User interface improvements #34
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## master #34 +/- ##
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+ Coverage 52.97% 78.13% +25.15%
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Lines 6233 6307 +74
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+ Hits 3302 4928 +1626
+ Misses 2931 1379 -1552
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I think this is ready to be merged. |
Higgcz
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This commit brings various changes and
improvements to the API of EvoTorch.
- All pre-defined problem classes now have
consistent default configuration:
`num_actors=None`, `num_gpus_per_actor=None`.
- New decorators:
- `@on_device(device)`: Decorates a fitness
function to make the associated problem
object become aware of the device on which
the evaluations are meant to happen
- `@on_cuda(i)`: Decorates a fitness function
to make the associated problem object
aware of the CUDA device on which the
evaluations are meant to happen
- `@on_aux_device`: Decorates a fitness
function to make the associated problem
object become aware that the evaluations
are meant to happen on the auxiliary device.
- `@vectorized`: Decorates a fitness function
to make the associated problem object become
aware that the fitness function expects
multiple solutions and returns multiple
fitnesses.
- The documentation is updated and improved.
- Quickstart example with different search algorithms - Simple Neuroevolution example - Problem definition of Neuroevolution for Gym example
This commit makes the API of `Problem` consistent with its docstring in this way: one can now omit `initial_bounds` and override the `_fill(...)` method for defining how the solutions should be initialized. Before this commit, one would have to specify dummy values for `initial_bounds` when using a `dtype` other than `object`. Unit tests are updated and improved regarding the API improvement mentioned above. This commit also brings new unit tests for the namespace `evotorch.optimizers`.
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This pull request aims to bring various changes and improvements to the API of EvoTorch.
num_actors=None,num_gpus_per_actor=None.@on_device(device): Decorates a fitness function to make the associated problem object become aware of the device on which the evaluations are meant to happen@on_cuda(i): Decorates a fitness function to make the associated problem object aware of the CUDA device on which the evaluations are meant to happen@on_aux_device: Decorates a fitness function to make the associated problem object become aware that the evaluations are meant to happen on the auxiliary device.@vectorized: Decorates a fitness function to make the associated problem object become aware that the fitness function expects multiple solutions and returns multiple fitnesses.