What are the differences between the Researcher, Team and Institution plan?
Each plan is built specifically for the research needs of an individual or group. The key differences between the Researcher plan versus the Team / Institution plans are the collaboration features, in-depth administration and reporting and the ability to group storage and compute into a single shared environment across multiple researchers or groups. The paid plans also include more storage and compute per user, allowing for higher aggregated totals due to the shared environment as well as single sign on.
Do I need an account to use Code Ocean?
You don't need an account to view or download any published code, data, or results. But, by signing up, you can run or augment published code or upload, run and publish your own.
What is included in the Researcher plan?
The researcher plan includes everything you need to explore, run and share code. Everyone is allotted 5GB of storage and 1 hour of compute time per month. Use your academic email to get 20GB of storage and 10 hours of compute time per month.
How is compute time measured?
Your compute time consists of the total time across all your runs, from start to finish (or cancellation). Time logged into the platform, or editing code without running it, will not count against your quota.
How is storage space measured?
All code, data (excluding datasets), and result files count against your storage quota prior to publication. Once published, they no longer count against your quota.
What if I'm running out of storage or compute time?
If you’re working on preparing your code and need more storage or compute time, please contact us. You can also purchase extra compute time at $1 per hour and extra storage at $5/mo for every 50GB. See machine specs here.
Which programming languages does the platform support?
C/C++, Fortran, Java, Julia, Lua, MATLAB, Octave, Perl, Python, Stata, R and any programming language available on Linux.
Do you support running code on the GPU?
Of course! Just pick the appropriate language (e.g. Python for TensorFlow, Lua for Torch, C++ for plain CUDA etc.) and select the base environment you want in the run environment dialog. Get more details here.
Who owns the rights to my code, and data?
You keep all rights to your content! You can choose under what license to publish your code so other users know how they can and cannot use it.