Deploying with Octue

Octue provides automated deployment to a cloud provider (like GCP or Azure), along with permissions and user management, monitoring, logging and data storage management out of the box.

There are also a whole bunch of collaborative helper tools, like the graphical twine builder and manifesting tools, designed to speed up the process of building and using twines.

The full set of services is in early beta, get in touch and we can help you architect systems - from small data services to large networks of Digital Twins.

Coming Soon - Deploying with doctue

Once we’ve bedded down our services internally at Octue, we’ll be open-sourcing more parts of our build/deploy process, including docker containers with pre-configured servers to run and monitor twine-based services and digital twins.

This will allow services to be easily spun up on GCP, Azure Digital Ocean etc., and be a nice halfway house between fully managed system on Octue and running your own webserver. Of course, without all the collaborative and data management features that Octue provides ;)

We’re looking for commercial sponsors for this part of the process - if that could be you, please get in touch

Deploying as a command-line application

Use the open-source octue app template as a guide. Write your new python code (or call your existing tools/libraries) within it. It’s set up to wrap and check configuration, inputs and outputs using twined. Follow the instructions there to set up your inputs, and your files, and run an analysis.

Deploying with your own web server

You can use any python based web server (need another language? see Language Choice):

  • Add configuration_values_data to your webserver config

  • Set up an endpoint to allow.

  • Set up an endpoint to handle incoming requests / socket messages - these will be input_values_data.

  • Treat these requests / messages as events which trigger a task.

  • In your task framework (e.g. your celery task), either:
    • Use twined directly to validate the input_values_data/output_values_data (and, on startup, the configuration_values_data) and handle running any required analysis yourself, or

    • import your analysis app (as built in Deploying as a command-line application) and call it with the configuration and input data in your task framework.

  • Return the result to the client.