In Datalore you can edit notebooks with your colleagues in real time. Split work into worksheets or even get together on the same code cell. When editing, you will see live changes to the code. Environment, data, and computation state are shared automatically.
You can share notebooks simply by using a link or email invitation and setting the level of access as either view or edit. Collaborators can access notebooks together with you in real time and when you are offline.
You can navigate to a collaborator's cursor and follow along in real time simply by clicking on the collaborator’s account icon in the upper right corner.
Get a fully collaborative experience by editing Python scripts and other files attached to the notebook together with your team members. You’ll be able to see collaborators' cursors in the right-hand sidebar editor and get real-time updates to the files’ contents.
Organize team projects in workspaces to keep everyone on the same page. Access your notebooks, data, and reports from a single place and get real-time updates for them – no more out-of-sync notebooks forgotten on local machines. For personal experiments, every user gets a Home workspace.
When sharing a notebook or a workspace, you can configure access rights. View access allows a user only to view the contents of the notebook and workspace, while edit access allows full access to the notebook, including changing code, running cells, and starting computations.
Track your team’s work progress by creating history checkpoints, see the differences between versions, and revert to previous checkpoints at any time. For actions such as deleting a sheet or a cell, checkpoints will be created automatically.
Different members of your data science team might need different computational resources. Datalore allows you to create separate usage plans for those who require high-efficiency GPUs and for those who need a lot of regular CPU computations, for example. You can specify how many hours each user is allowed to run each machine type, and restrict some users from running high-cost machines.