revamp CI

.github/workflows/CI.yml

@@ -10,28 +10,34 @@ on:
       - main
     paths-ignore:
       - 'docs/**'
+  schedule:
+    - cron: '41 0 * * 5'
 jobs:
   test:
-    runs-on: ubuntu-latest
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        group:
+          - Core
+        version:
+          - '1'
+        os:
+          - ubuntu-latest
+          - macos-latest
+          - windows-latest
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v1
         with:
-          version: 1
-      - uses: actions/cache@v3
-        env:
-          cache-name: cache-artifacts
+          version: ${{ matrix.version }}
+      - uses: julia-actions/cache@v1
         with:
-          path: ~/.julia/artifacts
-          key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
-          restore-keys: |
-            ${{ runner.os }}-test-${{ env.cache-name }}-
-            ${{ runner.os }}-test-
-            ${{ runner.os }}-
+          token: ${{ secrets.GITHUB_TOKEN }}
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
-        env:
-          GROUP: ${{ matrix.group }}
+        with:
+          depwarn: error
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v3
         with:

.github/workflows/documentation.yml

@@ -6,15 +6,16 @@ on:
       - main
     tags: '*'
   pull_request:
-
+  schedule:
+    - cron: '41 0 * * 5'
 jobs:
   build:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@latest
         with:
-          version: '1.6'
+          version: '1'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy

Project.toml

@@ -7,25 +7,36 @@ version = "1.2.1"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
-ExprTools = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
+Aqua = "0.8"
 CUDA = "3, 5"
 DiffEqBase = "6"
 DocStringExtensions = "0.8, 0.9"
-ExprTools = "0.1"
 Flux = "0.13, 0.14"
 Functors = "0.2, 0.3, 0.4"
+LinearAlgebra = "1"
+Random = "1"
 Reexport = "1"
+SafeTestsets = "0.1"
+SparseArrays = "1"
 Statistics = "1"
+Test = "1"
 Zygote = "0.6"
-julia = "1.6,1.7"
+julia = "1.10"
+
+[extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Aqua", "Test", "SafeTestsets"]

src/DeepBSDE.jl

@@ -1,22 +1,22 @@
 """
 ```julia
-NNPDENS(u0,σᵀ∇u;opt=Flux.ADAM(0.1))
+NNPDENS(u0,σᵀ∇u;opt=Flux.Optimise.Adam(0.1))
 ```
-Uses a neural stochastic differential equation, which is then solved by the methods available in DifferentialEquations.jl. 
-The alg keyword is required for specifying the SDE solver algorithm that will be used on the internal SDE. All of the other 
+Uses a neural stochastic differential equation, which is then solved by the methods available in DifferentialEquations.jl.
+The alg keyword is required for specifying the SDE solver algorithm that will be used on the internal SDE. All of the other
 keyword arguments are passed to the SDE solver.
 ## Arguments
 - `u0`: a Flux.jl `Chain` for the initial condition guess.
 - `σᵀ∇u`: a Flux.jl `Chain` for the BSDE value guess.
-- `opt`: the optimization algorithm to be used to optimize the neural networks. Defaults to `ADAM`.
+- `opt`: the optimization algorithm to be used to optimize the neural networks. Defaults to `Flux.Optimise.Adam`.
 """
 struct NNPDENS{C1,C2,O} <: NeuralPDEAlgorithm
     u0::C1
     σᵀ∇u::C2
     opt::O
 end
 
-NNPDENS(u0,σᵀ∇u;opt=Flux.ADAM(0.1)) = NNPDENS(u0,σᵀ∇u,opt)
+NNPDENS(u0,σᵀ∇u;opt=Flux.Optimise.Adam(0.1)) = NNPDENS(u0,σᵀ∇u,opt)
 
 function DiffEqBase.solve(
     prob::TerminalPDEProblem,
@@ -165,7 +165,7 @@ function DiffEqBase.solve(
             l < 1e-6 && Flux.stop()
         end
         dataS = Iterators.repeated((), maxiters_upper)
-        Flux.train!(loss_, ps, dataS, ADAM(0.01); cb = cb)
+        Flux.train!(loss_, ps, dataS, Flux.Optimise.Adam(0.01); cb = cb)
         u_high = loss_()
         # Function to precalculate the f values over the domain
         function give_f_matrix(X,urange,σᵀ∇u,p,t)

src/DeepSplitting.jl

@@ -1,17 +1,18 @@
-Base.copy(t::Tuple) = t # required for below
-function Base.copy(opt::O) where  O<:Flux.Optimise.AbstractOptimiser
-    return O([copy(getfield(opt,f)) for f in fieldnames(typeof(opt))]...)
+_copy(t::Tuple) = t
+_copy(t) = t
+function _copy(opt::O) where  O<:Flux.Optimise.AbstractOptimiser
+    return O([_copy(getfield(opt,f)) for f in fieldnames(typeof(opt))]...)
 end
 
 """
-    DeepSplitting(nn, K=1, opt = ADAM(0.01), λs = nothing, mc_sample =  NoSampling())
+    DeepSplitting(nn, K=1, opt = Flux.Optimise.Adam(0.01), λs = nothing, mc_sample =  NoSampling())
 
 Deep splitting algorithm.
 
 # Arguments
 * `nn`: a [Flux.Chain](https://fluxml.ai/Flux.jl/stable/models/layers/#Flux.Chain), or more generally a [functor](https://github.com/FluxML/Functors.jl).
 * `K`: the number of Monte Carlo integrations.
-* `opt`: optimizer to be used. By default, `Flux.ADAM(0.01)`.
+* `opt`: optimizer to be used. By default, `Flux.Optimise.Adam(0.01)`.
 * `λs`: the learning rates, used sequentially. Defaults to a single value taken from `opt`.
 * `mc_sample::MCSampling` : sampling method for Monte Carlo integrations of the non-local term. Can be `UniformSampling(a,b)`, `NormalSampling(σ_sampling, shifted)`, or `NoSampling` (by default).
 
@@ -25,7 +26,7 @@ nn = Flux.Chain(Dense(d, hls, tanh),
                 Dense(hls,hls,tanh),
                 Dense(hls, 1, x->x^2))
 
-alg = DeepSplitting(nn, K=10, opt = ADAM(), λs = [5e-3,1e-3],
+alg = DeepSplitting(nn, K=10, opt = Flux.Optimise.Adam(), λs = [5e-3,1e-3],
                     mc_sample = UniformSampling(zeros(d), ones(d)) )
 ```
 """
@@ -39,7 +40,7 @@ end
 
 function DeepSplitting(nn;
                         K=1,
-                        opt::O = ADAM(0.01),
+                        opt::O = Flux.Optimise.Adam(0.01),
                         λs::L = nothing,
                         mc_sample::MCSampling = NoSampling()) where {O <: Flux.Optimise.AbstractOptimiser, L <: Union{Nothing,Vector{N}} where N <: Number}
     isnothing(λs) ? λs = [opt.eta] : nothing
@@ -175,7 +176,7 @@ function solve(
         _maxiters = length(maxiters) > 1 ? maxiters[min(net,2)] : maxiters[]
 
         for λ in λs
-            opt_net = copy(opt) # starting with a new optimiser state at each time step
+            opt_net = _copy(opt) # starting with a new optimiser state at each time step
             opt_net.eta = λ
             verbose && println("Training started with ", typeof(opt_net), " and λ :", opt_net.eta)
             for epoch in 1:_maxiters