jay-johnson/antinex-client

AntiNex python client for training and using pre-trained deep neural networks with JWT authentication

AntiNex Python Client

Python API Client for training deep neural networks with the REST API running

https://github.com/jay-johnson/train-ai-with-django-swagger-jwt

Install

pip install antinex-client

AntiNex Stack Status

AntiNex client is part of the AntiNex stack:

Component	Build	Docs Link	Docs Build
REST API		Docs
Core Worker		Docs
Network Pipeline		Docs
AI Utils		Docs
Client		Docs

Run Predictions

These examples use the default user root with password 123321. It is advised to change this to your own user in the future.

Train a Deep Neural Network with a JSON List of Records

ai -u root -p 123321 -f examples/predict-rows-scaler-django-simple.json

Train a Deep Neural Network to Predict Attacks with the AntiNex Datasets

Please make sure the datasets are available to the REST API, Celery worker, and AntiNex Core worker. The datasets are already included in the docker container ai-core provided in the default compose.yml file:

https://github.com/jay-johnson/train-ai-with-django-swagger-jwt/blob/51f731860daf134ea2bd3b68468927c614c83ee5/compose.yml#L53-L104

If you're running outside docker make sure to clone the repo with:

git clone https://github.com/jay-johnson/antinex-datasets.git /opt/antinex/antinex-datasets

Train the Django Defensive Deep Neural Network

Please wait as this will take a few minutes to return and convert the predictions to a pandas DataFrame.

ai -u root -p 123321 -f examples/scaler-full-django-antinex-simple.json

...

[30200 rows x 72 columns]

Using Pre-trained Neural Networks to make Predictions

The AntiNex Core manages pre-trained deep neural networks in memory. These can be used with the REST API by adding the "publish_to_core": true to a request while running with the REST API compose.yml docker containers running.

Run:

ai -u root -p 123321 -f examples/publish-to-core-scaler-full-django.json

Here is the diff between requests that will run using a pre-trained model and one that will train a new neural network:

antinex-client$ diff examples/publish-to-core-scaler-full-django.json examples/scaler-full-django-antinex-simple.json
5d4
<     "publish_to_core": true,
antinex-client$

Prepare a Dataset

ai_prepare_dataset.py -u root -p 123321 -f examples/prepare-new-dataset.json

Get Job Record for a Deep Neural Network

Get a user's MLJob record by setting: -i <MLJob.id>

This include the model json or model description for the Keras DNN.

ai_get_job.py -u root -p 123321 -i 4

Get Predictions Results for a Deep Neural Network

Get a user's MLJobResult record by setting: -i <MLJobResult.id>

This includes predictions from the training or prediction job.

ai_get_results.py -u root -p 123321 -i 4

Get a Prepared Dataset

Get a user's MLPrepare record by setting: -i <MLPrepare.id>

ai_get_prepared_dataset.py -u root -p 123321 -i 15

Using a Client Built from Environment Variables

This is how the Network Pipeline streams data to the AntiNex Core to make predictions with pre-trained models.

Export the example environment file:

source examples/example-prediction.env

Run the client prediction stream script

ai_env_predict.py -f examples/predict-rows-scaler-full-django.json

Development

virtualenv -p python3 ~/.venvs/antinexclient && source ~/.venvs/antinexclient/bin/activate && pip install -e .

Testing

Run all

python setup.py test

Linting

flake8 .

pycodestyle .

License

Apache 2.0 - Please refer to the LICENSE for more details