Owkin Connect’s Python library enables users to launch federated computations, programmatically register and manage assets (datasets, algorithms, metrics, etc.) on the platform and analyze results. Any Python code and dependency can operate on the platform in the form of a self-contained docker container, therefore data scientists are able to use any machine learning libraries (Tensorflow, PyTorch, Scikit-learn, etc.).

The Owkin Connect library ships with extensive documentation and tutorials.

One can also use Owkin Connect with a Command Line Interface (CLI). This is particularly useful to monitor training tasks or to list assets registered on the platform. The CLI and the Python library provide the same functionalities, therefore users are free to use the tool with which they feel the most comfortable.

The Owkin Connect Command Line Interface (CLI).

​​Owkin Connect’s Graphical User Interface shows an overview of the network: how many nodes are there, and which compute plans are running on them. We define compute plan as a set of training, aggregation and testing tasks gathered together towards building one or multiple final models. The users can follow the progression of the compute plans. Then, once a model training is complete, they can review the history of the compute plan, step by step, as well as visualize the performance of their models. Through the interface, the traceability features of the software allow you to dive into all the assets that have been already registered.

Users can follow the progression of the compute plans. Then, once a model training is complete, they can review the history of the compute plan, step by step, as well as visualize the performance of their models.

To be robust and performant, Owkin Connect makes it possible to train predictive models on heterogeneous data to generalize well to different patient populations, treatment plans, and different data modalities such as histology slides and genomic data. Consequently, it is possible to register any type of data to the platform: tabular data, imaging, histology, -omics, text, videos, etc. Data needs to be copied into an Owkin Connect node. Connections between any database and Owkin Connect can be set up using Python code.

Data can be registered using either the CLI or the Python library.

Data harmonization between nodes is a major challenge in federated learning, especially in a blind setting where partners of a network cannot access or visualize the datasets of the other partners. If data scientists have remote access to every node individually (i.e. they can not move the data but can connect to a server where the data is), they can then look at the data and train local models before training a federated model. If they don’t have remote access to every node, data harmonization becomes a challenging task but still completely feasible (it has been done in different projects led by Owkin, including MELLODDY). What can help them? A data template so that data scientists know that every dataset follows the same template. Data engineers can also produce synthetic data that follows the same template to share with data scientists.

Users can easily design their own federated strategies, and Owkin Connect’s Python library is equipped with ready-to-run federated strategies such as:

The graphical user interface of Owkin Connect allows you to track model training steps, with performance indicators at each aggregation. Once a model training is complete, users can review the history of the compute plan, step by step, as well as visualize the performance of the federated model vs local individual models. Users can later download models trained on the platform if all the participants (data and algorithm providers) give permission.