Comments (2)
That's a good question. In general the DeepSymbolicOptimizer
object is not thread safe--tensorflow
does not make creating multiple TF-based objects like this easy.
One option is to use the n_cores_batch
parameter. Your multiple training runs will still run in series, but each one will leverage multiple cores.
Otherwise, for batch tasks, you'll be better off parallelizing the entire program and having each one create a single DeepSymbolicOptimizer
object called with your different datasets.
from deep-symbolic-optimization.
Okay thank you for the explanation.
We tried parallelizing the entire program and restricting to one DeepSymbolicOptimizer object per process and that seems to be working.
from deep-symbolic-optimization.
Related Issues (20)
- Deap fitness objective HOT 1
- create primitive set mapping name clash HOT 2
- Deap varying with constraints HOT 4
- Methods for Running DSO HOT 4
- Normalization for input variables to domain (0,1) HOT 1
- Normalization for input variables to domain (0,1) HOT 1
- Different Learned Equation with Different Numpy Array Shape HOT 4
- Doubts about output HOT 1
- Ratio of training set to testing set HOT 1
- training iteration N, current best R : 0.9 HOT 1
- Trouble Installing, sample virtual environment config? HOT 7
- How to install configuration and generate an interactive platform? HOT 1
- Defining custom gaussian function HOT 3
- Ignoring errors... HOT 1
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from deep-symbolic-optimization.