Comments (1)
The example name "heisenberg_2d_horvod_multy_gpu_fast_sampling.py" is slightly misleading, actually the parallelism is for the entire training steps (and not only the sampling).
I never tried to run Flowket on CPU clusters but from here it seem that it's possible.
Regarding the example, please look at the updated version that average the energy over the MPI cluster, if you plan to use
StochasticReconfiguration
you should add similar logic to ComplexValuesStochasticReconfiguration
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Related Issues (14)
- Tuple / List normalization issue HOT 1
- wrong max_number_of_local_connections fixing and continue recursively HOT 1
- Add a simple "wrapper"-like function or general class for easy autoregressive model HOT 1
- Observable design HOT 1
- J1J2 exact example leads to NaN values HOT 2
- Compatibility with TF2 HOT 4
- Tensorboard reporting samples instead of iterations HOT 1
- Tensorboard callback breaks on TF=1.14 (and probably TF2) HOT 2
- Break validation to steps instead of one large batch HOT 3
- Change name of project/package from PyKet to FlowKet in all relevant places HOT 2
- Stochastic reconfiguration for autoregressive models? HOT 3
- Cannot pip install flowket! HOT 2
- module named error HOT 9
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