Additional tuning advice

docs/tuning.md

@@ -14,7 +14,7 @@ I run from IPython (Jupyter Notebooks don't work as well[^1]) on the head node o
 2. Use only the operators I think it needs and no more.
 3. Increase `populations` to `3*num_cores`.
 4. If my dataset is more than 1000 points, I either subsample it (low-dimensional and not much noise) or set `batching=True` (high-dimensional or very noisy, so it needs to evaluate on all the data).
-5. Set `ncyclesperiteration` to maybe `5000` or so, until the head node occupation is under `10%`.
+5. While on a laptop or single node machine, you might leave the default `ncyclesperiteration`, on a cluster with ~100 cores I like to set `ncyclesperiteration` to maybe `5000` or so, until the head node occupation is under `10%`. (A larger value means the workers talk less frequently to eachother, which is useful when you have many workers!)
 6. Set `constraints` and `nested_constraints` as strict as possible. These can help quite a bit with exploration. Typically, if I am using `pow`, I would set `constraints={"pow": (9, 1)}`, so that power laws can only have a variable or constant as their exponent. If I am using `sin` and `cos`, I also like to set `nested_constraints={"sin": {"sin": 0, "cos": 0}, "cos": {"sin": 0, "cos": 0}}`, so that sin and cos can't be nested, which seems to happen frequently. (Although in practice I would just use `sin`, since the search could always add a phase offset!)
 7. Set `maxsize` a bit larger than the final size you want. e.g., if you want a final equation of size `30`, you might set this to `35`, so that it has a bit of room to explore.
 8. I typically don't use `maxdepth`, but if I do, I set it strictly, while also leaving a bit of room for exploration. e.g., if you want a final equation limited to a depth of `5`, you might set this to `6` or `7`, so that it has a bit of room to explore.