Workspaces
At HUB Ocean we use python extensively, it's a great language for data science, and we have made a Python SDK to help you interact with our platform.
We have a JupyterLab environment close to our data. Here you can use any python tools you like!
What does 'close to data' mean?
"Close to data" means that the workspaces environment is located in the same data center as our data. This means that you can work with large datasets without having to download them to your local machine. And the bandwidth between our platform and workspaces is much higher than your home internet connection - presumably.
Log in with your user, and select the amount of resources you want your workspace to have.
Your notebook will run on our servers. Choose the resources suited for your project.
Tip
Start out with a minimal for testing, and then scale up as you need. Your files will remain the same.
Tip
Tokens in the workspaces are for communicating with JupyterHub's REST API, not for communicating with the HUB Ocean platform. Jupyter Hub's REST API Guide
If you're a power user and need more processing power, you can use Dask to scale up your processing power even more. All users have access to a minimal set of Dask clusters. If you need more Dask processing power, please reach out.
Dask has a great list of examples to get you started.
Our workspaces documentation contains more information on using Dask in our Workspaces.
That was it for the getting started guide. If you have any questions, don't hesitate to ask us in our Slack community.
Check out the more in-depth guides and references for more information on how to use our platform.