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Python module for the CognitiveScale Cortex Cognitive Platform

Project description

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:


To install:

  > pip install cortex-python

or from source code:

  > git clone
  > cd cortex-python
  > pip install -e .

To install the optional components:

  > pip install cortex-python[viz]
  > pip install cortex-python[jupyter]
  > pip install cortex-python[builders]



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

  > virtualenv --python=python3.6 _venv
  > source _venv/bin/activate

Install developer dependencies:

  > git clone
  > cd cortex-python
  > make dev.install

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


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/
  • class method: pytest test/unit/

Publishing an alpha build

Suppose you want to release new functionality so it can be installed without releasing a new official version. We need to use an alpha version in PyPi.

  • we need to create and publish an alpha release:
  • get credentials to the cortex-python pypi CognitiveScale account (via lastpass)
  • run make dev.push. The alpha pre-release number (the N in X.Y.ZaN) with be determined automatically.


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 version and update the


Activate your virtual environment:

> source _venv/bin/activate

Setup 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.

Pre-release to staging

  1. Create and push an alpha release:
    > 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.

Migration steps from cortex-client to cortex-python

The cortex-python library and its optional add-ons are replacing the cortex-client library. The new libraries are more lightweight and use-case focussed. Cortex-python may be used for development with or without the add-ons.

Uninstall the previous library (cortex-client)

To use the new Cortex libraries, cortex-python and cortex-python-builders you must uninstall the cortex-client library; cortex-client and cortex-pythoncannot be installed simultaneously in your python environment.

  > pip uninstall cortex-client

Install cortex-python

To install:

  > pip install cortex-python

To install the optional components:

  > pip install cortex-python[viz]
  > pip install cortex-python[jupyter]
  > pip install cortex-python[builders]

Import Client functionalities

The way Client functionalities can be imported has changed.

To import cortex :

> import cortex

To import ConnectionClient :

> from cortex.connection import ConnectionClient

Upload to Managed Content

Use ManagedContentClient to upload and download to your account's managed content. In cortex-client, ConnectionClient was used for these functionalities. The methods to upload and download remain the same.

To import ManagedContentClient:

> from cortex.content import ManagedContentClient

ConnectionClient can be used to save and retrieve connections.

Use Cortex magics

Cortex magics can be used only when the optional builders dependency is installed:

> %reload_ext cortex_builders

Run in local (out of cluster) environment:

APIs working with secrets are protected to work inside of cluster only. To test/run them outside the cluster, you need to set them up locally the same way you would in a dedicated instance. Follow local setup guide

Deprecations and Removals from cortex-client

  1. The InputMessage and OutputMessage classes have been deprecated. Instead use the Message class:
> from cortex import Message
  1. ModelClient, ModelProcess and ModelRouter have been deprecated. Instead use the experiment API in the Client class to run experiments, save and retrieve your models.

  2. JobsClient has been deprecated. Instead use the action API in Client class to save or retrieve actions. Also, you can use the action in the builder class inside client class to build your actions. (Can be used only when optional dependency of builders is installed)

  3. SecretsClient has been deprecated. There is no equivalent replacement functionality in the python library, but you can manage secrets through the Cortex Vault in the Cortex Console or via the CLI cortex variables [command] [options].

  4. Message.with_payload() has been removed. This method was previously deprecated in cortex-client v5.5.4. Instead use the Client.message() method:

> from cortex import Cortex

> cortex = Cortex.client()
> message = cortex.message(payload={'value': 'hello world'})
  1. LocalExperiment.set_pipeline() has been removed. This method was previously deprecated in cortex-client v5.5.0. There is no replacement method for this functionality.

  2. The cortex-python package depends on less libraries than cortex-client. Users of the cortex-client that have incrementally upgraded to different versions of the cortex-client package in the same environment are likely to have downloaded the following transitive dependencies. When cortex-python is used in place of cortex-client, these transitive dependencies will no longer be included. Accordingly, users that depend on any of the transitive dependencies must explicitly install them. The list of these transitive dependencies includes:


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