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AntiNex Python client

Project description

AntiNex Python Client

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


pip install antinex-client

AntiNex Stack Status

AntiNex client is part of the AntiNex stack:



Docs Link

Docs Build


Travis Tests


Read the Docs REST API Tests

Core Worker

Travis AntiNex Core Tests


Read the Docs AntiNex Core Tests

Network Pipeline

Travis AntiNex Network Pipeline Tests


Read the Docs AntiNex Network Pipeline Tests

AI Utils

Travis AntiNex AI Utils Tests


Read the Docs AntiNex AI Utils Tests


Travis AntiNex Client Tests


Read the Docs AntiNex Client Tests

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:

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

git clone /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.


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
<     "publish_to_core": true,

Prepare a Dataset -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 <>

This include the model json or model description for the Keras DNN. -u root -p 123321 -i 4

Get Predictions Results for a Deep Neural Network

Get a user’s MLJobResult record by setting: -i <>

This includes predictions from the training or prediction job. -u root -p 123321 -i 4

Get a Prepared Dataset

Get a user’s MLPrepare record by setting: -i <> -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 -f examples/predict-rows-scaler-full-django.json


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


Run all

python test


flake8 .

pycodestyle .


Apache 2.0 - Please refer to the LICENSE for more details

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