Frequently Asked Questions

If you have any questions or problem and you can’t find the answer in the documentation, feel free to send an e-mail to support@lighton.io. An engineer will answer as soon as possible.

General questions

  • The press release says PyTorch and Scikit-learn support. Does this mean general support?

Our API offers scikit-learn interface, and a PyTorch one. To see how to use them check out the examples.

  • Is it going to be as easy as changing from CPU to GPU?

LightOn Cloud will feature a VM where customers will have access to a CPU, a GPU and LightOn’s OPU. A simple function call will allow computation to be performed on LightOn OPU.

  • How is the IP treated for algorithms running on your systems?

Any algorithm running on LightOn Cloud is the property of the user. LightOn makes no claim whatsoever on the IP generated by users while using LightOn Cloud.

  • Is it possible to combine GPU and OPU calculations?

One can combine GPU and OPU computation in the same way as GPU and CPU: by running a part of the computations on the GPU and a part on the OPU. Note that the operation performed by the OPU is not differentiable though.

Common errors

  • If you get one of the following errors, there is a hardware issue, please contact support: support@lighton.io

RuntimeError: Error getting image at 0. : Timeout
RuntimeError: Starting system: Unknown error -1
  • If I compute a random projection to dimension 10000, can I take the first 1000 columns to obtain a projection of dimension 1000?

Yes, subsampling any N columns from a projection to a bigger dimension is the same as performing a random projection to dimension N.