Comments (2)
@vpuri3 what versions are you on? (show the manifest versions of the following packages)
using LuxDeviceUtils, ComponentArrays
using WeightInitializers
using LuxCore, LuxCUDA
nt = (; a=ones32(2), b=rand32(2))
st = (; layer=(; frozen_params=ComponentArray(nt), states=(;)))
LuxCore.trainmode(gpu_device()(st))
Status `/mnt/research/ongoing/lux/Project.toml`
[b0b7db55] ComponentArrays v0.15.3
[b2108857] Lux v0.5.7 `Lux.jl`
[d0bbae9a] LuxCUDA v0.3.1
[bb33d45b] LuxCore v0.1.6
[34f89e08] LuxDeviceUtils v0.1.10 `LuxDeviceUtils.jl`
[82251201] LuxLib v0.3.7 `LuxLib.jl`
[d49dbf32] WeightInitializers v0.1.1
[e88e6eb3] Zygote v0.6.66
from lux.jl.
Thanks avik, I was using CUDA 4 due to an outdated compat. this works fine with CUDA 5.
from lux.jl.
Related Issues (20)
- Meta Issue for proper Enzyme Integration into Lux HOT 3
- Meta-Issue for improvements to `@compact`
- Rework ChainRules for DynamicExpressions HOT 1
- Nested AD for Parameter Gradient/Jacobian HOT 7
- Pullback over twice jacobian HOT 9
- Rewrite `@layer_map` to use KeyPath from Functors
- Extracting part of a model, with the corresponding parameters and states HOT 1
- Serializing/Deserializing Lux Models
- Differentiating `Zygote.pullback` HOT 9
- Batched Jacobian Functions
- Error for JVP by Enzyme HOT 16
- [Nested AD] Incorrect gradient when taking a gradient over a gradient using StatefulLuxLayer HOT 7
- batched_jacobian + CUDA => InvalidIRError HOT 2
- Add a compiled tape version for ReverseDiff
- SimpleChains integration doesn't work with Enzyme HOT 3
- Simple MLP requires Enzyme runtimeActivity HOT 2
- Using `swish` as `Conv` activation function errors on the GPU HOT 1
- Fast activation error HOT 1
- Definition and implementation of 'Loss' in Linear Regression Tutorial "Julia & Lux for the Uninitiated" HOT 2
- Add improper qualified accesses checks
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from lux.jl.