later, install the requirements:

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

Running the Backend

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 . -e
$ dashai

Optional Flags

Setting the local execution path

With the –local-path (alias -lp) 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 (alias -ll) 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).

Disabling automatic browser opening

By default, DashAI will open a browser window pointing to the application after starting. If you prefer to disable this behavior, you can use the –no-browser (alias -nb) flag:

$ python -m DashAI --no-browser

Checking Available Options

You can check all available options through the command:

$ python -m DashAI --help

Testing

Execute tests

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/

Note

The database session is parametrized in every endpoint as db: Session = Depends(get_db) so we can test endpoints on a test database without making changes to the main database.

Acknowledgments

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.