Use your chosen compute infrastructure via a familar Jupyter interface
Dotscience stores snapshots of your model code and associated data. It also allows you to deploy any snapshot, and thus run the model version, on any compute infrastructure you choose. You could choose simply to run on your local machine – your laptop, or local server. But you might also want to point your model at a cloud instance on AWS, Google Compute Platform, or anywhere else on the web. This lets you take advantage of a varity of processing options without needing to send files around, or keep track of copies of your model code and training data.
You tell Dotscience to run on your chosen machine by running a single command on that machine. Then, you can develop and run your model via an IDE in the Dotscience web interface (currently Jupyterlab) or via your local development environment. The model will execute remotely on the specified runner, sending snapshots of code changes, as well as the value of syntactic objects such as parameters and summary statistics, back to Dotscience’s web interface for storage and visualisation.