Fully remove DiffEqFlux

SciML · May 20, 2024 · 4b7e599 · 4b7e599
1 parent e25a651
commit 4b7e599
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Showing 14 changed files with 26 additions and 351 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -5,7 +5,6 @@ DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
 DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
-DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 DiffEqNoiseProcess = "77a26b50-5914-5dd7-bc55-306e6241c503"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
@@ -16,7 +15,6 @@ IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
 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"
@@ -41,7 +39,6 @@ DataInterpolations = "3.10, 4, 5"
 DelayDiffEq = "5"
 DelimitedFiles = "1"
 DiffEqCallbacks = "2.24, 3"
-DiffEqFlux = "3"
 DiffEqNoiseProcess = "5.14"
 DifferentialEquations = "7"
 Documenter = "1"
@@ -52,7 +49,6 @@ IterTools = "1"
 Lux = "0.5.7"
 LuxCUDA = "0.3"
 Optimization = "3.9"
-OptimizationFlux = "0.2"
 OptimizationNLopt = "0.2"
 OptimizationOptimJL = "0.2, 0.3"
 OptimizationOptimisers = "0.2"

diff --git a/docs/pages.jl b/docs/pages.jl
@@ -16,7 +16,6 @@ pages = ["index.md",
         "Neural Ordinary Differential Equations (Neural ODE)" => Any[
             "examples/neural_ode/neural_ode_flux.md",
             "examples/neural_ode/simplechains.md",
-            "examples/neural_ode/neural_gde.md",
             "examples/neural_ode/minibatch.md"],
         "Stochastic Differential Equations (SDEs)" => Any[
             "examples/sde/optimization_sde.md",

diff --git a/docs/src/Benchmark.md b/docs/src/Benchmark.md
@@ -37,8 +37,7 @@ Quick summary:
   - `ReverseDiffVJP(compile = true)` works well for small Lux neural networks
 
 ```julia
-using DiffEqFlux,
-      OrdinaryDiffEq, Lux, SciMLSensitivity, Zygote, BenchmarkTools, Random, ComponentArrays
+using OrdinaryDiffEq, Lux, SciMLSensitivity, Zygote, BenchmarkTools, Random, ComponentArrays
 
 u0 = Float32[2.0; 0.0]
 datasize = 30

diff --git a/docs/src/examples/dae/physical_constraints.md b/docs/src/examples/dae/physical_constraints.md
@@ -10,7 +10,7 @@ terms must add to one. An example of this is as follows:
 
 ```@example dae
 using SciMLSensitivity
-using Lux, ComponentArrays, DiffEqFlux, Optimization, OptimizationOptimJL,
+using Lux, ComponentArrays, Optimization, OptimizationOptimJL,
       OrdinaryDiffEq, Plots
 
 using Random
@@ -74,7 +74,7 @@ result_stiff = Optimization.solve(optprob, OptimizationOptimJL.BFGS(), maxiters
 
 ```@example dae2
 using SciMLSensitivity
-using Lux, ComponentArrays, DiffEqFlux, Optimization, OptimizationOptimJL,
+using Lux, ComponentArrays, Optimization, OptimizationOptimJL,
       OrdinaryDiffEq, Plots
 
 using Random

diff --git a/docs/src/examples/hybrid_jump/bouncing_ball.md b/docs/src/examples/hybrid_jump/bouncing_ball.md
@@ -2,7 +2,7 @@
 
 The bouncing ball is a classic hybrid ODE which can be represented in
 the [DifferentialEquations.jl event handling system](https://docs.sciml.ai/DiffEqDocs/stable/features/callback_functions/). This can be applied to ODEs, SDEs, DAEs, DDEs,
-and more. Let's now add the DiffEqFlux machinery to this
+and more. Let's now add the SciMLSensitivity machinery to this
 problem in order to optimize the friction that's required to match
 data. Assume we have data for the ball's height after 15 seconds. Let's
 first start by implementing the ODE:

diff --git a/docs/src/examples/hybrid_jump/hybrid_diffeq.md b/docs/src/examples/hybrid_jump/hybrid_diffeq.md
@@ -8,7 +8,7 @@ model and the universal differential equation is trained to uncover the missing
 dynamical equations.
 
 ```@example
-using DiffEqFlux, ComponentArrays, Random,
+using ComponentArrays, Random,
       Lux, OrdinaryDiffEq, Plots, Optimization, OptimizationOptimisers, DiffEqCallbacks
 
 u0 = Float32[2.0; 0.0]