aidkitcli 0.2.31

aidkit, the first aid kit for AI development, verification & validation. The AI-debugging &-boosting toolkit is both the embodiment of quality standards, as well as the plug & play tool for AI developers who want to put their model through its paces in every section of the AI lifecycle to reduce costs / iterations.

aidkit is the quality gate between machine learning models and the deployment of those models.

Installation

1. Activate your virtual environment with python 3.6, e.g. source venv/bin/activate
2. pip install aidkit

Example Usage

Authenticate

The only requirement for using aidkit is having a license for it.

To authenticate, you need to run the following once:

python -m aidkitcli.authenticate --url <subdomain>.aidkitcli.ai --token <your auth token>

Model

You can upload a model to aidkit, or list the names of all models uploaded.

For uploading, you need a keras .h5 file, that contains a LSTM architecture. Do the following to upload it:

python -m aidkitcli.model --file <path to your h5 file>

To list all uploaded models type:

python -m aidkitcli.model

Data

You can upload a data set to aidkit, or list the names of all datasets uploaded.

For uploading, you need a zip file. We expect a zip, containing a folder, that is named like the dataset should be called. This subfolder contains INPUT and OUTPUT folders that each contain csv files. Do the following to upload it:

python -m aidkitcli.data --file <path to your zip file>

To list all uploaded datasets type:

python -m aidkitcli.data

Analysis

You can start a new quality analysis. For doing so, you need a toml file. This file will follow a specified toml standard. Do the following to upload it:

python -m aidkitcli.analysis --file <path to your toml file>

To list all uploaded datasets type:

python -m aidkitcli.analysis

Visualization

After running an analysis you can observe the results in our web-GUI. to get the link type:

python -m aidkitcli.url

Just follow the link and authorize yourself with your credentials.