Deprecate FastChain and sciml_train for v2.0

Project.toml

@@ -1,16 +1,14 @@
 name = "DiffEqFlux"
 uuid = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 authors = ["Chris Rackauckas <accounts@chrisrackauckas.com>"]
-version = "1.54.0"
+version = "2.0.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
-Cassette = "7057c7e9-c182-5462-911a-8362d720325c"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ConsoleProgressMonitor = "88cd18e8-d9cc-4ea6-8889-5259c0d15c8b"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
-DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
@@ -20,8 +18,6 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 LoggingExtras = "e6f89c97-d47a-5376-807f-9c37f3926c36"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
-NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationFlux = "253f991c-a7b2-45f8-8852-8b9a9df78a86"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
@@ -34,39 +30,29 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
-StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 TerminalLoggers = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [compat]
 Adapt = "3"
-Cassette = "0.3.7"
 ChainRulesCore = "1"
 ConsoleProgressMonitor = "0.1"
 DataInterpolations = "3.3"
 DiffEqBase = "6.41"
-DiffResults = "1.0"
 Distributions = "0.23, 0.24, 0.25"
 DistributionsAD = "0.6"
 Flux = "0.12, 0.13"
 ForwardDiff = "0.10"
 Functors = "0.4"
 LoggingExtras = "0.4, 1"
 Lux = "0.4"
-NNlib = "0.7, 0.8"
-Optim = "1"
 Optimization = "3"
-OptimizationFlux = "0.1"
-OptimizationOptimJL = "0.1"
-OptimizationPolyalgorithms = "0.1"
 ProgressLogging = "0.1"
 RecursiveArrayTools = "2"
 Reexport = "0.2, 1"
-Requires = "0.5, 1.0"
 SciMLBase = "1"
 SciMLSensitivity = "7"
-StaticArrays = "0.11, 0.12, 1"
 TerminalLoggers = "0.1"
 Zygote = "0.5, 0.6"
 ZygoteRules = "0.2"

docs/src/layers/CNFLayer.md

@@ -3,7 +3,6 @@
 The following layers are helper functions for easily building neural differential equation architectures specialized for the task of density estimation through Continuous Normalizing Flows (CNF).
 
 ```@docs
-DeterministicCNF
 FFJORD
 FFJORDDistribution
 ```

src/DiffEqFlux.jl

@@ -1,23 +1,14 @@
 module DiffEqFlux
 
 using Adapt, Base.Iterators, ConsoleProgressMonitor, DataInterpolations,
-    DiffEqBase, SciMLSensitivity, DiffResults, Distributions, DistributionsAD,
-    ForwardDiff, Optimization, OptimizationPolyalgorithms, LinearAlgebra,
+    DiffEqBase, SciMLSensitivity, Distributions, DistributionsAD,
+    ForwardDiff, LinearAlgebra,
     Logging, LoggingExtras, Printf, ProgressLogging, Random, RecursiveArrayTools,
-    Reexport, SciMLBase, StaticArrays, TerminalLoggers, Zygote, ZygoteRules
+    Reexport, SciMLBase, TerminalLoggers, Zygote, ZygoteRules
 
 @reexport using SciMLSensitivity
 @reexport using Zygote
-
-# deprecate
-
-import OptimizationFlux
-import NNlib
-import Lux
-using Requires
-using Cassette
 @reexport using Flux
-@reexport using OptimizationOptimJL
 using Functors
 
 import ChainRulesCore
@@ -43,25 +34,19 @@ ZygoteRules.@adjoint ZygoteRules.literal_getproperty(A::Tridiagonal, ::Val{:du})
 ZygoteRules.@adjoint Tridiagonal(dl, d, du) = Tridiagonal(dl, d, du), p̄ -> (diag(p̄[2:end, 1:end-1]), diag(p̄), diag(p̄[1:end-1, 2:end]))
 
 include("ffjord.jl")
-include("train.jl")
-include("fast_layers.jl")
 include("neural_de.jl")
-include("require.jl")
 include("spline_layer.jl")
 include("tensor_product_basis.jl")
 include("tensor_product_layer.jl")
 include("collocation.jl")
 include("hnn.jl")
 include("multiple_shooting.jl")
 
-Flux.device(::FastLayer) = @warn "device(f::FastLayer) is a no-op: to move FastChain computations to a GPU, apply gpu(x) to the weight vector"
-Flux.gpu(::FastLayer) = @warn "device(f::FastLayer) is a no-op: to move FastChain computations to a GPU, apply gpu(x) to the weight vector"
-Flux.cpu(::FastLayer) = @warn "device(f::FastLayer) is a no-op: to move FastChain computations to a CPU, apply cpu(x) to the weight vector"
-
-export DeterministicCNF, FFJORD, NeuralODE, NeuralDSDE, NeuralSDE, NeuralCDDE, NeuralDAE, NeuralODEMM, TensorLayer, AugmentedNDELayer, SplineLayer, NeuralHamiltonianDE
+export FFJORD, NeuralODE, NeuralDSDE, NeuralSDE, NeuralCDDE, NeuralDAE, 
+       NeuralODEMM, TensorLayer, AugmentedNDELayer, SplineLayer, NeuralHamiltonianDE
 export HamiltonianNN
 export ChebyshevBasis, SinBasis, CosBasis, FourierBasis, LegendreBasis, PolynomialBasis
-export FastDense, StaticDense, FastChain, initial_params
+export FastDense, StaticDense, initial_params
 export FFJORDDistribution
 export DimMover, FluxBatchOrder