Comments (2)
Genetic Algorithm to discover and construct neural network model from the scratch to solve the problem would be perfect scientific publications topic as well.
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Lux already uses multiple cores by default. Note that it won't always use multiple-cores since parallelizing has an inherent cost associated with it, so it will only parallelize if there is a benefit.
Distributed computing is covered in this part of the documentation: https://lux.csail.mit.edu/stable/manual/distributed_utils. Note that distributing on heterogeneous architecture is not as simple as letting them run of the computers, since you need to handle load balancing else the computation is bottlenecked by the slowest machines.
Genetic Algorithm to discover and construct neural network model from the scratch to solve the problem would be perfect scientific publications topic as well.
This particular area is called Neural Architecture Search. It is a pretty interesting domain, though I am not sure if anyone has use Lux for such applications. Would be cool to try it out for sure.
I am going to close this issue, please open a new issue with a concrete example of a model where Lux wasn't using multiple cores.
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Related Issues (20)
- Error in `compute_gradients` when loss already has a `Zygote.gradient` HOT 6
- NCCL Complex wrapper HOT 2
- Drop `Tracker.jl` support for SimpleChains
- Feature request: TimeDistributed Layer HOT 1
- Feature Request: Allow recurrent layers with 2D input (features * seq_length), even if the order is BatchLastIndex HOT 2
- DifferentiationInterface testing HOT 10
- Missing statistics tracking in normalization layers
- unexpected parameter type for AbstractExplicitContainer with single trainable field HOT 3
- Test with DispatchDoctor error mode
- `BFloat16` Support
- Change defaults for Layers to match Pytorch HOT 1
- Gradient checkpointing/ rematerialization
- how to use Lux.jl utility 'BinaryCrossEntropy'
- Mixed-Precision Matrix Multiply Performance Regression HOT 28
- Lux.testmode not updating state for BatchNorm layers for nested models? HOT 2
- Add Float128 support HOT 1
- `Enzyme.Forward` hits Octavian dispatch in Dense HOT 9
- Move uncommon layers to Boltz.jl
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