DeriveRandomSeed¶
Derive a new random seed from an initial seed and some data.
This can be used to "specialize" a seed for a particular use to ensure unique but determinitic random numbers. An example is a deterministic parallel computation with N workers, where each worker needs a unique seed that is however derived from a common initial seed. The initial seed must have been generated using the Create Random Seed node. Version 0.5.0
Ports/Properties¶
key¶
Splittable random seed.
data¶
Data to fold into the seed.
algorithm¶
Random number generation algorithm for which to generate a seed. The default depends on the chosen backend, and not all backends support all algorithms. For this reason it is recommended to use the default.
backend¶
Optional compute backend for which to generate the seed. Keep is the current default, which is typically numpy, but which can be overridden in contexts that require it. Some backends (notably jax and tensorflow) require that the seed be generated using the same backend as the downstream random operation. For this reason, it is a good idea to use the same backend for both the seed management and the random operation to future-proof one's pipeline.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.