ADAM is ISI's effort under DARPA's Grounded Artificial Intelligence Language Acquisition (GAILA) program. Background for the GAILA program is given in DARPA's call for proposals and here is a video of a talk giving an overview of our plans for ADAM (aimed at an audience familiar with the GAILA program).
Documentation can be found here. The documentation includes tutorials on how to use ADAM (though it may not appear on Read the Docs).
You can check out our papers to read more about the system and to cite our work:
conda create --name adam python=3.7
followed by conda activate adam
.pip install -r requirements.txt
Make a file under parameters
called root.params
which contains:
adam_root: PATH_TO_WORKING_COPY_OF_THIS_REPO
# if you want to view experiment results in the UI, you must point adam_experiment_root to
# %adam_root%/data, otherwise any directory is fine:
adam_experiment_root: %adam_root%/data
# if you want to run the full pipeline which includes preprocessing:
stroke_python_root: /path/to/anaconda3/envs/adam_preprocessing
# for the segmentation part of the pipeline (ISI: 500{1,2,3} are in use):
segmentation_api_port: XXXX
conda_environment: adam
conda_base_path: PATH_TO_ANACONDA_DIRECTORY
conda install -c conda-forge pypy3.7
.make test
.To generate Sphinx documentation:
cd docs
make html
The docs will be under docs/_build/html
Run adam.curriculum_to_html /full/path/to/parameters/html/curriculum_to_html.phase1.params
Run adam.experiment.run_m9 /full/path/to/parameters/experiment/m9/m9.params
First, make sure the Flask backend is running. To start the backend:
cd adam/flask_backend
bash start_flask.sh &
The angular application is located under /angular-viewer/adam-angular-demo
Navigate to the directory above and serve the application locally on port 4200:
ng serve --open
npm install -g npm
cd {adam_root}
PYTHONPATH=. python scripts/install_fonts.py
For more detailed information including how to check npm versions on your machine, please refer: https://docs.npmjs.com/downloading-and-installing-node-js-and-npm
Currently, our curriculum dump and learner run in English by default, but they are also runnable in Chinese. Our Chinese implementation uses Yale romanization to maintain UTF-8 encoding, but this can easily be converted to the more common Pinyin romanization.
To generate a curriculum dump or run the learner in Chinese, add the following line to your parameters/root.params
file:
language_mode : CHINESE
and then run the commands given above.
Run make precommit
before commiting.
If you are using PyCharm, please set your docstring format to "Google" and your unit test runner to "PyTest" in
Preferences | Tools | Python Integrated Tools
.
Deniz Beser (beser@isi.edu
)
Joe Cecil (jcecil@isi.edu
)
Ryan Gabbard (ryan.gabbard@gmail.com
)
Jacob Lichtefeld (jacobl@isi.edu
)
The following papers have informed our design decisions and may be referenced in issues.