Update SciMLSensitivity compats

LuxDL · May 14, 2024 · 8e2723c · 8e2723c
1 parent f0d2020
commit 8e2723c
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Showing 5 changed files with 8 additions and 6 deletions.
diff --git a/docs/run_single_tutorial.jl b/docs/run_single_tutorial.jl
@@ -10,6 +10,8 @@ output_directory = ARGS[2]
 path = ARGS[3]
 
 io = open(pkg_log_path, "w")
+Pkg.Registry.update()
+Pkg.update()
 Pkg.develop(; path=joinpath(@__DIR__, ".."), io)
 Pkg.instantiate(; io)
 close(io)

diff --git a/examples/GravitationalWaveForm/Project.toml b/examples/GravitationalWaveForm/Project.toml
@@ -25,4 +25,4 @@ LuxCUDA = "0.2, 0.3"
 Optimization = "3"
 OptimizationOptimJL = "0.1, 0.2"
 OrdinaryDiffEq = "6"
-SciMLSensitivity = "7"
+SciMLSensitivity = "7.57"
diff --git a/examples/NeuralODE/Project.toml b/examples/NeuralODE/Project.toml
@@ -27,6 +27,6 @@ MLUtils = "0.2, 0.3, 0.4"
 OneHotArrays = "0.1, 0.2"
 Optimisers = "0.2, 0.3"
 OrdinaryDiffEq = "6"
-SciMLSensitivity = "7.45"
+SciMLSensitivity = "7.57"
 Statistics = "1"
 Zygote = "0.6"
diff --git a/examples/PolynomialFitting/main.jl b/examples/PolynomialFitting/main.jl
@@ -74,12 +74,12 @@ vjp_rule = AutoZygote()
 function main(tstate::Lux.Experimental.TrainState, vjp, data, epochs)
     data = data .|> gpu_device()
     for epoch in 1:epochs
-        grads, loss, stats, tstate = Lux.Training.compute_gradients(
+        grads, loss, stats, tstate = Lux.Experimental.compute_gradients(
             vjp, loss_function, data, tstate)
         if epoch % 50 == 1 || epoch == epochs
             @printf "Epoch: %3d \t Loss: %.5g\n" epoch loss
         end
-        tstate = Lux.Training.apply_gradients!(tstate, grads)
+        tstate = Lux.Experimental.apply_gradients!(tstate, grads)
     end
     return tstate
 end

diff --git a/examples/SymbolicOptimalControl/Project.toml b/examples/SymbolicOptimalControl/Project.toml
@@ -30,7 +30,7 @@ Optimization = "3.24.3"
 OptimizationOptimJL = "0.2.3"
 OptimizationOptimisers = "0.2.1"
 OrdinaryDiffEq = "6.74.1"
-SciMLSensitivity = "7.56.2"
-Statistics = "1.11.1"
+SciMLSensitivity = "7.57"
+Statistics = "1.11"
 SymbolicRegression = "0.24.1"
 SymbolicUtils = "1.5.1"