Comments (2)
sub_named_parameters
can only bring advantages when begin_virtual
is called (when surrogate steps > 0). The overhead of computing sub_named_parameters
for every named_parameters
call is around 1/20 for a 300-hid size 8-step per cell 1 layer random sampled RNN discrete arch. So... this might not be a performance issue. Maybe the speed up brought by this mechanism by reducing the cloning is also not big...so the choices of mask or not and cache or not might not have a big influence...
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Related Issues (20)
- Hardware: Profiling Network Generation Problem
- About FTT-NAS GPU search problem... HOT 3
- I seem to found a small bug in your FTT-NAS project... HOT 4
- After you revised the project last time, I tried to run FTT-NAS search. It was normal at first, but after 10epochs were found, an error was reported when running the test() method... HOT 3
- ray_dispatcher
- ray version HOT 1
- Adversarial Supernet training HOT 5
- Similar DAG encoding scheme to an existing paper HOT 1
- Generate sample config error and FBNet support? HOT 1
- nasbench.api.OutOfDomainError: unsupported op none HOT 1
- The program is stuck in an endless loop, when run awnas search examples/nasbench/nasbench-101_gates_sa.yaml HOT 1
- How to tune each rollout and how to use the Pareto-based EA HOT 2
- In aw_nas/aw_nas/objective/zerocost.py, the module foresight cannot be imported. HOT 2
- How to change the environment variable “AWNAS_HOME“ HOT 2
- Missing data dumping code for NAS-Bench-201 in gates. HOT 2
- Question about the supernet training?
- question about supernet training.
- Code for TA-GATES HOT 3
- Regrading TA-GATES dataset HOT 1
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