Comments (3)
rcherukuri12
commented on May 18, 2024
1
updated version works.
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mcabbott
commented on May 18, 2024
Note that v0.13.17 is quite far from the latest, released 17 Jun 2023. This may mean you have ancient versions of some packages... I would try ] add [email protected] before anything else.
Then, can you post what errors you see, say from this http://fluxml.ai/Flux.jl/stable/models/quickstart/ ? Without a stack trace it's very hard to guess.
https://github.com/jondeuce/Ignite.jl looks neat, but appears to be at a higher level -- its example still calls setup(Adam(1f-3), model) so that has to work first.
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rcherukuri12
commented on May 18, 2024
Thank you ...I switched and was able to run. Flux.setup(...) was the one that was blowing up in that version.
Now I have to go back and see if I can run ReinforcementLearning.jl
closing this issue.
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Related Issues (20)
- Dimensions check for `Conv` is incomplete, leading to confusing error HOT 1
- 2x performance regression due to 5e80211c3302b5e7b79b4f670498f5a68af6659b HOT 2
- Why is Flux.destructure type unstable? HOT 3
- bad formatting for PairwiseFusion docstring HOT 1
- Zero-sized arrays cannot be applied to Dense layers. HOT 4
- Adding Simple Recurrent Unit as a recurrent layer
- Collecting PyTorch -> Flux migration notes
- tests are failing due to ComponentArrays HOT 2
- deprecate Flux.params HOT 7
- Significant time spent moving medium-size arrays to GPU, type instability HOT 10
- ConvTranspose errors with symmetric non-constant pad
- SamePad() for even sized filters.
- Dense layers with shared parameters HOT 5
- Implementation of `AdamW` differs from PyTorch HOT 10
- `gpu` should warn if cuDNN is not installed HOT 2
- Cannot take `gradient` of L2 regularization loss HOT 1
- Create a flag to use Enzyme as the AD in training/etc. HOT 13
- test Enzyme gradient for loss functions
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