Fully remove OptimizationFlux

SciML · Nov 6, 2023 · 11c0734 · 11c0734
1 parent 39101bf
commit 11c0734
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Showing 13 changed files with 14 additions and 18 deletions.
diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
@@ -26,7 +26,6 @@ jobs:
  - SDE2
  - SDE3
  version:
- - '1.6'
  - '1'
  steps:
  - uses: actions/checkout@v4

diff --git a/.github/workflows/Downstream.yml b/.github/workflows/Downstream.yml
@@ -14,7 +14,7 @@ jobs:
  strategy:
  fail-fast: false
  matrix:
- julia-version: [1, 1.6]
+ julia-version: [1]
  os: [ubuntu-latest]
  package:
  - {user: SciML, repo: DiffEqFlux.jl, group: All}

diff --git a/Project.toml b/Project.toml
@@ -81,7 +81,7 @@ Tracker = "0.2"
 TruncatedStacktraces = "1.2"
 Zygote = "0.6"
 ZygoteRules = "0.2"
-julia = "1.6"
+julia = "1.9"
 
 [extras]
 AlgebraicMultigrid = "2169fc97-5a83-5252-b627-83903c6c433c"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -19,7 +19,6 @@ MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
-OptimizationFlux = "253f991c-a7b2-45f8-8852-8b9a9df78a86"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
@@ -58,7 +57,6 @@ MLDatasets = "0.7"
 NNlib = "0.8, 0.9"
 Optimisers = "0.2, 0.3"
 Optimization = "3.9"
-OptimizationFlux = "0.1"
 OptimizationNLopt = "0.1"
 OptimizationOptimJL = "0.1"
 OptimizationOptimisers = "0.1"

diff --git a/docs/src/examples/neural_ode/simplechains.md b/docs/src/examples/neural_ode/simplechains.md
@@ -8,8 +8,7 @@ First, we'll need data for training the NeuralODE, which can be obtained by solv
 
 ```@example sc_neuralode
 using SimpleChains,
- StaticArrays, OrdinaryDiffEq, SciMLSensitivity, Optimization,
- OptimizationFlux, Plots
+ StaticArrays, OrdinaryDiffEq, SciMLSensitivity, OptimizationOptimisers, Plots
 
 u0 = @SArray Float32[2.0, 0.0]
 datasize = 30

diff --git a/docs/src/examples/ode/exogenous_input.md b/docs/src/examples/ode/exogenous_input.md
@@ -41,7 +41,7 @@ used as an input into the neural network of a neural ODE system.
 
 ```@example exogenous
 using DifferentialEquations, Lux, ComponentArrays, DiffEqFlux, Optimization,
- OptimizationPolyalgorithms, OptimizationFlux, Plots, Random
+ OptimizationPolyalgorithms, OptimizationOptimisers, Plots, Random
 
 rng = Random.default_rng()
 tspan = (0.1f0, Float32(10.0))

diff --git a/docs/src/examples/ode/second_order_adjoints.md b/docs/src/examples/ode/second_order_adjoints.md
@@ -14,7 +14,7 @@ with Hessian-vector products (never forming the Hessian) for large parameter
 optimizations.
 
 ```@example secondorderadjoints
-using Flux, DiffEqFlux, Optimization, OptimizationFlux, DifferentialEquations,
+using Flux, DiffEqFlux, Optimization, OptimizationOptimisers, DifferentialEquations,
  Plots, Random, OptimizationOptimJL
 
 u0 = Float32[2.0; 0.0]

diff --git a/docs/src/examples/ode/second_order_neural.md b/docs/src/examples/ode/second_order_neural.md
@@ -22,7 +22,7 @@ An example of training a neural network on a second order ODE is as follows:
 
 ```@example secondorderneural
 using DifferentialEquations,
- Flux, Optimization, OptimizationFlux, RecursiveArrayTools,
+ Flux, Optimization, OptimizationOptimisers, RecursiveArrayTools,
  Random
 
 u0 = Float32[0.0; 2.0]

diff --git a/docs/src/examples/optimal_control/optimal_control.md b/docs/src/examples/optimal_control/optimal_control.md
@@ -36,7 +36,7 @@ will first reduce control cost (the last term) by 10x in order to bump the netwo
 of a local minimum. This looks like:
 
 ```@example neuraloptimalcontrol
-using Flux, DifferentialEquations, Optimization, OptimizationNLopt, OptimizationFlux,
+using Flux, DifferentialEquations, Optimization, OptimizationNLopt, OptimizationOptimisers,
  SciMLSensitivity, Zygote, Plots, Statistics, Random
 
 rng = Random.default_rng()

diff --git a/docs/src/examples/sde/optimization_sde.md b/docs/src/examples/sde/optimization_sde.md
@@ -86,7 +86,7 @@ end
 We can then use `Optimization.solve` to fit the SDE:
 
 ```@example sde
-using Optimization, Zygote, OptimizationFlux
+using Optimization, Zygote, OptimizationOptimisers
 pinit = [1.2, 0.8, 2.5, 0.8, 0.1, 0.1]
 adtype = Optimization.AutoZygote()
 optf = Optimization.OptimizationFunction((x, p) -> loss(x), adtype)
@@ -121,7 +121,7 @@ In this example, we will find the parameters of the SDE that force the
 solution to be close to the constant 1.
 
 ```@example sde
-using DifferentialEquations, DiffEqFlux, Optimization, OptimizationFlux, Plots
+using DifferentialEquations, DiffEqFlux, Optimization, OptimizationOptimisers, Plots
 
 function lotka_volterra!(du, u, p, t)
  x, y = u

diff --git a/docs/src/tutorials/data_parallel.md b/docs/src/tutorials/data_parallel.md
@@ -83,7 +83,7 @@ The following is a full copy-paste example for the multithreading.
 Distributed and GPU minibatching are described below.
 
 ```@example dataparallel
-using DifferentialEquations, Optimization, OptimizationFlux
+using DifferentialEquations, Optimization, OptimizationOptimisers
 pa = [1.0]
 u0 = [3.0]
 θ = [u0; pa]
@@ -198,7 +198,7 @@ using Distributed
 addprocs(4)
 
 @everywhere begin
- using DifferentialEquations, Optimization, OptimizationFlux
+ using DifferentialEquations, Optimization, OptimizationOptimisers
  function f(u, p, t)
  1.01u .* p
  end
@@ -251,7 +251,7 @@ The following is an example of minibatch ensemble parallelism across
 a GPU:
 
 ```julia
-using DifferentialEquations, Optimization, OptimizationFlux, DiffEqGPU
+using DifferentialEquations, Optimization, OptimizationOptimisers, DiffEqGPU
 function f(du, u, p, t)
  @inbounds begin
  du[1] = 1.01 * u[1] * p[1] * p[2]

diff --git a/docs/src/tutorials/training_tips/divergence.md b/docs/src/tutorials/training_tips/divergence.md
@@ -29,7 +29,7 @@ A full example making use of this trick is:
 
 ```@example divergence
 using DifferentialEquations,
- SciMLSensitivity, Optimization, OptimizationFlux,
+ SciMLSensitivity, Optimization, OptimizationOptimisers,
  OptimizationNLopt, Plots
 
 function lotka_volterra!(du, u, p, t)

diff --git a/docs/src/tutorials/training_tips/local_minima.md b/docs/src/tutorials/training_tips/local_minima.md
@@ -18,7 +18,7 @@ on `(0,5.0)`. Naively, we use the same training strategy as before:
 ```@example iterativefit
 using DifferentialEquations,
  ComponentArrays, SciMLSensitivity, Optimization,
- OptimizationFlux
+ OptimizationOptimisers
 using Lux, Plots, Random
 
 rng = Random.default_rng()