cortex-python 7.0.0

Python module for the Tecnotree Sensa Platform

Python Module for the Cortex Cognitive Platform

The Cortex Python module provides an API client library to easily integrate with the Cortex Cognitive Platform. Refer to the Cortex documentation for details on how to use the library:

• Developer guide: https://cognitivescale.github.io/cortex-fabric/
• Cortex Python references: https://cognitivescale.github.io/cortex-python/master/

Installation

There are several installation options available:

• base library - for interacting with a Sensa cluster or developing skills

poetry add cortex-python

• model development - for feature and model developing within jupyter notebooks

poetry add cortex-python[model_development]

• model development extras - for developing model training and inference skills

poetry add cortex-python[model_runtime]

• Certifai Evaluator plugin - this must be installed with on of the model* extras NOTE: extra config needed to access the Sensa python repository

poetry config http-basic.sensa <USER> <TOKEN> 
poetry add cortex-python[model_development,certifai]

Install from source

git clone [e-mail]:CognitiveScale/cortex-python.git
cd cortex-python
# Needed for certifai components
poetry config http-basic.sensa <USER> <TOKEN> 
poetry install

Development

Setup

When developing, it's a best practice to work in a virtual environment. Create and activate a virtual environment:

poetry install
poetry shell

Install developer dependencies:

git clone [e-mail]:CognitiveScale/cortex-python.git
cd cortex-python
make dev.install

Run Developer test and linting tasks: Three types of checks are configured for this:

1. symilar - to test code duplication
2. pylint - for linting
3. pytest - for running the unit tests. These are orchestrated through tox. The tox configuration is available at tox.ini

There's a convenience Makefile that has commands to common tasks, such as build, test, etc. Use it!

Testing

Unit Tests

Follow above setup instructions (making sure to be in the virtual environment and having the necessary dependencies)

• make test to run test suite

To run an individual file or class method, use pytest. Example tests shown below:

• file: pytest test/unit/agent_test.py
• class method: pytest test/unit/agent_test.py::TestAgent::test_get_agent

Pre-release to staging

Note: this repository using git tag for versionning

1. Create and push an alpha release:

git tag -a 6.5.0a<N> -m 'alpha tag'
git push --tags
make dev.push

This will build an alpha-tagged package.

2. Merge develop to staging branch:

make stage

3. In GitHub, create a pull request from staging to master.

git tag -a 6.5.0 -m 'rlease tag'
git push --tags

Contributing

After contributing to the library, and before you submit changes as a PR, please do the following

1. Run unit tests via make test
2. Manually verification (i.e. try the new changes out in Cortex) to make sure everything is going well. Not required, but highly encouraged.
3. Bump up setup.py version and update the CHANGELOG.md

Documentation

Activate your virtual environment:

poetry shell

Set up your environment, if you have not done so:

make dev.install 

The package documentation is built with Sphinx and generates versioned documentation for all tag matching the release/X.Y.Z pattern and for the master branch. To build the documentation:

make docs.multi

The documentation will be rendered in HTML format under the docs/_build/${VERSION} directory.

Configuring the client

There are four mechanisms for configuring a client connection.

1. Use $HOME/.cortex/config file setup via cortex configure.

   from cortex.client import Cortex
   client=Cortex.client(verify_ssl_cert=True|False)
   

2. Use environment variables CORTEX_URL, CORTEX_TOKEN, and CORTEX_PROJECT

   from cortex.client import Cortex
   client=Cortex.client(verify_ssl_cert=True|False)
   

This is a table of supported environment variables.

These ENV vars are injected by the Sensa RUNTIME for use during skill startup.

Environment variables	Description
CORTEX_PERSONAL_ACCESS_CONFIG	JSON string containing a Pesonal Access Token obtained from the console UI
CORTEX_CONFIG_DIR	A folder containing the config created by the cortex configure command
CORTEX_TOKEN	A JWT token generate by cortex configure token or provided during skill invokes
CORTEX_PROJECT	Project used during api requests
CORTEX_URL	URL for Sensa apis

3. Use method kwargs with Cortex.client()

   from cortex.client import Cortex
   client=Cortex.client(api_endpoint="<URL>", token="<token>", project="<project>", verify_ssl_cert=True|False)
   

4. Using the skills invoke message, this allows for authentication using the caller's identity.

   from cortex.client import Cortex
   client=Cortex.from_message({})